Skip to main content Accessibility help
×
Hostname: page-component-594f858ff7-7tp2g Total loading time: 0 Render date: 2023-06-10T07:08:19.335Z Has data issue: false Feature Flags: { "corePageComponentGetUserInfoFromSharedSession": true, "coreDisableEcommerce": false, "corePageComponentUseShareaholicInsteadOfAddThis": true, "coreDisableSocialShare": false, "useRatesEcommerce": true } hasContentIssue false

References

Published online by Cambridge University Press:  05 June 2012

Terence C. Mills
Affiliation:
Loughborough University
Raphael N. Markellos
Affiliation:
Norwich Business School, University of East Anglia
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2008

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abadir, K. M., and Taylor, A. M. R. (1999), ‘On the Definitions of (Co-)Integration’, Journal of Time Series Analysis, 20, 129–37.CrossRefGoogle Scholar
Agiakloglou, C., and Newbold, P. (1994), ‘Lagrange Multiplier Tests for Fractional Difference’, Journal of Time Series Analysis, 15, 253–62.CrossRefGoogle Scholar
Aït-Sahalia, Y. (1996a), ‘Nonparametric Pricing of Interest Rate Derivative Securities’, Econometrica, 64, 527–60.CrossRefGoogle Scholar
Aït-Sahalia, Y.(1996b), ‘Testing Continuous-time Models of the Spot Interest Rate’, Review of Financial Studies, 9, 385–426.CrossRefGoogle Scholar
Aït-Sahalia, Y.(1999), ‘Transition Densities for Interest Rates and Other Nonlinear Diffusions’, Journal of Finance, 54, 499–547.CrossRefGoogle Scholar
Akaike, H. (1974), ‘A New Look at the Statistical Model Identification’, IEEE Transactions on Automatic Control, AC–19, 716–23.CrossRefGoogle Scholar
Al-Falou, A. A., and Trummer, D. (2003), ‘Identifiability of Recurrent Neural Networks’, Econometric Theory, 19, 812–28.CrossRefGoogle Scholar
Andersen, T. G., Bollerslev, T., and Diebold, F. X. (2007), ‘Parametric and Nonparametric Volatility Measurement’, in Aït-Sahalia, Y. and Hansen, L. P. (eds.), Handbook of Financial Econometrics, New York: Elsevier.Google Scholar
Andersen, T. G., Bollerslev, T., Diebold, F. X., and Labys, P. (2003), ‘Modeling and Forecasting Realized Volatility’, Econometrica, 71, 529–626.CrossRefGoogle Scholar
Anderson, H. M., Nam, K., and Vahid, F. (1999), ‘Asymmetric nonlinear smooth transition GARCH models’, in Rothman, P. (ed.), Nonlinear Time Series Analysis of Economic and Financial Data, Boston: Kluwer, 191–207.CrossRefGoogle Scholar
Anderson, H. M., and Vahid, F. (1998), ‘Testing Multiple Equation Systems for Common Nonlinear Components’, Journal of Econometrics, 84, 1–36.CrossRefGoogle Scholar
Andersson, M. K., Eklund, B., and Lyhagen, J. (1999), ‘A Simple Linear Time Series Model with Misleading Nonlinear Properties’, Economics Letters, 65, 281–4.CrossRefGoogle Scholar
Andreou, E., and Ghysels, E., (2002), ‘Detecting Multiple Breaks in Financial Market Volatility Dynamics’, Journal of Applied Econometrics, 17, 579–600.CrossRefGoogle Scholar
Andreou, E., Pittis, N., and Spanos, A. (2001), ‘On Modelling Speculative Prices: The Empirical Literature’, Journal of Economic Surveys, 15, 187–220.CrossRefGoogle Scholar
Andrews, D. W. K. (1991), ‘Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation’, Econometrica, 59, 817–58.CrossRefGoogle Scholar
Andrews, D. W. K., and Sun, Y. (2004), ‘Adaptive Local Polynomial Whittle Estimation of Long-range Dependence’, Econometrica, 72, 569–614.CrossRefGoogle Scholar
Audrino, F., and Bühlmann, P. (2001), ‘Tree-structured Generalized Autoregressive Conditional Heteroscedastic Models’, Journal of the Royal Statistical Society, Series B, 63, 727–44.CrossRefGoogle Scholar
Bachelier, L. (1900), ‘Théorie de la Spéculation’, Annales de l'Ecole Normale Superieure, Series 3, 17, 21–86.CrossRefGoogle Scholar
Backus, D. K., and Gregory, A. W. (1993), ‘Theoretical Relations between Risk Premiums and Conditional Variances’, Journal of Business and Economic Statistics, 11, 177–85.Google Scholar
Badrinath, S. G., and Chatterjee, S. (1988), ‘On Measuring Skewness and Elongation in Common Stock Distributions: The Case of the Market Index’, Journal of Business, 61, 451–72.CrossRefGoogle Scholar
Badrinath, S. G., and Chatterjee, S.(1991), ‘A Data-analytic Look at Skewness and Elongation in Common Stock Return Distributions’, Journal of Business and Economic Statistics, 9, 223–33.Google Scholar
Bai, J. (1997), ‘Estimating Multiple Breaks One at a Time’, Econometric Theory, 13, 315–52.CrossRefGoogle Scholar
Bai, J., and Perron, P. (1998), ‘Estimating and Testing Linear Models with Multiple Structural Changes’, Econometrica, 66, 47–78.CrossRefGoogle Scholar
Baillie, R. T. (1996), ‘Long Memory Processes and Fractional Integration in Econometrics’, Journal of Econometrics, 73, 5–59.CrossRefGoogle Scholar
Baillie, R. T., and Bollerslev, T. (1989), ‘The Message in Daily Exchange Rates: A Conditional Variance Tale’, Journal of Business and Economic Statistics, 7, 297–305.Google Scholar
Baillie, R. T., and Bollerslev, T.(1992), ‘Prediction in Dynamic Models with Time-dependent Conditional Variances’, Journal of Econometrics, 52, 91–113.CrossRefGoogle Scholar
Baillie, R. T., Bollerslev, T., and Mikkelson, H. O. (1996), ‘Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity’, Journal of Econometrics, 74, 3–30.CrossRefGoogle Scholar
Baillie, R. T., and Kapetanios, G. (2007), ‘Testing for Neglected Nonlinearity in Long Memory Models’, Journal of Business and Economic Statistics, 25, 447–61.CrossRefGoogle Scholar
Bali, T. G., and Weinbaum, D. (2005), ‘The Empirical Performance of Alternative Extreme-value Volatility Estimators’, Journal of Futures Markets, 25, 873–92.CrossRefGoogle Scholar
Bali, T. G., and Wu, L. (2006), ‘A Comprehensive Analysis of the Short-term Interest Rate Dynamics’, Journal of Banking and Finance, 30, 1269–90.CrossRefGoogle Scholar
Balke, N. S., and Fomby, T. B. (1997), ‘Threshold Cointegration’, International Economic Review, 38, 627–45.CrossRefGoogle Scholar
Banerjee, A., Dolado, J. J., Galbraith, J. W., and Hendry, D. F. (1993), Co-integration, Error Correction and the Econometric Analysis of Non-Stationary Data, Oxford: Oxford University Press.CrossRefGoogle Scholar
Banerjee, A., Dolado, J. J., and Mestre, R. (1998), ‘Error-correction Mechanism Tests for Cointegration in a System Equation Framework’, Journal of Time Series Analysis, 19, 267–83.CrossRefGoogle Scholar
Barndorff-Nielsen, O. E., Graversen, S. E., and Shephard, N. (2004), ‘Power Variation and Stochastic Volatility: a Review and some New Results’, Journal of Applied Probability, 41, 133–43.CrossRefGoogle Scholar
Barndorff-Nielsen, O. E., and Shephard, N. (2004), ‘Power and Bipower Variation with Stochastic Volatility and Jumps’, Journal of Financial Econometrics, 2, 1–37.CrossRefGoogle Scholar
Barndorff-Nielsen, O. E., and Shephard, N.(2005), ‘How Accurate is the Asymptotic Approximation to the Distribution of Realized Volatility?’, in Andrews, D., Powell, J., Ruud, P. and Stock, J. (eds.), Identification and Inference for Econometric Models: A Festschrift for Tom Rothenberg, Cambridge: Cambridge University Press, 306–31.CrossRefGoogle Scholar
Barnett, W. A., Gallant, A. R., Hinich, M. J., Jungeilges, J. A., Kaplan, D. T., and Jensen, M. J. (1996), ‘An Experimental Design to Compare Tests of Nonlinearity and Chaos’, in Barnett, W. A., Kirman, A. P. and Salmon, M. (eds.), Nonlinear Dynamics and Economics, Cambridge: Cambridge University Press, 163–90.Google Scholar
Barnett, W. A., Gallant, A. R., Hinich, M. J., Jungeilges, J. A., Kaplan, D. T., and Jensen, M. J.(1997), ‘A Single-blind Controlled Competition among Tests of Nonlinearity and Chaos’, Journal of Econometrics, 82, 157–92.CrossRefGoogle Scholar
Basu, P., and Dua, P. (1996), ‘The Behaviour of Velocity and Nominal Interest Rates in a Cash-in-advance Model’, Journal of Macroeconomics, 18, 463–78.CrossRefGoogle Scholar
Bates, D. S. (2003) ‘Empirical Option Pricing: A Retrospection’, Journal of Econometrics, 116, 387–404.CrossRefGoogle Scholar
Bauwens, L., and Giot, P. (2003), ‘Asymmetric ACD Models: Introducing Price Information in ACD Models with a Two State Transition Model’, Empirical Economics, 28, 709–31.CrossRefGoogle Scholar
Bauwens, L., Laurent, S., and Rombouts, J. V. K. (2006), ‘Multivariate GARCH Models: A Survey’, Journal of Applied Econometrics, 21, 79–109.CrossRefGoogle Scholar
Baxter, M., and King, R. G. (1999), ‘Measuring Business Cycles: Approximate Band-pass Filters for Economic Time Series’, Review of Economics and Statistics, 81, 575–93.CrossRefGoogle Scholar
Bera, A. K., and Higgins, M. L. (1993), ‘On ARCH Models: Properties, Estimation and Testing’, Journal of Economic Surveys, 7, 305–66.CrossRefGoogle Scholar
Beran, J. A. (1992), ‘Statistical Methods for Data with Long-range Dependence’, Statistical Science, 7, 404–27.CrossRefGoogle Scholar
Beran, J. A.(1995), ‘Maximum Likelihood Estimation of the Differencing Parameter for Invertible Short and Long-memory Autoregressive Integrated Moving Average Models’, Journal of the Royal Statistical Society, Series B, 57, 659–72.Google Scholar
Berliner, L. M. (1992), ‘Statistics, Probability and Chaos’, Statistical Science, 7, 69–90.CrossRefGoogle Scholar
Bernanke, B. (1986), ‘Alternative Explanations of the Money–Income Correlation’, Carnegie-Rochester Conference Series on Public Policy, 25, 49–100.CrossRefGoogle Scholar
Berndt, E. R. (1991), The Practice of Econometrics: Classic and Contemporary, Reading, MA: Addison-Wesley.Google Scholar
Berndt, E. R., Hall, B. H., Hall, R. E., and Hausman, J. A. (1974), ‘Estimation and Inference in Nonlinear Structural Models’, Annals of Economic and Social Measurement, 4, 653–65.Google Scholar
Beveridge, S., and Nelson, C. R. (1981), ‘A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention to Measurement of the “Business Cycle” ’, Journal of Monetary Economics, 7, 151–74.CrossRefGoogle Scholar
Bhardwaj, G., and Swanson, N. R. (2006), ‘An Empirical Investigation of the Usefulness of ARFIMA Models for Predicting Macroeconomic and Financial Time Series’, Journal of Econometrics, 131, 539–78.CrossRefGoogle Scholar
Bhargava, A. (1986), ‘On the Theory of Testing for Unit Roots in Observed Time Series’, Review of Economic Studies, 53, 369–84.CrossRefGoogle Scholar
Bierens, H. J. (1997), ‘Nonparametric Cointegration Analysis’, Journal of Econometrics, 77, 379–404.CrossRefGoogle Scholar
Bierans, H. J.(2000), ‘Nonparametric Nonlinear Co-trending Analysis, with an Application to Inflation and Interest Rates in the US’, Journal of Business and Economic Statistics, 18, 323–37.Google Scholar
Blake, A. P., and Kapetanios, G. (2007), ‘Testing for ARCH in the Presence of Nonlinearity of Unknown Form in the Conditional Mean’, Journal of Econometrics, 137, 472–88.CrossRefGoogle Scholar
Blanchard, O. J. (1989), ‘A Traditional Interpretation of Macroeconomic Fluctuations’, American Economic Review, 79, 1146–64.Google Scholar
Blanchard, O. J., and Quah, D. (1989), ‘Dynamic Effects of Aggregate Demand and Aggregate Supply Disturbances’, American Economic Review, 79, 655–73.Google Scholar
Blanchard, O. J., and Watson, M. W. (1982), ‘Bubbles, Rational Expectations, and Financial Markets’, in Wachtel, P. (ed.), Crises in the Economic and Financial Structure, Lexington, MA: Lexington Books, 295–315.Google Scholar
Bollerslev, T. (1986), ‘Generalised Autoregressive Conditional Heteroskedasticity’, Journal of Econometrics, 31, 307–27.CrossRefGoogle Scholar
Bollerslev, T.(1987), ‘A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return’, Review of Economics and Statistics, 69, 542–6.CrossRefGoogle Scholar
Bollerslev, T.(1988), ‘On the Correlation Structure for the Generalised Autoregressive Conditional Heteroskedastic Process’, Journal of Time Series Analysis, 9, 121–32.CrossRefGoogle Scholar
Bollerslev, T.(1990), ‘Modelling the Coherence in Short-run Nominal Exchange Rates: a Multivariate Generalized ARCH Model’, Review of Economics and Statistics, 72, 498–505.CrossRefGoogle Scholar
Bollerslev, T., Chou, R. Y., and Kroner, K. F. (1992), ‘ARCH Modelling in Finance: A Review of the Theory and Empirical Evidence’, Journal of Econometrics, 52, 5–59.CrossRefGoogle Scholar
Bollerslev, T., and Engle, R. F. (1993), ‘Common Persistence in Conditional Variances’, Econometrica, 61, 166–87.CrossRefGoogle Scholar
Bollerslev, T., Engle, R. F., and Nelson, D. B. (1994), ‘ARCH Models’, in Engle, R. F. and McFadden, D. L. (eds.), Handbook of Econometrics, Vol. IV, New York: North-Holland, 2959–3038.Google Scholar
Bollerslev, T., Engle, R. F., and Wooldridge, J. M. (1988), ‘A Capital Asset Pricing Model with Time-varying Covariances’, Journal of Political Economy, 96, 116–31.CrossRefGoogle Scholar
Bollerslev, T., and Wooldridge, J. M. (1992), ‘Quasi Maximum Likelihood Estimation and Inference in Dynamic Models with Time Varying Covariances’, Econometric Reviews, 11, 143–72.CrossRefGoogle Scholar
Bollerslev, T., and Zhou, H. (2002), ‘Estimating Stochastic Volatility Diffusion Using Conditional Moments of Integrated Volatility’, Journal of Econometrics, 109, 33–65CrossRefGoogle Scholar
Bollerslev, T., and Zhou, H.(2006), ‘Volatility Puzzles: A Simple Framework for Gauging Return-volatility Regressions’, Journal of Econometrics, 127, 123–50.CrossRefGoogle Scholar
Boothe, P., and Glassman, D. (1987), ‘The Statistical Distribution of Exchange Rates: Empirical Evidence and Economic Implications’, Journal of International Economics, 22, 297–319.CrossRefGoogle Scholar
Boudoukh, J., Richardson, M., and Whitelaw, R. F. (1997), ‘Investigation of a Class of Volatility Estimators’, Journal of Derivatives, 4, 63–71.CrossRefGoogle Scholar
Bougerol, P., and Picard, N. (1992), ‘Stationarity of GARCH Processes and of some Nonnegative Time Series’, Journal of Econometrics, 52, 115–28.CrossRefGoogle Scholar
Box, G. E. P., and Cox, D. R. (1964), ‘An Analysis of Transformations’, Journal of the Royal Statistical Society, Series B, 26, 211–43.Google Scholar
Box, G. E. P., and Jenkins, G. M. (1976), Time Series Analysis: Forecasting and Control, rev. edn., San Francisco: Holden Day.Google Scholar
Box, G. E. P., and Pierce, D. A. (1970), ‘Distribution of Residual Autocorrelations in Autoregressive Moving Average Time Series Models’, Journal of the American Statistical Association, 65, 1509–26.CrossRefGoogle Scholar
Breidt, F. J., Crato, N., and Lima, P. J. F. (1998), ‘The Detection and Estimation of Long Memory in Stochastic Volatility’, Journal of Econometrics, 83, 325–48.CrossRefGoogle Scholar
Breitung, J. (2001), ‘Rank Tests for Unit Roots and Cointegration’, Journal of Business and Economic Statistics, 19, 331–40.CrossRefGoogle Scholar
Breitung, J.(2002), ‘Nonparametric Tests for Unit Roots and Cointegration’, Journal of Econometrics, 108, 343–64.CrossRefGoogle Scholar
Brock, W. A. (1986), ‘Distinguishing Random and Deterministic Systems: Abridged Version’, Journal of Economic Theory, 40, 168–95.CrossRefGoogle Scholar
Brock, W. A.(1988), ‘Nonlinearity and Complex Dynamics in Economics and Finance’, in Anderson, P., Arrow, K. J. and Pines, D. (eds.), The Economy as an Evolving Complex System, Reading, MA: Addison-Wesley, 77–97.Google Scholar
Brock, W. A., and Dechert, W. D. (1991), ‘Non-linear Dynamical Systems: Instability and Chaos in Economics’, in Hildenbrand, W. and Sonnenschein, H. (eds.), Handbook of Mathematical Economics, Amsterdam: North-Holland, 2209–35.Google Scholar
Brock, W. A., Hsieh, D., and LeBaron, B. (1991), A Test for Nonlinear Dynamics, Chaos and Instability, Cambridge, MA: MIT Press.Google Scholar
Brock, W. A., Lakonishok, J., and LeBaron, B. (1992), ‘Simple Technical Trading Rules and the Stochastic Properties of Stock Returns’, Journal of Finance, 47, 1731–64.CrossRefGoogle Scholar
Brockwell, P. J., and Davis, R. A. (1996), Time Series: Theory and Methods, 2nd edn., New York: Springer-Verlag.Google Scholar
Brooks, C. (2006), ‘Multivariate Volatility Models’, in Mills, T. C. and Patterson, K. (eds.), Palgrave Handbook of Econometrics, Vol. I: Econometric Theory, Basingstoke: Palgrave Macmillan, 765–83.Google Scholar
Brooks, C., and Burke, S. P. (2002), ‘Selecting from amongst Non-nested Conditional Variance Models: Information Criteria and Portfolio Determination’, Manchester School, 70, 747–67.CrossRefGoogle Scholar
Brooks, C., and Burke, S. P.(2003) ‘Information Criteria for GARCH Model Selection’, European Journal of Finance, 9, 557–80.CrossRefGoogle Scholar
Brooks, C., Burke, S. P., Heravi, S., and Persand, G. (2005), ‘Autoregressive Conditional Kurtosis’, Journal of Financial Econometrics, 3, 399–421.CrossRefGoogle Scholar
Brooks, C., Burke, S. P., and Persand, G. (2001) ‘Benchmarks and the Accuracy of GARCH Model’, International Journal of Forecasting, 17, 45–56.CrossRefGoogle Scholar
Brooks, C., and Chong, J. (2001), ‘The Cross-country Hedging Performance of Implied versus Statistical Forecasting Models’, Journal of Futures Markets, 21, 1043–69.CrossRefGoogle Scholar
Brooks, C., Persand, G., and Burke, S. (2003), ‘Multivariate GARCH Models: Software Choice and Estimation Issues’, Journal of Applied Econometrics, 18, 725–34.CrossRefGoogle Scholar
Broto, C., and Ruiz, E. (2004), ‘Estimation Methods for Stochastic Volatility Models: A Survey’, Journal of Economic Surveys, 18, 613–49.CrossRefGoogle Scholar
Brown, R. L., Durbin, J., and Evans, J. M. (1975), ‘Techniques for Testing the Constancy of Regression Relationships over Time’, Journal of the Royal Statistical Society, Series B, 39, 107–13.Google Scholar
Brown, S. J., Goetzmann, W. N., and Kumar, A. (1998), ‘The Dow Theory: William Peter Hamilton's Track Record Reconsidered’, Journal of Finance, 53, 1311–33.CrossRefGoogle Scholar
Busetti, F., and Harvey, A. C. (2001), ‘Testing for the Presence of a Random Walk in Series with Structural Breaks’, Journal of Time Series Analysis, 22, 127–50.CrossRefGoogle Scholar
Busetti, F., and Harvey, A. C.(2003), ‘Further Comments on Stationarity Tests in Series with Structural Breaks at Unknown Points’, Journal of Time Series Analysis, 24, 137–40.CrossRefGoogle Scholar
Cai, J. (1994), ‘A Markov Model of Switching-regime ARCH’, Journal of Business and Economic Statistics, 12, 309–16.Google Scholar
Campbell, J. Y. (1991), ‘A Variance Decomposition for Stock Returns’, Economic Journal, 101, 157–79.CrossRefGoogle Scholar
Campbell, J. Y., Lo, A. W., and MacKinlay, A. C. (1997), The Econometrics of Financial Markets, Princeton, NJ: Princeton University Press.Google Scholar
Campbell, J. Y., and Mankiw, N. G. (1987), ‘Permanent and Transitory Components in Macroeconomic Fluctuations’, American Economic Review, Papers and Proceedings, 77, 111–17.Google Scholar
Campbell, J. Y., and Perron, P. (1991), ‘Pitfalls and Opportunities: What Macroeconomists Should Know about Unit Roots’, NBER Macroeconomics Annual, Cambridge, MA: MIT Press, 141–201.Google Scholar
Campbell, J. Y., and Shiller, R. J. (1987), ‘Cointegration and Tests of Present Value Models’, Journal of Political Economy, 95, 1062–88.CrossRefGoogle Scholar
Campbell, J. Y., and Shiller, R. J.(1988a), ‘Interpreting Cointegrated Models’, Journal of Economic Dynamics and Control, 12, 503–22.CrossRefGoogle Scholar
Campbell, J. Y., and Shiller, R. J.(1988b), ‘Stock Prices, Earnings, and Expected Dividends’, Journal of Finance, 43, 661–76.CrossRefGoogle Scholar
Campbell, J. Y., and Shiller, R. J.(1988c), ‘The Dividend–Price Ratio and Expectations of Future Dividends and Discount Factors’, Review of Financial Studies, 1, 195–228.CrossRefGoogle Scholar
Campos, J., Ericsson, N. R., and Hendry, D. F. (1996), ‘Cointegration Tests in the Presence of Structural Breaks’, Journal of Econometrics, 70, 187–220.CrossRefGoogle Scholar
Caner, M. (1998), ‘Tests for Cointegration with Infinite Variance Errors’, Journal of Econometrics, 86, 155–75.CrossRefGoogle Scholar
Chan, K. C., Karolyi, A., Longstaff, F., and Sanders, A. (1992), ‘An Empirical Comparison of Alternative Models of the Short Term Interest Rate’, Journal of Finance, 47, 1209–27.CrossRefGoogle Scholar
Chan, L. K. C., and Lakonishok, J. (1992), ‘Robust Measurement of Beta Risk’, Journal of Financial and Quantitative Analysis, 27, 265–82.CrossRefGoogle Scholar
Charemza, W. W., Lifshits, M., and Makarova, S. (2005), ‘Conditional Testing for Unit-root Bilinearity in Financial Time Series: Some Theoretical and Empirical Results,’ Journal of Economic Dynamics and Control, 29, 63–96.CrossRefGoogle Scholar
Chen, Y.-T., Chou, R.-Y., and Kuan, C.-M. (2000), ‘Testing Time Reversibility without Moment Restrictions’, Journal of Econometrics, 95, 199–218.CrossRefGoogle Scholar
Cheng, B., and Titterington, D. M. (1994), ‘Neural Networks: A Review from a Statistical Perspective’, Statistical Science, 9, 2–54.CrossRefGoogle Scholar
Chib, S., Nardarib, F., and Shephard, N. (2006), ‘Analysis of High Dimensional Multivariate Stochastic Volatility Models’, Journal of Econometrics, 134, 341–71.CrossRefGoogle Scholar
Choi, I., and Saikkonen, P. (2004), ‘Testing Linearity in Cointegrating Smooth Transition Regressions’, Econometrics Journal, 7, 341–65.CrossRefGoogle Scholar
Chong, T. T. L. (2000), ‘Estimating the Differencing Parameter via the Partial Autocorrelation Function’, Journal of Econometrics, 97, 365–81.CrossRefGoogle Scholar
Chow, G. C. (1960), ‘Tests of Equality between Sets of Coefficients in Two Linear Regressions’, Econometrica, 28, 591–605.CrossRefGoogle Scholar
Chow, K. V., and Denning, K. C. (1993), ‘A Simple Multiple Variance Ratio Test’, Journal of Econometrics, 58, 385–401.CrossRefGoogle Scholar
Christiano, L. J., and Fitzgerald, T. J. (2003), ‘The Band Pass Filter’, International Economic Review, 44, 435–65.CrossRefGoogle Scholar
Christoffersen, P., and Jacobs, K. (2004), ‘Which GARCH Model for Option Valuation?’, Management Science, 50, 1204–21.CrossRefGoogle Scholar
Chu, C. -S. J., Hornik, K., and Kuan, C.-M. (1995), ‘The Moving-estimates Test for Parameter Stability’, Econometric Theory, 11, 699–720.CrossRefGoogle Scholar
Cioczek-Georges, R., and Taqqu, M. S. (1995), ‘Form of the Conditional Variance for Symmetric Stable Random Variables’, Statistica Sinica, 5, 351–61.Google Scholar
Clark, P. K. (1973), ‘A Subordinated Stochastic Process Model with Finite Variances for Speculative Prices’, Econometrica, 41, 135–55.CrossRefGoogle Scholar
Cochrane, J. H. (1988), ‘How Big is the Random Walk in GNP?’, Journal of Political Economy, 96, 893–920.CrossRefGoogle Scholar
Cochrane, J. H.(1991), ‘A Critique of the Application of Unit Root Tests’, Journal of Economic Dynamics and Control, 15, 275–84.CrossRefGoogle Scholar
Cooley, T. F., and LeRoy, S. F. (1985), ‘Atheoretical Macroeconometrics: A Critique’, Journal of Monetary Economics, 16, 283–308.CrossRefGoogle Scholar
Cootner, P. A. (ed.) (1964), The Random Character of Stock Market Prices, Cambridge, MA: MIT Press.Google Scholar
Corradi, V., Swanson, N. R., and White, H. (2000), ‘Testing for Stationarity–Ergodicity and for Comovements between Nonlinear Discrete Time Markov Processes’, Journal of Econometrics, 96, 39–73.CrossRefGoogle Scholar
Coutts, J. A., Mills, T. C., and Roberts, J. (1994), ‘Misspecification of the Market Model: The Implications for Event Studies’, Applied Economic Letters, 2, 143–5.Google Scholar
Coutts, J. A., Roberts, J., and Mills, T. C. (1997), ‘Parameter Stability in the Market Model: Tests and Time Varying Parameter Estimation with UK Data’, The Statistician, 46, 57–70.Google Scholar
Cowles, A. (1933), ‘Can Stock Market Forecasters Forecast?’, Econometrica, 1, 309–24.CrossRefGoogle Scholar
Cowles, A.(1944), ‘Stock Market Forecasting’, Econometrica, 12, 206–14.CrossRefGoogle Scholar
Cowles, A.(1960), ‘A Revision of Previous Conclusions regarding Stock Price Behaviour’, Econometrica, 28, 909–15.CrossRefGoogle Scholar
Cowles, A., and Jones, H. E. (1937), ‘Some A Posteriori Probabilities in Stock Market Action’, Econometrica, 5, 280–94.CrossRefGoogle Scholar
Cramer, H. (1961), ‘On Some Classes of Non-stationary Processes’, Proceedings of the 4th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley: University of California Press, 57–78.Google Scholar
Creedy, J., Lye, J., and Martin, V. L. (1996), ‘A Non-linear Model of the Real US/UK Exchange Rate’, Journal of Applied Econometrics, 11, 669–86.3.0.CO;2-5>CrossRefGoogle Scholar
Cuddington, J. T., and Winters, L. A. (1987), ‘The Beveridge–Nelson Decomposition of Economic Time Series: A Quick Computational Method’, Journal of Monetary Economics, 19, 125–7.CrossRefGoogle Scholar
Cuthbertson, K. (1996), Quantitative Financial Economics: Stocks, Bonds, and Foreign Exchange, New York: Wiley.Google Scholar
Dacarogna, M. M., Müller, U. A., Nagler, R. J., Olsen, R. B., and Pictet, O. V. (1993), ‘A Geographical Model for the Daily and Weekly Seasonal Volatility in the Foreign Exchange Market’, Journal of International Money and Finance, 12, 413–38.CrossRefGoogle Scholar
Davidson, J. (2006a), Time Series Modelling, Version 4.18, Exeter: University of Exeter.Google Scholar
Davidson, J.(2006b), ‘Asymptotic Methods and Functional Central Limit Theorems’, in Mills, T. C. and Patterson, K. (eds.), Palgrave Handbook of Econometrics, Vol. I: Econometric Theory, Basingstoke: Palgrave Macmillan, 159–211.Google Scholar
Gooijer, J. G. (1989), ‘Testing Non-linearities in World Stock Market Prices’, Economics Letters, 31, 31–5.CrossRefGoogle Scholar
De Haan, L., Jansen, D. W., Koedijk, K., and de Vries, C. G. (1994), ‘Safety First Portfolio Selection, Extreme Value Theory and Long Run Asset Risks’, in Galambos, J., Lechner, J. A., Simiu, E. and Hagwood, C. (eds.), Extreme Value Theory and Applications, Boston: Kluwer Academic, 471–87.CrossRefGoogle Scholar
Haan, L., and Resnick, S. I. (1980), ‘A Simple Asymptotic Estimate for the Index of a Stable Distribution’, Journal of the Royal Statistical Society, Series B, 42, 83–7.Google Scholar
Haan, L., Resnick, S. I., Rootzén, H., and Vries, C. G. (1989), ‘Extremal Behaviour of Solutions to a Stochastic Difference Equation with Applications to ARCH Processes’, Stochastic Processes and their Applications, 32, 213–24.CrossRefGoogle Scholar
Lima, P. J. F. (1997), ‘On the Robustness of Nonlinearity Tests to Moment Condition Failure’, Journal of Econometrics, 76, 251–80.CrossRefGoogle Scholar
Dechert, W. D. (1996), ‘Testing Time Series for Nonlinearities: The BDS Approach’, in Barnett, W. A., Kirman, A. P. and Salmon, M. (eds.), Nonlinear Dynamics and Economics, Cambridge: Cambridge University Press, 191–200.Google Scholar
DeJong, D. N., Nankervis, J. C., Savin, N. E., and Whiteman, C. H. (1992), ‘The Power Problems of Unit Root Tests in Time Series with Autoregressive Errors’, Journal of Econometrics, 53, 323–43.CrossRefGoogle Scholar
DeJong, D. N., and Whiteman, C. H. (1991a), ‘The Temporal Stability of Dividends and Stock Prices: Evidence from the Likelihood Function’, American Economic Review, 81, 600–17.Google Scholar
DeJong, D. N., and Whiteman, C. H.(1991b), ‘Trends and Random Walks in Macroeconomic Time Series: A Reconsideration Based on the Likelihood Principle’, Journal of Monetary Economics, 28, 221–54.CrossRefGoogle Scholar
Dekkers, A. L. M., and Haan, L., (1989), ‘On the Estimation of the Extreme-value Index and Large Quantile Estimation’, Annals of Statistics, 17, 1795–832.CrossRefGoogle Scholar
Delgado, M. A., and Velasco, C. (2005), ‘Sign Tests for Long-memory Time Series’, Journal of Econometrics, 128, 215–51.CrossRefGoogle Scholar
Demos, A., and Sentana, E. (1998), ‘Testing for GARCH Effects: A One-sided Approach’, Journal of Econometrics, 86, 97–127.CrossRefGoogle Scholar
Deo, R. S., and Richardson, M. (2003), ‘On the Asymptotic Power of the Variance Ratio Test’, Econometric Theory, 19, 231–9.CrossRefGoogle Scholar
Diba, B. T. (1990), ‘Bubbles and Stock-price Volatility’, in Dwyer, G. P. and Hafer, R. W. (eds.), The Stock Market: Bubbles, Volatility, and Chaos, Boston: Kluwer Academic, 9–26.CrossRefGoogle Scholar
Diba, B. T., and Grossman, H. I. (1987), ‘On the Inception of Rational Bubbles’, Quarterly Journal of Economics, 103, 697–700.CrossRefGoogle Scholar
Diba, B. T., and Grossman, H. I.(1988), ‘Explosive Rational Bubbles in Stock Prices?’, American Economic Review, 81, 600–17.Google Scholar
Dickey, D. A., and Fuller, W. A. (1979), ‘Distribution of the Estimators for Autoregressive Time Series with a Unit Root’, Journal of the American Statistical Association, 74, 427–31.Google Scholar
Dickey, D. A., and Fuller, W. A.(1981), ‘Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root’, Econometrica, 49, 1057–72.CrossRefGoogle Scholar
Dickey, D. A., and Pantula, S. (1987), ‘Determining the Order of Differencing in Autoregressive Processes’, Journal of Business and Economic Statistics, 5, 455–61.Google Scholar
Diebold, F. X., Hickman, A., Inoue, A., and Schuermann, T. (1998), ‘Scale Models’, Risk, 11, 104–7.Google Scholar
Diebold, F. X., and Inoue, A. (2001), ‘Long Memory and Regime Switching’, Journal of Econometrics, 105, 131–59.CrossRefGoogle Scholar
Diebold, F. X., and Lopez, J. (1996), ‘Forecast Evaluation and Combination’, in Maddala, G. S. and Rao, C. R. (eds.), Handbook of Statistics, Amsterdam: North-Holland, 241–68.Google Scholar
Diebold, F. X., and Nerlove, M. (1990), ‘Unit Roots in Economic Time Series: A Selective Survey’, in Rhodes, G. F. and Fomby, T. B. (eds.), Advances in Econometrics, Vol. VIII, Greenwich, CT: JAI Press, 3–69.Google Scholar
Diebold, F. X., and Rudebusch, G. D. (1991), ‘On the Power of Dickey–Fuller Tests against Fractional Alternatives’, Economics Letters, 35, 155–60.CrossRefGoogle Scholar
Dimand, R. W. (1993), ‘The Case of Brownian Motion: A Note on Bachelier's Contribution’, British Journal for the History of Science, 26, 233–4.CrossRefGoogle Scholar
Ding, Z., and Granger, C. W. J. (1996), ‘Modeling Persistence of Speculative Returns: A New Approach’, Journal of Econometrics, 73, 185–215.CrossRefGoogle Scholar
Ding, Z., Granger, C. W. J., and Engle, R. F. (1993), ‘A Long Memory Property of Stock Returns and a New Model’, Journal of Empirical Finance, 1, 83–106.CrossRefGoogle Scholar
Dittman, I., and Granger, C. W. J. (2002), ‘Properties of Nonlinear Transformations of Fractionally Integrated Processes’, Journal of Econometrics, 110, 113–33.CrossRefGoogle Scholar
Doan, T. A., Litterman, R. B., and Sims, C. A. (1984), ‘Forecasting and Conditional Projection Using Realistic Prior Distributions’, Econometric Reviews, 3, 1–100.CrossRefGoogle Scholar
Dolado, J. J., Gonzalo, J., and Moayoral, L. (2002), ‘A Fractional Dickey–Fuller Test for Unit Roots’, Econometrica, 70, 1963–2006.CrossRefGoogle Scholar
Dolado, J. J., Jenkinson, T., and Sosvilla-Rivero, S. (1990), ‘Cointegration and Unit Roots’, Journal of Economic Surveys, 4, 249–73.CrossRefGoogle Scholar
Domowitz, I., and El-Gamal, M. A. (2001), ‘A Consistent Nonparametric Test of Ergodicity for Time Series with Applications’, Journal of Econometrics, 102, 365–98.CrossRefGoogle Scholar
Domowitz, I., and Hakkio, C. S. (1985), ‘Conditional Variance and the Risk Premium in the Foreign Exchange Market’, Journal of International Economics, 19, 47–66.CrossRefGoogle Scholar
Dotsis, G., and Markellos, R. N. (2007), ‘The Finite Sample Properties of the GARCH Option Pricing Model’, Journal of Futures Markets, 27, 599–615.CrossRefGoogle Scholar
Drost, F. C., and Nijman, T. E. (1993), ‘Temporal Aggregation of GARCH Processes’, Econometrica, 61, 909–27.CrossRefGoogle Scholar
Drost, F. C., and Werker, B. J. M. (1996), ‘Closing the GARCH Gap: Continuous Time GARCH Modeling’, Journal of Econometrics, 74, 31–57.CrossRefGoogle Scholar
Duan, J., Gauthier, G., and Simonato, J. (1999), ‘An Analytical Approximation for the GARCH Option Pricing Model’, Journal of Computational Finance, 2, 75–116.CrossRefGoogle Scholar
Dufour, J.-M. (1982), ‘Recursive Stability Analysis of Linear Regression Relationships: An Exploratory Analysis’, Journal of Econometrics, 19, 31–75.CrossRefGoogle Scholar
Dufour, J.-M., Khalaf, L., Bernard, J.-T., and Genest, I. (2004), ‘Simulation-based Finite-sample Tests for Heteroskedasticity and ARCH Effects’, Journal of Econometrics, 122, 317–47.CrossRefGoogle Scholar
EViews (2003), EViews User Guide Version 5.0, Irvine, CA: Quantitative Micro Software.
Edgerton, D., and Wells, C. (1994), ‘Critical Values for the CUSUMSQ Statistic in Medium and Large Sized Samples’, Oxford Bulletin of Economics and Statistics, 56, 355–65.CrossRefGoogle Scholar
Efron, B., and Tibshirani, R. J. (1993), An Introduction to the Bootstrap, London: Chapman and Hall.CrossRefGoogle Scholar
Eitrhem, Ø., and Teräsvirta, T. (1996), ‘Testing the Adequacy of Smooth Transition Autoregressive Models’, Journal of Econometrics, 74, 59–75.CrossRefGoogle Scholar
Elliott, G., Rothenberg, T. J., and Stock, J. H. (1996), ‘Efficient Tests for an Autoregressive Unit Root’, Econometrica, 64, 813–36.CrossRefGoogle Scholar
Engle, C. R., and Hamilton, J. D. (1990), ‘Long Swings in the Dollar; Are They in the Data and do Markets Know It?’, American Economic Review, 80, 689–713.Google Scholar
Engle, R. F. (1982), ‘Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK Inflation’, Econometrica, 50, 987–1008.CrossRefGoogle Scholar
Engle, R. F.(1990), ‘Discussion: Stock Market Volatility and the Crash of 1987’, Review of Financial Studies, 3, 103–6.CrossRefGoogle Scholar
Engle, R. F.(2000), ‘The Econometrics of Ultra-high Frequency Data’, Econometrica, 68, 1–22.CrossRefGoogle Scholar
Engle, R. F.(2001), ‘GARCH 101: An Introduction to the use of ARCH/GARCH Models in Applied Econometrics’, Journal of Economic Perspectives, 15, 157–68.CrossRefGoogle Scholar
Engle, R. F.(2002), ‘New Frontiers for ARCH models’, Journal of Applied Econometrics, 17, 425–46.CrossRefGoogle Scholar
Engle, R. F., and Bollerslev, T. (1986), ‘Modelling the Persistence of Conditional Variances’, Econometric Reviews, 5, 1–50.CrossRefGoogle Scholar
Engle, R. F., and Gallo, G. M., (2006), ‘A Multiple Indicators Model for Volatility using Intra-daily Data’, Journal of Econometrics, 127, 3–27.CrossRefGoogle Scholar
Engle, R. F., and Gonzalez-Rivera, G. (1991), ‘Semiparametric ARCH Models’, Journal of Business and Economic Statistics, 9, 345–59.Google Scholar
Engle, R. F., and Granger, C. W. J. (1987), ‘Cointegration and Error Correction: Representation, Estimation and Testing’, Econometrica, 55, 251–76.CrossRefGoogle Scholar
Engle, R. F., and Hendry, D. F. (1993), ‘Testing Super Exogeneity and Invariance in Regression Models’, Journal of Econometrics, 56, 119–39.CrossRefGoogle Scholar
Engle, R. F., Hendry, D. F., and Richard, J.-F. (1983), ‘Exogeneity’, Econometrica, 51, 277–304.CrossRefGoogle Scholar
Engle, R. F., Hendry, D. F., and Trumble, D. (1985), ‘Small Sample Properties of ARCH Estimators and Tests’, Canadian Journal of Economics, 43, 66–93.CrossRefGoogle Scholar
Engle, R. F., and Issler, J. V. (1995), ‘Estimating Common Sectoral Cycles’, Journal of Monetary Economics, 35, 83–113.CrossRefGoogle Scholar
Engle, R. F., and Kozicki, S. (1993), ‘Testing for Common Features’, Journal of Business and Economic Statistics, 11, 369–80.Google Scholar
Engle, R. F., and Kroner, K. F. (1995), ‘Multivariate Simultaneous Generalized ARCH’, Econometric Theory, 11, 122–50.CrossRefGoogle Scholar
Engle, R. F., and Lee, G. J. (1999), ‘A Permanent and Transitory Component Model of Stock Return Volatility’, in Engle, R. F. and White, H. (eds.), Cointegration, Causality, and Forecasting: A Festchrift in Honor of Clive W. J. Granger, Oxford: Oxford University Press, 475–97.Google Scholar
Engle, R. F., Lilien, D. M., and Robbins, R. P. (1987), ‘Estimating Time Varying Risk Premia in the Term Structure: The ARCH-M Model’, Econometrica, 55, 391–408.CrossRefGoogle Scholar
Engle, R. F., and Ng, V. (1993), ‘Measuring and Testing the Impact of News on Volatility’, Journal of Finance, 48, 1749–78.CrossRefGoogle Scholar
Engle, R. F., and Patton, A. (2001), ‘What Good is a Volatility Model?’, Quantitative Finance, 1, 237–45.CrossRefGoogle Scholar
Engle, R. F., and Russell, J. R. (1997), ‘Forecasting the Frequency of Changes in Quoted Foreign Exchange Prices with the Autoregressive Conditional Duration Model’, Journal of Empirical Finance, 4, 187–212.CrossRefGoogle Scholar
Engle, R. F., and Russell, J. R.(1998), ‘Autoregressive Conditional Duration: A New Model for Irregularly-spaced Transaction Data’, Econometrica, 66, 1127–62.CrossRefGoogle Scholar
Engle, R. F., and Smith, A. D. (1999), ‘Stochastic Permanent Breaks’, Review of Economics and Statistics, 81, 553–74.CrossRefGoogle Scholar
Eraker, B. (2004), ‘Do Stock Prices and Volatility Jump? Reconciling Evidence from Spot and Option Prices’, Journal of Finance, 59, 1367–403.CrossRefGoogle Scholar
Eraker, B., Johannes, M., and Polson, N. (2003), ‘The Impact of Jumps in Volatility and Returns’, Journal of Finance, 59, 1269–300.CrossRefGoogle Scholar
Ericsson, N. R., and MacKinnon, J. G. (2002), ‘Distributions of Error Correction Tests for Cointegration’, Econometrics Journal, 5, 285–318.CrossRefGoogle Scholar
Faff, R., and Gray, P. (2006), ‘On the Estimation and Comparison of Short-rate Models Using the Generalised Method of Moments’, Journal of Banking and Finance, 30, 3131–46.CrossRefGoogle Scholar
Fama, E. F. (1965), ‘The Behaviour of Stock-Market Prices’, Journal of Business, 38, 34–105.CrossRefGoogle Scholar
Fama, E. F.(1970), ‘Efficient Capital Markets: A Review of Theory and Empirical Work’, Journal of Finance, 25, 383–417.CrossRefGoogle Scholar
Fama, E. F.(1975), ‘Short Term Interest Rates as Predictors of Inflation’, American Economic Review, 65, 269–82.Google Scholar
Fama, E. F.(1991), ‘Efficient Capital Markets II’, Journal of Finance, 26, 1575–617.CrossRefGoogle Scholar
Fama, E. F.(1998), ‘Market Efficiency, Long-term Returns, and Behavioural Finance’, Journal of Financial Economics, 49, 283–306.CrossRefGoogle Scholar
Fama, E. F., and MacBeth, J. D. (1973), ‘Risk, Return, and Equilibrium: Empirical Tests’, Journal of Political Economy, 81, 607–36.CrossRefGoogle Scholar
Fan, J., and Yao, Q. (2003), Nonlinear Time Series: Nonparametric and Parametric Methods, New York: Springer.CrossRefGoogle Scholar
Feller, W. (1966), An Introduction to Probability Theory and its Applications, Vol. II, New York: Wiley.Google Scholar
Fernandes, M., and Grammig, J. (2005), ‘Nonparametric Specification Tests for Conditional Duration Models’, Journal of Econometrics, 27, 35–68.CrossRefGoogle Scholar
Fernandes, M., and Grammig, J.(2006) ‘A Family of Autoregressive Conditional Duration Models’, Journal of Econometrics, 130, 1–23.CrossRefGoogle Scholar
Fernández-Rodriguez, F., Sosvilla-Rivero, S., and Andrada-Félix, J. (2005), ‘Testing Chaotic Dynamics via Lyapunov Exponents’, Journal of Applied Econometrics, 20, 911–30.CrossRefGoogle Scholar
Fiorentini, G., Sentana, E., and Shephard, N. (2004), ‘Likelihood-based Estimation of Latent Generalized ARCH Structures’, Econometrica, 72, 1481–517.CrossRefGoogle Scholar
Fong, W. M., Koh, S. K., and Ouliaris, S. (1997), ‘Joint Variance-ratio Tests of the Martingale Hypothesis for Exchange Rates’, Journal of Business and Economic Statistics, 15, 51–9.Google Scholar
Fox, R., and Taqqu, M. S. (1986), ‘Large-sample Properties of Parameter Estimates for Strongly Dependent Stationary Gaussian Time Series’, Annals of Statistics, 14, 517–32.CrossRefGoogle Scholar
Frankel, F. A., and Froot, K. A. (1988), ‘Chartists, Fundamentalists and the Demand for Dollars’, Greek Economic Review, 10, 49–102.Google Scholar
Franses, P. H., and Paap, R. (1999), ‘Does Seasonal Adjustment Change Inference from Markov Switching Models?’, Journal of Macroeconomics, 21, 79–92.CrossRefGoogle Scholar
French, K. R., Schwert, G. W., and Stambaugh, R. F. (1987), ‘Expected Stock Returns and Volatility’, Journal of Financial Economics, 19, 3–29.CrossRefGoogle Scholar
Fuller, W. A. (1976), Introduction to Statistical Time Series, New York: Wiley.Google Scholar
Fuller, W. A.(1996), Introduction to Statistical Time Series, 2nd edn., New York: Wiley.Google Scholar
Gallant, A. R., Rossi, P. E., and Tauchen, G. (1992), ‘Stock Prices and Volume’, Review of Financial Studies, 5, 199–242.CrossRefGoogle Scholar
Gallant, A. R., and White, H. (1988), A Unified Theory of Estimation and Inference for Nonlinear Dynamics, Oxford: Blackwell.Google Scholar
Garcia, R., Ghysels, E., and Renault, E. (2007), ‘The Econometrics of Option Pricing’, in Aıt-Sahalia, Y. and Hansen, L. P. (eds.), Handbook of Financial Econometrics, New York: Elsevier.Google Scholar
Garman, M. B., and Klass, M. J. (1980), ‘On the Estimation of Security Price Volatilities from Historical Data’, Journal of Business, 53, 67–78.CrossRefGoogle Scholar
Gavridis, M., Markellos, R. N., and Mills, T. C. (1999), ‘High-frequency Random Walks?’, in Lequeux, P. (ed.), The Financial Markets Tick by Tick: Insights in Financial Markets Microstructure, Chichester: Wiley, 227–54.Google Scholar
Geweke, J. (1978), ‘Testing the Exogeneity Specification in the Complete Dynamic Simultaneous Equations Model’, Journal of Econometrics, 7, 163–85.CrossRefGoogle Scholar
Geweke, J.(1984), ‘Inference and Causality in Economic Time Series Models’, in Griliches, Z. and Intriligator, M. D. (eds.), Handbook of Econometrics, Vol. II, Amsterdam: North-Holland, 1101–44.Google Scholar
Geweke, J.(1993), ‘Inference and Forecasting for Chaotic Nonlinear Time Series’, in Chen, P. and Day, R. (eds.), Nonlinear Dynamics and Evolutionary Economics, Oxford: Oxford University Press, 459–512.Google Scholar
Geweke, J., and Porter-Hudak, S. (1983), ‘The Estimation and Application of Long Memory Time Series Models’, Journal of Time Series Analysis, 4, 221–38.CrossRefGoogle Scholar
Ghose, D., and Kroner, K. F. (1995), ‘The Relationship between GARCH and Symmetric Stable Processes: Finding the Source of Fat Tails in Financial Data’, Journal of Empirical Finance, 2, 225–51.CrossRefGoogle Scholar
Ghysels, E., Granger, C. W. J., and Siklos, P. L. (1996), ‘Is Seasonal Adjustment a Linear or Nonlinear Data Filtering Process?’, Journal of Business and Economic Statistics, 14, 374–86.Google Scholar
Ghysels, E., Harvey, A. C., and Renault, E. (1996), ‘Stochastic Volatility’, in Maddala, G. S. (ed.), Handbook of Statistics, Vol. XIV: Statistical Method in Finance, Amsterdam: North-Holland, 119–91.Google Scholar
Ghysels, E., Santa-Clara P., and Valkanov, R. (2006), ‘Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies’, Journal of Econometrics, 131, 59–95.CrossRef
Gibbons, M. R. (1982), ‘Multivariate Tests of Financial Models’, Journal of Financial Economics, 10, 3–27.CrossRefGoogle Scholar
Gibbons, M. R., Ross, S. A., and Shanken, J. (1989), ‘A Test of the Efficiency of a Given Portfolio’, Econometrica, 57, 1121–52.CrossRefGoogle Scholar
Gibson, R., Lhabitant, F. S., Pistre, N., and Talay, D. (1999), ‘Interest Rate Model Risk: An Overview’, The Journal of Risk, 1, 37–62.CrossRefGoogle Scholar
Gil-Alana, L. A. (2003), ‘Testing of Fractional Cointegration in Macroeconomic Time Series’, Oxford Bulletin of Economics and Statistics, 65, 517–29.CrossRefGoogle Scholar
Giot, P. (2000), ‘Time Transformations, Intraday Data and Volatility Models’, Journal of Computational Finance, 4, 31–62.CrossRefGoogle Scholar
Giraitis, L., Kokoszka, P., Leipus, R., and Teyssière, G. (2003), ‘Rescaled Variance and Related Tests for Long Memory in Volatility and Levels’, Journal of Econometrics, 112, 265–94.CrossRefGoogle Scholar
Giraitis, L., Leipus, R., and Surgailis, D. (2006), ‘Recent Advances in ARCH Modelling’, in Kirman, A. and Teyssiere, G. (eds.), Long Memory in Economics, Berlin: Springer, 3–38.Google Scholar
Giraitis, L., and Surgailis, D. (2002), ‘ARCH-type Bilinear Models with Double Long Memory,’ Stochastic Proccesses and their Applications, 100, 275–300.CrossRefGoogle Scholar
Glosten, L. R., Jagannathan, R., and Runkle, D. (1993), ‘Relationship between the Expected Value and the Volatility of the Nominal Excess Return on Stocks’, Journal of Finance, 48, 1779–801.CrossRefGoogle Scholar
Godfrey, L. G. (1979), ‘Testing the Adequacy of a Time Series Model’, Biometrika, 66, 67–72.CrossRefGoogle Scholar
Godfrey, L. G.(1988), Misspecification Tests in Econometrics, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Goldberg, M. D., and Frydman, R. (1996), ‘Imperfect Knowledge and Behaviour in the Foreign Exchange Market’, Economic Journal, 106, 869–93.CrossRefGoogle Scholar
Gonzalez-Rivera, G. (1998), ‘Smooth Transition GARCH Models’, Studies in Nonlinear Dynamics and Econometrics, 3, 161–78.Google Scholar
Gonzalez-Rivera, G., and Drost, F. C. (1999), ‘Efficiency Comparisons of Maximum-likelihood Based Estimators in GARCH Models’, Journal of Econometrics, 93, 93–111.CrossRefGoogle Scholar
Goodhart, C. A. E., and O'Hara, M. (1997), ‘High Frequency Data in Financial Markets: Issues and Applications’, Journal of Empirical Finance, 4, 73–114.CrossRefGoogle Scholar
Grammig, J., and Wellner, M. (2002), ‘Modeling the Interdependence of Volatility and Inter-transaction Duration Processes’, Journal of Econometrics, 106, 369–400.CrossRefGoogle Scholar
Granger, C. W. J. (1966), ‘The Typical Spectral Shape of an Economic Variable’, Econometrica, 34, 150–61.CrossRefGoogle Scholar
Granger, C. W. J.(1969), ‘Investigating Causal Relations by Econometric Models and Cross-spectral Methods’, Econometrica, 37, 424–38.CrossRefGoogle Scholar
Granger, C. W. J.(1980), ‘Long Memory Relationships and the Aggregation of Dynamic Models’, Journal of Econometrics, 14, 227–38.CrossRefGoogle Scholar
Granger, C. W. J.(1992), ‘Comment’, Statistical Science, 7, 102–4.CrossRefGoogle Scholar
Granger, C. W. J.(1995), ‘Modelling Nonlinear Relationships Between Extended-memory Variables’, Econometrica, 63, 265–79.CrossRefGoogle Scholar
Granger, C. W. J.(1997), ‘On Modelling the Long Run in Applied Economics’, Economic Journal, 107, 169–77.CrossRefGoogle Scholar
Granger, C. W. J.(1998), ‘Extracting Information from Mega-panels and High-frequency Data’, Statistica Neerlandica, 52, 258–72.CrossRefGoogle Scholar
Granger, C. W. J., and Andersen, A. P. (1978), An Introduction to Bilinear Time Series Models, Göttingen: Vandenhoeck and Ruprecht.Google Scholar
Granger, C. W. J., and Ding, Z. (1995), ‘Some Properties of Absolute Returns: An Alternative Measure of Risk’, Annales d'Economie et de Statistique, 40, 67–91.CrossRefGoogle Scholar
Granger, C. W. J., and Ding, Z.(1996), ‘Varieties of Long Memory Models’, Journal of Econometrics, 73, 61–78.CrossRefGoogle Scholar
Granger, C. W. J., and Hallman, J. J. (1991), ‘Long Memory Series with Attractors’, Oxford Bulletin of Economics and Statistics, 53, 11–26.CrossRefGoogle Scholar
Granger, C. W. J., and Hyung, N. (2004), ‘Occasional Structural Breaks and Long Memory with an Application to the S&P 500 Absolute Stock Returns’, Journal of Empirical Finance, 11, 399–421.CrossRefGoogle Scholar
Granger, C. W. J., and Hyung, N.(2006), ‘Introduction to M-M Processes’, Journal of Econometrics, 130, 143–64.CrossRefGoogle Scholar
Granger, C. W. J., Inoue, T., and Morin, N. (1997), ‘Nonlinear Stochastic Trends’, Journal of Econometrics, 81, 65–92.CrossRefGoogle Scholar
Granger, C. W. J., and Joyeux, R. (1980), ‘An Introduction to Long Memory Time Series Models and Fractional Differencing’, Journal of Time Series Analysis, 1, 15–29.CrossRefGoogle Scholar
Granger, C. W. J., and Morgenstern, O. (1970), Predictability of Stock Market Prices, Lexington, Heath MA: Lexington Books.Google Scholar
Granger, C. W. J., and Morris, M. J. (1976), ‘Time Series Modelling and Interpretation’, Journal of the Royal Statistical Society, Series A, 139, 246–57.CrossRefGoogle Scholar
Granger, C. W. J., and Newbold, P. (1974), ‘Spurious Regressions in Econometrics’, Journal of Econometrics, 2, 111–20.CrossRefGoogle Scholar
Granger, C. W. J., and Newbold, P.(1986), Forecasting Economic Time Series, 2nd edn., New York: Academic Press.Google Scholar
Granger, C. W. J., and Orr, D. (1972), ‘ “Infinite Variance” and Research Strategy in Time Series Analysis’, Journal of the American Statistical Association, 67, 275–85.Google Scholar
Granger, C. W. J., Spear, S., and Ding, Z. (2000), ‘Stylized Facts on the Temporal and Distributional Properties of Absolute Returns: An Update’, in Chan, W.-S., Li, W. K. and Tong, H. (eds.), Statistics and Finance: An Interface, London: Imperial College Press, 97–120.CrossRefGoogle Scholar
Granger, C. W. J., and Swanson, N. (1996), ‘Future Developments in the Study of Cointegrated Variables’, Oxford Bulletin of Economics and Statistics, 58, 537–53.CrossRefGoogle Scholar
Granger, C. W. J., and Swanson, N.(1997), ‘An Introduction to Stochastic Unit Root Processes’, Journal of Econometrics, 80, 35–62.CrossRefGoogle Scholar
Granger, C. W. J., and Teräsvirta, T. (1993), Modeling Nonlinear Economic Relationships, Oxford: Oxford University Press.Google Scholar
Granger, C. W. J., and Teräsvirta, T.(1999), ‘A Simple Nonlinear Time Series Model with Misleading Linear Properties’, Economics Letters, 62, 161–5.CrossRefGoogle Scholar
Granger, C. W. J., and Yoon, G. (2002), Hidden Cointegration, Economics Working Paper 2002–02, San Diego: University of California.Google Scholar
Gregory, A. W., and Hansen, B. E. (1996), ‘Residual-based Tests for Cointegration in Models with Regime Shifts’, Journal of Econometrics, 70, 99–126.CrossRefGoogle Scholar
Gregory, A. W., Nason, J. M., and Watt, D. G. (1996), ‘Testing for Structural Breaks in Cointegrating Relationships’, Journal of Econometrics, 71, 321–41.CrossRefGoogle Scholar
Griffeath, D. (1992), ‘Comment: Randomness in Complex Systems’, Statistical Science, 7, 108.CrossRefGoogle Scholar
Groenendijk, P. A., Lucas, A., and Vries, C. G. (1995), ‘A Note on the Relationship between GARCH and Symmetric Stable Processes’, Journal of Empirical Finance, 2, 253–64.CrossRefGoogle Scholar
Guégan, D. (1987), ‘Different Representations for Bilinear Models’, Journal of Time Series Analysis, 8, 389–408.CrossRefGoogle Scholar
Haas, M., Mittnik, S., and Paolella, M. S. (2004), ‘A New Approach to Markov-switching GARCH Models’, Journal of Financial Econometrics, 4, 493–530.CrossRefGoogle Scholar
Haefke, C., and Helmenstein, C. (1996), ‘Forecasting Austrian IPOs: An Application of Linear and Neural Network Error-correction Models’, Journal of Forecasting, 15, 237–52.3.0.CO;2-5>CrossRefGoogle Scholar
Haggan, V., Heravi, S. M., and Priestley, M. B. (1984), ‘A Study of the Application of State-dependent Models in Non-linear Time Series Analysis’, Journal of Time Series Analysis, 5, 69–102.CrossRefGoogle Scholar
Haggan, V., and Ozaki, T. (1981), ‘Modelling Non-linear Vibrations Using an Amplitude Dependent Autoregressive Time Series Model’, Biometrika, 68, 189–96.CrossRefGoogle Scholar
Haldrup, N., and Jansson, M. (2006), ‘Improving Size and Power in Unit Root Testing’, in Mills, T. C. and Patterson, K. (eds.), Palgrave Handbook of Econometrics, Vol. I: Econometric Theory, Basingstoke: Palgrave Macmillan, 252–77.Google Scholar
Hall, P. (1982), ‘On Some Simple Estimates of an Exponent of Regular Variation’, Journal of the Royal Statistical Society, Series B, 44, 37–42.Google Scholar
Hall, P., and Welsh, A. H. (1985), ‘Adaptive Estimates of Parameters of Regular Variation’, Annals of Statistics, 13, 331–41.CrossRefGoogle Scholar
Hall, S. G., Psaradakis, Z., and Sola, M. (1997), ‘Cointegration and Changes in Regime: The Japanese Consumption Function’, Journal of Applied Econometrics, 12, 151–68.3.0.CO;2-J>CrossRefGoogle Scholar
Hamilton, J. D. (1986), ‘On Testing for Self-fulfilling Speculative Price Bubbles’, International Economic Review, 27, 545–52.CrossRefGoogle Scholar
Hamilton, J. D.(1989), ‘A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle’, Econometrica, 57, 357–84.CrossRefGoogle Scholar
Hamilton, J. D.(1990), ‘Analysis of Time Series Subject to Changes in Regime’, Journal of Econometrics, 45, 39–70.CrossRefGoogle Scholar
Hamilton, J. D.(1994), Time Series Analysis, Princeton, NJ: Princeton University Press.Google Scholar
Hamilton, J. D., and Susmel, R. (1994), ‘Autoregressive Conditional Heteroskedasticity and Changes in Regime’, Journal of Econometrics, 64, 307–33.CrossRefGoogle Scholar
Hamilton, J. D., and Whiteman, C. H. (1985), ‘The Observable Implications of Self-fulfilling Expectations’, Journal of Monetary Economics, 16, 353–73.CrossRefGoogle Scholar
Hamilton, W. P. (1922), The Stock Market Barometer, New York: Harper and Brothers.Google Scholar
Hansen, B. E. (1992), ‘Testing for Parameter Instability in Linear Models’, Journal of Policy Modelling, 14, 517–33.CrossRefGoogle Scholar
Hansen, B. E.(1994), ‘Autoregressive Conditional Density Estimation’, International Economic Review, 35, 705–30.CrossRefGoogle Scholar
Hansen, L. P. (1982), ‘Large Sample Properties of Generalized Method of Moments Estimators’, Econometrica, 50, 1029–54.CrossRefGoogle Scholar
Hansen, L. P., and Hodrick, R. J. (1980), ‘Forward Exchange Rates as Optimal Predictors of Future Spot Rates’, Journal of Political Economy, 88, 829–53.CrossRefGoogle Scholar
Hansen, P. R., and Lunde, A. (2005), ‘A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH (1,1)?’, Journal of Applied Econometrics, 20, 873–89.CrossRefGoogle Scholar
Hansen, P. R., and Lunde, A.(2006a), ‘Consistent Ranking of Volatility Models’, Journal of Econometrics, 131, 97–121.CrossRefGoogle Scholar
Hansen, P. R., and Lunde, A.(2006b), ‘Realized Variance and Market Microstructure Noise’, Journal of Business and Economic Statistics, 24, 127–61.CrossRefGoogle Scholar
Härdle, W. (1990), Applied Nonparametric Regression, Oxford: Oxford University Press.CrossRefGoogle Scholar
Harvey, A. C. (1989), Forecasting Structural Time Series Models and the Kalman Filter, Cambridge: Cambridge University Press.Google Scholar
Harvey, A. C.(1998), ‘Long Memory in Stochastic Volatility’, in Knight, J. and Satchell, S. (eds.), Forecasting Volatility in Financial Markets, Oxford: Butterworth-Heinemann, 307–20.Google Scholar
Harvey, A. C., Ruiz, E., and Sentana, E. (1992), ‘Unobserved Component Models with ARCH Disturbances’, Journal of Econometrics, 52, 129–57.CrossRefGoogle Scholar
Harvey, A. C., Ruiz, E., and Shephard, N. (1994), ‘Multivariate Stochastic Variance Models’, Review of Economic Studies, 61, 247–64.CrossRefGoogle Scholar
Harvey, A. C., and Shephard, N. (1992), ‘Structural Time Series Models’, in Maddala, G. S., Rao, C. R. and Vinod, H. D. (eds.), Handbook of Statistics, Vol. XI: Econometrics, Amsterdam: North-Holland, 261–302.Google Scholar
Harvey, A. C., and Shephard, N.(1996), ‘Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns’, Journal of Business and Economic Statistics, 14, 429–34.Google Scholar
Harvey, C. R., and Siddique, A. (2000), ‘Conditional skewness in asset pricing tests’, Journal of Finance, 55, 1263–95.CrossRefGoogle Scholar
Harvey, D. I., Leybourne, S. J., and Newbold, P. (2001), ‘Innovational Outlier Unit Root Tests with an Endogenously Determined Break in Level’, Oxford Bulletin of Economics and Statistics, 63, 559–75.CrossRefGoogle Scholar
Harvey, D. I., and Mills, T. C. (2002), ‘Unit Roots and Double Smooth Transitions’, Journal of Applied Statistics, 29, 675–83.CrossRefGoogle Scholar
Harvey, D. I., and Mills, T. C.(2003), ‘A Note on Busetti–Harvey Tests for Stationarity in Series with Structural Breaks’, Journal of Time Series Analysis, 24, 159–64.CrossRefGoogle Scholar
Harvey, D. I., and Mills, T. C.(2004) ‘Tests for Stationarity in Series with Endogenously Determined Structural Change’, Oxford Bulletin of Economics and Statistics, 66, 863–94.CrossRefGoogle Scholar
Hassler, U., and Wolters, J. (1994), ‘On the Power of Unit Root Tests against Fractionally Integrated Alternatives’, Economics Letters, 45, 1–5.CrossRefGoogle Scholar
Haug, A. A. (1996), ‘Tests for Cointegration: A Monte Carlo Comparison’, Journal of Econometrics, 71, 89–115.CrossRefGoogle Scholar
Haykin, S. (1999), Neural Networks: A Comprehensive Foundation, 2nd edn., Upper Saddle River, NJ: Prentice Hall.Google Scholar
Hendry, D. F. (1995), Dynamic Econometrics, Oxford: Oxford University Press.CrossRefGoogle Scholar
Hendry, D. F., and Doornik, J. A. (2006), PcGive 11, London: Timberlake Consultants.Google Scholar
Hendry, D. F., Pagan, A. R., and Sargan, J. D. (1984), ‘Dynamic Specification’, in Griliches, Z. and Intriligator, M. D. (eds.), Handbook of Econometrics, Vol. II, Amsterdam: North-Holland, 1023–100.Google Scholar
Hentschel, L. (1995), ‘All in the Family: Nesting Symmetric and Asymmetric GARCH Models’, Journal of Financial Economics, 39, 71–104.CrossRefGoogle Scholar
Heston, S. L. (1993), ‘A Closed-form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options’, Review of Financial Studies, 6, 327–43.CrossRefGoogle Scholar
Heston, S. L., and Nandi, S. (2000), ‘A Closed Form GARCH Option Pricing Model’, Review of Financial Studies, 13, 585–625.CrossRefGoogle Scholar
Hiemstra, C., and Jones, J. D. (1997), ‘Another Look at Long Memory in Common Stock Returns’, Journal of Empirical Finance, 4, 373–401.CrossRefGoogle Scholar
Higgins, M. L., and Bera, A. K. (1988), ‘A Joint Test for ARCH and Bilinearity in the Regression Model’, Econometric Reviews, 7, 171–81.CrossRefGoogle Scholar
Higgins, M. L., and Bera, A. K.(1992), ‘A Class of Nonlinear ARCH Models’, International Economic Review, 33, 137–58.CrossRefGoogle Scholar
Hilborn, R. C. (1997), ‘Resource Letter: Nonlinear Dynamics’, American Journal of Physics, 65, 822–34.CrossRefGoogle Scholar
Hill, B. M. (1975), ‘A Simple General Approach to Inference about the Tail of a Distribution’, Annals of Statistics, 3, 1163–74.CrossRefGoogle Scholar
Hillebrand, E. (2005), ‘Neglecting Parameter Changes in GARCH models’, Journal of Econometrics, 129, 121–38.CrossRefGoogle Scholar
Hinich, M. (1982), ‘Testing for Gaussianity and Linearity of a Stationary Time SeriesJournal of Time Series Analysis, 3, 169–76.CrossRefGoogle Scholar
Hinich, M., and Patterson, D. M. (1985), ‘Evidence of Nonlinearity in Daily Stock Returns’, Journal of Business and Economic Statistics, 3, 69–77.Google Scholar
Ho, M. S., and Sørensen, B. E. (1996), ‘Finding Cointegration Rank in High Dimensional Systems Using the Johansen Test: An Illustration Using Data Based Monte Carlo Simulations’, Review of Economics and Statistics, 78, 726–32.CrossRefGoogle Scholar
Hoaglin, D. C. (1985), ‘Summarizing Shape Numerically: The g-and-h Distributions’, in Hoaglin, D. C., Mosteller, F. and Tukey, J. W. (eds.), Exploring Data Tables, Trends and Shapes, New York: Wiley, 461–513.Google Scholar
Hodrick, R. J., and Prescott, E. C. (1997), ‘Postwar US Business Cycles: An Empirical Investigation’, Journal of Money Credit and Banking, 19, 1–16.CrossRefGoogle Scholar
Hols, M. C. A. B., and Vries, C. G. (1991), ‘The Limiting Distribution of Extremal Exchange Rate Returns’, Journal of Applied Econometrics, 6, 287–302.CrossRefGoogle Scholar
Hong, Y. (1997), ‘One-sided Testing for Conditional Heteroskedasticity in Time Series Models’, Journal of Time Series Analysis, 18, 253–77.CrossRefGoogle Scholar
Hong, Y., and Lee, T. H. (2003), ‘Diagnostic Checking for the Adequacy of Nonlinear Time Series Models’, Econometric Theory, 19, 1065–121.CrossRefGoogle Scholar
Hornik, K., Stinchcombe, M., and White, H. (1989), ‘Multilayer Feed-forward Networks Are Universal Approximators’, Neural Networks, 2, 359–66.CrossRefGoogle Scholar
Horowitz, J. L. (2001), ‘The Bootstrap’, in Heckman, J. J. and Leamer, E. E. (eds.), Handbook of Econometrics, Vol. V, Amsterdam: Elsevier Science, 3159–228.Google Scholar
Horowitz, J. L., Lobato, I. N., Nankervis, J. C., and Savin, N. E. (2006), ‘Bootstrapping the Box–Pierce Q Test: A Robust Test of Uncorrelatedness’, Journal of Econometrics, 133, 841–62.CrossRefGoogle Scholar
Hosking, J. R. M. (1981), ‘Fractional Differencing’, Biometrika, 68, 165–76.CrossRefGoogle Scholar
Hosking, J. R. M.(1984), ‘Modelling Persistence in Hydrological Time Series Using Fractional Differencing’, Water Resources Research, 20, 1898–908.CrossRefGoogle Scholar
Hsiao, C. (1997), ‘Cointegration and Dynamic Simultaneous Equation Model’, Econometrica, 65, 647–70.CrossRefGoogle Scholar
Hsieh, D. A. (1989a), ‘Modelling Heteroskedasticity in Daily Foreign Exchange Rates’, Journal of Business and Economic Statistics, 7, 307–17.Google Scholar
Hsieh, D. A.(1989b), ‘Testing for Nonlinear Dependence in Daily Foreign Exchange Rates’, Journal of Business, 62, 339–68.CrossRefGoogle Scholar
Hsieh, D. A.(1991), ‘Chaos and Nonlinear Dynamics: Application to Financial Markets’, Journal of Finance, 46, 1839–77.CrossRefGoogle Scholar
Huang, X., and Tauchen, G. (2005), ‘The Relative Contribution of Jumps to Total Price Variance’, Journal of Financial Econometrics, 3, 456–99.CrossRefGoogle Scholar
Hughes, A. W., King, M. L., and Teng, K. K. (2004), ‘Selecting the Order of an ARCH Model’, Economic Letters, 83, 269–75.CrossRefGoogle Scholar
Hull, J. (2005), Options, Futures and Other Derivatives, 6th edn., London: Prentice Hall.Google Scholar
Hurst, H. (1951), ‘Long Term Storage Capacity of Reservoirs’, Transactions of the American Society of Civil Engineers, 116, 770–99.Google Scholar
Hurvich, C. M., Deo, R. S., and Brodsky, J. (1998), ‘The Mean Square Error of Geweke and Porter-Hudak's Estimator of the Memory Parameter of a Long Memory Time Series’, Journal of Time Series Analysis, 19, 19–46.CrossRefGoogle Scholar
Hurvich, C. M., and Ray, B. K. (1995), ‘Estimation of the Memory Parameter for Nonstationary or Noninvertible Fractionally Integrated Processes’, Journal of Time Series Analysis, 16, 17–41.CrossRefGoogle Scholar
Hylleberg, S., Engle, R. F., Granger, C. W. J., and Yoo, B. S. (1990), ‘Seasonal Integration and Co-integration’, Journal of Econometrics, 44, 215–28.CrossRefGoogle Scholar
Hylleberg, S., and Mizon, G. E. (1989), ‘A Note on the Distribution of the Least Squares Estimator of a Random Walk with Drift’, Economic Letters, 29, 225–30.CrossRefGoogle Scholar
Jacquier, E., Polson, N. G., and Rossi, P. E. (2004), ‘Bayesian Analysis of Stochastic Volatility Models with Fat-tails and Correlated Errors’, Journal of Econometrics, 122, 185–212.CrossRefGoogle Scholar
Jansen, D. W., and Vries, C. G. (1991), ‘On the Frequency of Large Stock Returns: Putting Booms and Busts into Perspective’, Review of Economics and Statistics, 73, 18–24.CrossRefGoogle Scholar
Jarque, C. M., and Bera, A. K. (1980), ‘Efficient Tests for Normality, Homoskedasticity and Serial Dependence of Regression Residuals’, Economics Letters, 6, 255–9.CrossRefGoogle Scholar
Jensen, S. T., and Rahbek, A. (2004), ‘Asymptotic Inference for Nonstationary ARCH’, Econometric Theory, 20, 1203–26.CrossRefGoogle Scholar
Johannes, M., and Polson, N. G. (2007), ‘MCMC Methods for Financial Econometrics’, in Aït-Sahalia, Y. and Hansen, L. P. (eds.), Handbook of Financial Econometrics, New York: Elsevier.Google Scholar
Johansen, S. (1995), Likelihood-based Inference in Cointegrated Vector Autoregressive Models, Oxford: Oxford University Press.CrossRefGoogle Scholar
Johansen, S.(2002a), ‘A Small Sample Correction of the Test for Cointegrating Rank in the Vector Autoregressive Model’, Econometrica, 70, 1929–61.CrossRefGoogle Scholar
Johansen, S.(2002b), ‘A Small Sample Correction for Tests of Hypotheses on the Cointegrating Vectors’, Journal of Econometrics, 111, 195–221.CrossRefGoogle Scholar
Johansen, S.(2006), ‘Cointegration: An Overview’, in Mills, T. C. and Patterson, K. (eds.), Palgrave Handbook of Econometrics, Vol. I: Econometric Theory, Basingstoke: Palgrave Macmillan, 540–77.Google Scholar
Johansen, S., and Juselius, K. (1994), ‘Identification of the Long-run and Short-run Structure: An Application to the ISLM Model’, Journal of Econometrics, 63, 7–36.CrossRefGoogle Scholar
Johnston, J., and DiNardo, J. (1997), Econometric Methods, 4th edn., New York: McGraw-Hill.Google Scholar
Jondeau, E., and Rockinger, M. (2003), ‘Conditional Volatility, Skewness, and Kurtosis: Existence, Persistence, and Comovements’, Journal of Economic Dynamics and Control, 27, 1699–737.CrossRefGoogle Scholar
Jones, C. S. (2003), ‘The Dynamics of Stochastic Volatility: Evidence from Underlying and Options Markets’, Journal of Econometrics, 116, 181–224.CrossRefGoogle Scholar
Jones, R. (1978), ‘Nonlinear Autoregressive Processes’, Proceedings of the Royal Society of London, A, 360, 71–95.CrossRefGoogle Scholar
Jorion, P. (1988), ‘On Jump Processes in the Foreign Exchange and Stock Markets’, Review of Financial Studies, 1, 427–45.CrossRefGoogle Scholar
Judge, G. G., Griffiths, W. E., Carter Hill, R., Lütkepohl, H., and Lee, T. C. (1985), The Theory and Practice of Econometrics, 2nd edn., New York: Wiley.Google Scholar
Kapetanios, G., Shin, Y., and Snell, A. (2006), ‘Testing for Cointegration in Nonlinear Smooth Transition Error Correction Models’, Econometric Theory, 22, 279–303.CrossRefGoogle Scholar
Karanasos, M., Psaradakis, Z., and Sola, M. (2004), ‘On the Autocorrelation Properties of Long-memory GARCH Processes’, Journal of Time Series Analysis, 25, 265–81.CrossRefGoogle Scholar
Keenan, D. M. (1985), ‘A Tukey Nonadditivity-type Test for Time Series Nonlinearity’, Biometrika, 72, 39–44.CrossRefGoogle Scholar
Kendall, M. J. (1953), ‘The Analysis of Economic Time Series, Part I: Prices’, Journal of the Royal Statistical Society, Series A, 96, 11–25.CrossRefGoogle Scholar
Kim, C. J., Nelson, C. R., and Startz, R. (1998), ‘Testing for Mean Reversion in Heteroskedastic Data Based on Gibbs-sampling-augmented Randomization’, Journal of Empirical Finance, 5, 131–54.CrossRefGoogle Scholar
Kim, K., and Schmidt, P. (1990), ‘Some Evidence on the Accuracy of Phillips–Perron Tests Using Alternative Estimates of Nuisance Parameters’, Economics Letters, 34, 345–50.CrossRefGoogle Scholar
Kleidon, A. W. (1986a), ‘Variance Bounds Tests and Stock Price Valuation Models’, Journal of Political Economy, 94, 953–1001.CrossRefGoogle Scholar
Kleidon, A. W.(1986b), ‘Bias in Small Sample Tests of Stock Price Rationality’, Journal of Business, 59, 237–61.CrossRefGoogle Scholar
Koedijk, K. G., and Kool, C. J. M. (1992), ‘Tail Estimates of East European Exchange Rates’, Journal of Business and Economic Statistics, 10, 83–96.Google Scholar
Koedijk, K. G., Schafgans, M. M. A., and Vries, C. G. (1990), ‘The Tail Index of Exchange Rate Returns’, Journal of International Economics, 29, 93–108.CrossRefGoogle Scholar
Koedijk, K. G., Stork, P. A., and Vries, C. G. (1992), ‘Differences between Foreign Exchange Rate Regimes: The View from the Tails’, Journal of International Money and Finance, 11, 462–73.CrossRefGoogle Scholar
Koenker, R. (1982), ‘Robust Methods in Econometrics’, Econometric Reviews, 1, 213–55.Google Scholar
Kokoszka, P. S., and Taqqu, M. S. (1994), ‘Infinite Variance Stable ARMA Processes’, Journal of Time Series Analysis, 15, 203–20.CrossRefGoogle Scholar
Kokoszka, P. S., and Taqqu, M. S.(1996), ‘Infinite Variance Stable Moving Averages with Long Memory’, Journal of Econometrics, 73, 79–99.CrossRefGoogle Scholar
Kon, S. (1984), ‘Models of Stock Returns: A Comparison’, Journal of Finance, 39, 147–65.Google Scholar
Koop, G. (1992), ‘ “Objective” Bayesian Unit Root Tests’, Journal of Applied Econometrics, 7, 65–82.CrossRefGoogle Scholar
Koop, G., Pesaran, M. H., and Potter, S. M. (1996), ‘Impulse Response Analysis in Nonlinear Multivariate Models’, Journal of Econometrics, 74, 119–47.CrossRefGoogle Scholar
Koop, G., and Potter, S. M. (2001), ‘Are Apparent Findings of Nonlinearity Due to Structural Instability in Economic Time Series?’, Econometrics Journal, 4, 37–55.CrossRefGoogle Scholar
Koopman, S. J., Harvey, A. C., Doornik, J. A., and Shephard, N. (2006), Stamp 6: Structural Time Series Analyser, Modeller and Predictor, London: Timberlake Consultants.Google Scholar
Koopman, S. J., Jungbacker, B., and Hol, E. (2005), ‘Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility Measurements’, Journal of Empirical Finance, 12, 445–75.CrossRefGoogle Scholar
Koopman, S. J., Shephard, N., and Doornik, J. A. (1999), ‘Statistical Algorithms for Models in State Space Using SsfPack 2.2’, Econometrics Journal, 2, 113–66.CrossRefGoogle Scholar
Krämer, W., and Ploberger, W. (1990), ‘The Local Power of CUSUM and CUSUM of Squares Tests’, Econometric Theory, 6, 335–47.Google Scholar
Kristensen, D. (2005), On Stationarity and Ergodicity of the Bilinear Model with Applications to GARCH Models, Working Paper, University of Wisconsin.Google Scholar
Kristensen, D., and Linton, O. (2006), ‘A Closed-form Estimator for the GARCH(1,1)-Model’, Econometric Theory, 22, 323–37.CrossRefGoogle Scholar
Kuan, C.-M., and White, H. (1994), ‘Artificial Neural Networks: An Econometric Perspective (with Discussion)’, Econometric Reviews, 13, 1–143.CrossRefGoogle Scholar
Künsch, H. R. (1987), ‘Statistical Aspects of Self-similar Processes’, in Prohorov, Yu and Sazanov, V. V. (eds.), Proceedings of the First World Congress of the Bernoulli Society, Utrecht: VNU Science Press, 67–74.Google Scholar
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., and Shin, Y. (1992), ‘Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root’, Journal of Econometrics, 54, 159–78.CrossRefGoogle Scholar
Kyrtsou, C., and Serletis, A. (2006), ‘Univariate Tests for Nonlinear Structure’, Journal of Macroeconomics, 28, 154–68.CrossRefGoogle Scholar
Lam, P. S. (1990), ‘The Hamilton Model with a General Autoregressive Component: Estimation and Comparison with Other Models of Economic Time Series’, Journal of Monetary Economics, 20, 409–32.CrossRefGoogle Scholar
Lanne, M., and Saikkonen, P. (2005), ‘Nonlinear GARCH Models for Highly Persistent Volatility’, Econometrics Journal, 8, 251–76.CrossRefGoogle Scholar
Lee, D., and Schmidt, P. (1996), ‘On the Power of the KPSS Test of Stationarity against Fractionally Integrated Alternatives’, Journal of Econometrics, 73, 285–302.CrossRefGoogle Scholar
Lee, J. H. H., and King, M. L. (1993), ‘A Locally Most Mean Powerful Based Score Test for ARCH and GARCH Regression Disturbances’, Journal of Business and Economic Statistics, 11, 17–27.Google Scholar
Lee, T.-H. (1994), ‘Spread and Volatility in Spot and Forward Exchange Rates’, Journal of International Money and Finance, 13, 375–83.CrossRefGoogle Scholar
Lee, T.-H., White, H., and Granger, C. W. J. (1993), ‘Testing for Neglected Nonlinearity in Time Series Models: A Comparison of Neural Network Methods and Alternative Tests’, Journal of Econometrics, 56, 269–90.CrossRefGoogle Scholar
Lequeux, P. (ed.) (1999), The Financial Markets Tick by Tick: Insights in Financial Markets Microstructure, Chichester: Wiley.Google Scholar
LeRoy, S. (1973), ‘Risk Aversion and the Martingale Property of Stock Returns’, International Economic Review, 14, 436–46.CrossRefGoogle Scholar
LeRoy, S.(1982), ‘Expectations Models of Asset Prices: A Survey of the Theory’, Journal of Finance, 37, 185–217.CrossRefGoogle Scholar
LeRoy, S.(1989) ‘Efficient Capital Markets and Martingales’, Journal of Economic Literature, 27, 1583–621.Google Scholar
Levy, H., and Markowitz, H. M. (1979), ‘Approximating Expected Utility by a Function of Mean and Variance’, American Economic Review, 69, 308–17.Google Scholar
Leybourne, S. J., and McCabe, B. P. M. (1994), ‘A Consistent Test for a Unit Root’, Journal of Business and Economic Statistics, 12, 157–66.Google Scholar
Leybourne, S. J., and McCabe, B. P. M.(1999) ‘Modified Stationarity Tests with Data Dependent Model Selection Rules’, Journal of Business and Economic Statistics, 17, 264–70.Google Scholar
Leybourne, S. J., McCabe, B. P. M., and Mills, T. C. (1996), ‘Randomized Unit Root Processes for Modelling and Forecasting Financial Time Series: Theory and Applications’, Journal of Forecasting, 15, 253–70.3.0.CO;2-C>CrossRefGoogle Scholar
Leybourne, S. J., McCabe, B. P. M., and Tremayne, A. R. (1996), ‘Can Economic Time Series be Differenced to Stationarity?’, Journal of Business and Economic Statistics, 14, 435–46.Google Scholar
Leybourne, S. J., Mills, T. C., and Newbold, P. (1998), ‘Spurious Rejections by Dickey–Fuller Tests in the Presence of a Break under the Null’, Journal of Econometrics, 87, 191–203.CrossRefGoogle Scholar
Leybourne, S. J., Newbold, P., and Vougas, D. (1998), ‘Unit Roots and Smooth Transitions’, Journal of Time Series Analysis, 19, 83–97.CrossRefGoogle Scholar
Li, C. W., and Li, W. K. (1996), ‘On a Double Threshold Autoregressive Heteroskedasticity Time Series Model’, Journal of Applied Econometrics, 11, 253–74.3.0.CO;2-8>CrossRefGoogle Scholar
Li, Q., Yang, J., Hsiao, C., and Chang, Y.-J. (2005), ‘The Relationship between Stock Returns and Volatility in International Stock Markets’, Journal of Empirical Finance, 12, 650–65.CrossRefGoogle Scholar
Li, W. K., Ling, S., and McAleer, M. (2002), ‘Recent Theoretical Results for Time Series Models with GARCH Errors’, Journal of Economic Surveys, 16, 245–69.CrossRefGoogle Scholar
Li, W. K., and Mak, T. K. (1994), ‘On the Squared Residual Autocorrelations in Nonlinear Time Series with Conditional Heteroskedasticity’, Journal of Time Series Analysis, 15, 627–36.CrossRefGoogle Scholar
Lieberman, O. (2001), ‘The Exact Bias of the Log-periodogram Regression Estimator’, Econometric Reviews, 20, 369–83.CrossRefGoogle Scholar
Liesenfeld, R., and Richard, J. F. (2003), ‘Univariate and Multivariate Stochastic Volatility Models: Estimation and Diagnostics’, Journal of Empirical Finance, 11, 1–27.Google Scholar
Ling, S. (2007), ‘Self-weighted and Local Quasi-maximum Likelihood Estimators for ARMA-GARCH/IGARCH Models’, Journal of Econometrics, 140, 849–73.CrossRefGoogle Scholar
Ling, S., and McAleer, M. (2003), ‘Asymptotic Theory for a Vector ARMA-GARCH Model’, Econometric Theory, 19, 280–310.CrossRefGoogle Scholar
Linton, O. (1993), ‘Adaptive Estimation of ARCH Models’, Econometric Theory, 9, 539–69.CrossRefGoogle Scholar
Linton, O.(2007), ‘Semi- and Nonparametric ARCH/GARCH Modelling’, in Andersen, T. G., Davis, R. A., Kreiss, J. P. and Mikosch, T. (eds.), Handbook of Financial Time Series, New York: Springer.Google Scholar
Linton, O., and Perron, B. (2003), ‘The Shape of the Risk Premium: Evidence from a Semiparametric Generalized Autoregressive Conditional Heteroscedasticity Model’, Journal of Business and Economic Statistics, 21, 354–67.CrossRefGoogle Scholar
Liu, C. Y., and He, J. (1991), ‘A Variance Ratio Test of Random Walks in Foreign Exchange Rates’, Journal of Finance, 46, 773–85.CrossRefGoogle Scholar
Liu, M. (2000), ‘Modeling Long Memory in Stock Market Volatility’, Journal of Econometrics, 99, 139–71.CrossRefGoogle Scholar
Ljung, G. M., and Box, G. E. P. (1978), ‘On a Measure of Lack of Fit in Time Series Models’, Biometrika, 65, 297–303.CrossRefGoogle Scholar
Lo, A. W. (1991), ‘Long-term Memory in Stock Market Prices’, Econometrica, 59, 1279–313.CrossRefGoogle Scholar
Lo, A. W., and MacKinlay, A. C. (1988), ‘Stock Prices do not Follow Random Walks: Evidence from a Simple Specification Test’, Review of Financial Studies, 1, 41–66.CrossRefGoogle Scholar
Lo, A. W., and MacKinlay, A. C.(1989), ‘The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation’, Journal of Econometrics, 40, 203–38.CrossRefGoogle Scholar
Lobato, I. N., Nankervis, J. C., and Savin, N. E. (2001), ‘Testing for Autocorrelation Using a Modified Box–Pierce Q Test’, International Economic Review, 42, 187–205.CrossRefGoogle Scholar
Lobato, I. N., Nankervis, J. C., and Savin, N. E.(2002), ‘Testing for Zero Autocorrelation in the Presence of Statistical Dependence’, Econometric Theory, 18, 730–43.CrossRefGoogle Scholar
Loretan, M., and Phillips, P. C. B. (1994), ‘Testing the Covariance Stationarity of Heavy-tailed Time Series: An Overview of the Theory with Applications to Several Financial Datasets’, Journal of Empirical Finance, 1, 211–48.CrossRefGoogle Scholar
Lucas, R. E. (1978), ‘Asset Prices in an Exchange Economy’, Econometrica, 46, 1429–46.CrossRefGoogle Scholar
Luger, R. (2003), ‘Exact Non-parametric Tests for a Random Walk with Unknown Drift under Conditional Heteroskedasticity’, Journal of Econometrics, 115, 259–76.CrossRefGoogle Scholar
Lumsdaine, R. L., and Ng, S. (1999), ‘Testing for ARCH in the Presence of a Possibly Mispecified Conditional Mean’, Journal of Econometrics, 93, 257–79.CrossRefGoogle Scholar
Lundbergh, S., and Teräsvirta, T. (2002), ‘Evaluating GARCH Models’, Journal of Econometrics, 110, 417–35.CrossRefGoogle Scholar
Lütkepohl, H. (1985), ‘Comparison of Criteria for Estimating the Order of a Vector Autoregresive Process’, Journal of Time Series Analysis, 6, 35–52.CrossRefGoogle Scholar
Lütkepohl, H.(1991), Introduction to Multiple Time Series Analysis, Berlin: Springer-Verlag.CrossRefGoogle Scholar
MacKinlay, A. C. (1987), ‘On Mulivariate Tests of the CAPM’, Journal of Financial Economics, 18, 431–71.CrossRefGoogle Scholar
MacKinnon, J. G. (1991), ‘Critical Values for Cointegration Tests’, in Engle, R. F. and Granger, C. W. J. (eds.), Long-run Economic Relationships, Oxford: Oxford University Press, 267–76.Google Scholar
MacKinnon, J. G.(1996), ‘Numerical Distribution Functions for Unit Root and Cointegration Tests’, Journal of Applied Econometrics, 11, 601–18.3.0.CO;2-T>CrossRefGoogle Scholar
McCabe, B. P. M., and Tremayne, A. R. (1995), ‘Testing a Time Series for Difference Stationarity’, Annals of Statistics, 23, 1015–28.CrossRefGoogle Scholar
McCulloch, J. H. (1996), ‘Financial Applications of Stable Distributions’, in Maddala, G. S. and Rao, C. R. (eds.), Handbook of Statistics, Vol. XIV: Statistical Methods in Finance, Amsterdam: Elsevier Science, 393–425.Google Scholar
McCulloch, J. H.(1997), ‘Measuring Tail Thickness to Estimate the Stable Index α: A Critique’, Journal of Business and Economic Statistics, 15, 74–81.Google Scholar
McDonald, J. B. (1996), ‘Probability Distributions for Financial Models’, in Maddala, G. S. and Rao, C. R. (eds.), Handbook of Statistics, Vol. XIV: Statistical Methods in Finance, Amsterdam: Elsevier Science, 427–61.Google Scholar
McKenzie, E. (1988), ‘A Note on Using the Integrated Form of ARIMA Forecasts’, International Journal of Forecasting, 4, 117–24.CrossRefGoogle Scholar
McLeod, A. J., and Li, W. K. (1983), ‘Diagnostic Checking ARMA Time Series Models using Squared-residual Correlations’, Journal of Time Series Analysis, 4, 269–73.CrossRefGoogle Scholar
Maddala, G. S., and Kim, I.-M. (1998), Unit Roots, Cointegration, and Structural Change, Cambridge: Cambridge University Press.Google Scholar
Malkiel, B. G. (2003). A Random Walk down Wall Street, New York: Norton.Google Scholar
Mandelbrot, B. B. (1963a), ‘New Methods in Statistical Economics’, Journal of Political Economy, 71, 421–40.CrossRefGoogle Scholar
Mandelbrot, B. B.(1963b), ‘The Variation of Certain Speculative Prices’, Journal of Business, 36, 394–419.CrossRefGoogle Scholar
Mandelbrot, B. B.(1966), ‘Forecasts of Future Prices, Unbiased Markets and “Martingale” Models’, Journal of Business, 39, 242–55.CrossRefGoogle Scholar
Mandelbrot, B. B.(1969), ‘Long-run Linearity, Locally Gaussian Process, H-Spectra, and Infinite Variances’, International Economic Review, 10, 82–111.CrossRefGoogle Scholar
Mandelbrot, B. B.(1972), ‘Statistical Methodology for Nonperiodic Cycles: From the Covariance to R/S Analysis’, Annals of Economic and Social Measurement, 1/3, 259–90.Google Scholar
Mandelbrot, B. B.(1989), ‘Louis Bachelier’, in Eatwell, J., Milgate, M. and Newman, P. (eds.), The New Palgrave: Finance, London: Macmillan, 86–8.Google Scholar
Mandelbrot, B. B., and Wallis, J. R. (1969), ‘Some Long-run Properties of Geophysical Records’, Water Resources Research, 5, 321–40.CrossRefGoogle Scholar
Mantegna, R. N., and Stanley, H. E. (1994), ‘Stochastic Process with Ultraslow Convergence to a Gaussian: The Truncated Lévy Flight’, Physical Review Letters, 73, 2946–9.CrossRefGoogle ScholarPubMed
Mantegna, R. N., and Stanley, H. E.(1995), ‘Scaling Behaviour in the Dynamics of an Economic Index’, Nature, 376, 46–9.CrossRefGoogle Scholar
Maravall, A. (1983), ‘An Application of Nonlinear Time Series Forecasting’, Journal of Business and Economic Statistics, 3, 350–5.Google Scholar
Markellos, R. N., and Mills, T. C. (1998), ‘Complexity Reduction for Co-trending Variables’, Journal of Computational Intelligence in Finance, 6, 6–13.Google Scholar
Markellos, R. N., and Mills, T. C.(2001), ‘Unit Roots in the CAPM?’, Applied Economics Letters, 8, 499–502.CrossRefGoogle Scholar
Markellos, R. N., and Mills, T. C.(2003), ‘Asset Pricing Dynamics’, European Journal of Finance, 9, 533–56.CrossRefGoogle Scholar
Markellos, R. N., Mills, T. C., and Siriopoulos, C. (2003), ‘Intradaily Behavior of Listed and Unlisted Security Basket Indices in the Emerging Greek Stock Market’, Managerial Finance, 29, 29–54.CrossRefGoogle Scholar
Marsh, T. A., and Merton, R. C. (1987), ‘Dividend Behaviour for the Aggregate Stock Market’, Journal of Business, 60, 1–40.CrossRefGoogle Scholar
Matacz, A. (2000), ‘Financial Modeling and Option Theory with the Truncated Lévy Process’, International Journal of Theoretical and Applied Finance, 3, 143–60.CrossRefGoogle Scholar
Meddahi, N., and Renault, E. (2004), ‘Temporal Aggregation of Volatility Models’, Journal of Econometrics, 119, 355–79.CrossRefGoogle Scholar
Meese, R. A. (1986), ‘Testing for Bubbles in Exchange Markets: A Case of Sparkling Rates?’, Journal of Political Economy, 94, 345–73.CrossRefGoogle Scholar
Meese, R. A., and Singleton, K. J. (1982), ‘On Unit Roots and the Empirical Modelling of Exchange Rates’, Journal of Finance, 37, 1029–35.CrossRefGoogle Scholar
Merton, R. C. (1973), ‘An Intertemporal Capital Asset Pricing Model’, Econometrica, 41, 867–87.CrossRefGoogle Scholar
Merton, R. C.(1976), ‘Option Prices when the Underlying Stock Returns Are Discontinuous’, Journal of Financial Economics, 3, 125–44.CrossRefGoogle Scholar
Merton, R. C.(1980), ‘On Estimating the Expected Return on the Market: An Exploratory Investigation’, Journal of Financial Economics, 8, 323–61.CrossRefGoogle Scholar
Meyer, R., and Yu, J. (2000), ‘BUGS for a Bayesian Analysis of Stochastic Volatility Models’, Econometrics Journal, 3, 198–215.CrossRefGoogle Scholar
Michael, P., Nobay, A. R., and Peel, D. A. (1997), ‘Transactions Costs and Nonlinear Adjustment in Real Exchange Rates: An Empirical Investigation’, Journal of Political Economy, 105, 862–79.CrossRefGoogle Scholar
Mikosch, T., and Starica, C. (2004), ‘Changes of Structure in Financial Time Series and the GARCH Model’, Revstat Statistical Journal, 2, 41–73.Google Scholar
Milhøj, A. (1985), ‘The Moment Structure of ARCH Processes’, Scandinavian Journal of Statistics, 12, 281–92.Google Scholar
Miller, S. M. (1988), ‘The Beveridge–Nelson Decomposition of Economic Time Series: Another Economical Computational Method’, Journal of Monetary Economics, 21, 141–2.CrossRefGoogle Scholar
Mills, T. C. (1990), Time Series Techniques for Economists, Cambridge: Cambridge University Press.Google Scholar
Mills, T. C.(1991a), ‘Equity Prices, Dividends and Gilt Yields in the UK: Cointegration, Error Correction and “Confidence” ’, Scottish Journal of Political Economy, 38, 242–55.CrossRefGoogle Scholar
Mills, T. C.(1991b), ‘The Term Structure of UK Interest Rates: Tests of the Expectations Hypothesis’, Applied Economics, 23, 599–606.CrossRefGoogle Scholar
Mills, T. C.(1993), ‘Testing the Present Value Model of Equity Prices for the UK Stock Market’, Journal of Business Finance and Accounting, 20, 803–13.CrossRefGoogle Scholar
Mills, T. C.(1995), ‘Modelling Skewness and Kurtosis in the London Stock Exchange FT-SE Index Return Distributions’, The Statistician, 44, 323–32.CrossRefGoogle Scholar
Mills, T. C.(1996a), ‘Non-linear Forecasting of Financial Time Series: An Overview and Some New Models’, Journal of Forecasting, 15, 127–35.3.0.CO;2-1>CrossRefGoogle Scholar
Mills, T. C.(1996b), ‘The Econometrics of the “Market Model”: Cointegration, Error Correction and Exogeneity’, International Journal of Finance and Economics, 1, 275–86.3.0.CO;2-7>CrossRefGoogle Scholar
Mills, T. C.(1997a), ‘Stylized Facts on the Temporal and Distributional Properties of Daily FT-SE Returns’, Applied Financial Economics, 7, 599–604.CrossRefGoogle Scholar
Mills, T. C.(1997b), ‘Technical Analysis and the London Stock Exchange: Testing Trading Rules Using the FT30’, International Journal of Finance and Economics, 2, 319–31.3.0.CO;2-6>CrossRefGoogle Scholar
Mills, T. C.(1998), ‘Recent Developments in Modelling Nonstationary Vector Auto-regressions’, Journal of Economic Surveys, 12, 279–312.CrossRefGoogle Scholar
Mills, T. C.(2003), Modelling Trends and Cycles in Economic Time Series, Basingstoke: Palgrave Macmillan.CrossRefGoogle Scholar
Mills, T. C., and Coutts, J. A. (1995), ‘Anomalies and Calendar Effects in the New FT-SE Indices’, European Journal of Finance, 1, 79–93.CrossRefGoogle Scholar
Mills, T. C., and Coutts, J. A.(1996), ‘Misspecification and Robust Estimation of the Market Model: Estimating Betas for the FT-SE Industry Baskets’, European Journal of Finance, 2, 319–31.CrossRefGoogle Scholar
Mills, T. C., and Stephenson, M. J. (1986), ‘An Empirical Analysis of the UK Treasury Bill Market’, Applied Economics, 17, 689–703.CrossRefGoogle Scholar
Mina, J., and Xiao, J. Y. (2001), Return to RiskMetrics: The Evolution of a Standard, New York: RiskMetrics Group.Google Scholar
Mittnik, S., and Rachev, S. T. (1993a), ‘Modeling Asset Returns with Alternative Stable Distributions’, Econometric Reviews, 12, 261–330.CrossRefGoogle Scholar
Mittnik, S., and Rachev, S. T.(1993b), ‘Reply to Comments on “Modeling Asset Returns with Alternative Stable Distributions” and Some Extensions’, Econometric Reviews, 12, 347–89.CrossRefGoogle Scholar
Modha, D. S., and Fainman, Y. (1994), ‘A Learning Law for Density Estimation’, IEEE Transactions on Neural Networks, 5, 519–23.CrossRefGoogle ScholarPubMed
Morgan, M. S. (1990), The History of Econometric Ideas, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Muth, J. F. (1960), ‘Optimal Properties of Exponentially Weighted Forecasts’, Journal of the American Statistical Association, 55, 299–305.CrossRefGoogle Scholar
Nawrocki, D. N. (1999), ‘A Brief History of Downside Risk Measures’, Journal of Investing, 8, 9–25.CrossRefGoogle Scholar
Nelson, C. R., and Plosser, C. I. (1982), ‘Trends and Random Walks in Macroeconomic Time Series’, Journal of Monetary Economics, 10, 139–62.CrossRefGoogle Scholar
Nelson, C. R., and Schwert, G. W. (1977), ‘Short-term Interest Rates as Predictors of Inflation: On Testing the Hypothesis that the Real Rate of Interest is Constant’, American Economic Review, 67, 478–86.Google Scholar
Nelson, D. B. (1990a), ‘Stationarity and Persistence in the GARCH(1,1) Model’, Econometric Theory, 6, 318–34.CrossRefGoogle Scholar
Nelson, D. B.(1990b), ‘ARCH Models as Diffusion Approximations’, Journal of Econometrics, 45, 7–38.CrossRefGoogle Scholar
Nelson, D. B.(1991), ‘Conditional Heteroskedasticity in Asset Returns’, Econometrica, 59, 347–70.CrossRefGoogle Scholar
Nelson, D. B., and Cao, C. Q. (1992), ‘Inequality Constraints in Univariate GARCH Models’, Journal of Business and Economic Statistics, 10, 229–35.Google Scholar
Nelson, D. B., and Foster, D. P. (1994), ‘Asymptotic Filtering for Univariate GARCH Models’, Econometrica, 62, 1–41.CrossRefGoogle Scholar
Nelson, S. A. (1902), The ABC of Stock Speculation, New York: S. A. Nelson.Google Scholar
Newbold, P. (1990), ‘Precise and Efficient Computation of the Beveridge–Nelson Decomposition of Economic Time Series’, Journal of Monetary Economics, 26, 453–7.CrossRefGoogle Scholar
Newbold, P., and Agiakloglou, C. (1993), ‘Bias in the Sample Autocorrelations of Fractional White Noise’, Biometrika, 80, 698–702.CrossRefGoogle Scholar
Newey, W. K. (1985), ‘Generalized Method of Moment Specification Testing’, Journal of Econometrics, 29, 229–56.CrossRefGoogle Scholar
Newey, W. K., and West, K. D. (1987), ‘A Simple Positive Semidefinite, Heteroskedasticity Consistent Covariance Matrix’, Econometrica, 55, 703–8.CrossRefGoogle Scholar
Ng, S., and Perron, P. (1995), ‘Unit Root Tests in ARMA Models with Data Dependent Methods for Good Size and Power’, Journal of the American Statistical Association, 90, 268–81.CrossRefGoogle Scholar
Ng, S., and Perron, P.(2001), ‘Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power’, Econometrica, 69, 1519–54.CrossRefGoogle Scholar
Nickell, S. (1985), ‘Error Correction, Partial Adjustment and All That: An Expository Note’, Oxford Bulletin of Economics and Statistics, 47, 119–29.CrossRefGoogle Scholar
Nychka, D. W., Stephen, E., Gallant, A. R., and McCaffrey, D. F. (1992), ‘Finding Chaos in Noisy Systems’, Journal of the Royal Statistical Society, Series B, 54, 399–426.Google Scholar
Osborne, M. M. (1959), ‘Brownian Motion in the Stock Market’, Operations Research, 7, 145–73.CrossRefGoogle Scholar
Pagan, A. R. (1996), ‘The Econometrics of Financial Markets’, Journal of Empirical Finance, 3, 15–102.CrossRefGoogle Scholar
Pagan, A. R., and Schwert, G. W. (1990), ‘Testing for Covariance Stationarity in Stock Market Data’, Economics Letters, 33, 165–70.CrossRefGoogle Scholar
Park, J. Y. (1990), ‘Testing for Unit Roots and Cointegration by Variable Addition’, in Rhodes, G. F. and Fomby, T. B. (eds.), Advances in Econometrics, Vol. VIII, Greenwich, CT: JAI Press, 107–33.Google Scholar
Park, J. Y., and Phillips, P. C. B. (1988), ‘Statistical Inference in Regressions with Cointegrated Processes: Part I’, Econometric Theory, 4, 468–97.CrossRefGoogle Scholar
Park, J. Y., and Phillips, P. C. B.(1999) ‘Asymptotics for Nonlinear Transformations of Integrated Time Series’, Econometric Theory, 15, 269–98.CrossRefGoogle Scholar
Park, J. Y., and Phillips, P. C. B.(2001), ‘Nonlinear Regressions with Integrated Time Series’, Econometrica, 69, 117–61.CrossRefGoogle Scholar
Pearson, K., and Rayleigh, Lord (1905), ‘The Problem of the Random Walk’, Nature, 72, 294, 318, 342.CrossRefGoogle Scholar
Pena, D., and Rodriguez, J. (2005), ‘Detecting Nonlinearity in Time Series by Model Selection Criteria’, International Journal of Forecasting, 21, 731–48.CrossRefGoogle Scholar
Perron, P. (1988), ‘Trends and Random Walks in Macroeconomic Time Series: Further Evidence from a New Approach’, Journal of Economic Dynamics and Control, 12, 297–332.CrossRefGoogle Scholar
Perron, P.(1989), ‘The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis’, Econometrica, 57, 1361–401.CrossRefGoogle Scholar
Perron, P.(1990), ‘Testing for a Unit Root in a Time Series with a Changing Mean’, Journal of Business and Economic Statistics, 8, 153–62.Google Scholar
Perron, P.(1991), ‘Test Consistency with Varying Sampling Frequency’, Econometric Theory, 7, 341–68.CrossRefGoogle Scholar
Perron, P.(1997), ‘Further Evidence on Breaking Trend Functions in Macroeconomic Variables’, Journal of Econometrics, 80, 355–85.CrossRefGoogle Scholar
Perron, P.(2006), ‘Dealing with Structural Breaks’, in Mills, T. C. and Patterson, K. (eds.), Palgrave Handbook of Econometrics, Vol. I: Econometric Theory, Basingstoke: Palgrave Macmillan, 278–352.Google Scholar
Perron, P., and Ng, S. (1996), ‘Useful Modifications to Some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties’, Review of Economic Studies, 63, 435–63.CrossRefGoogle Scholar
Perron, P., and Vogelsang, T. J. (1993), ‘A Note on the Asymptotic Distributions in the Additive Outlier Model with Breaks’, Revista de Econometrica, 8, 181–202.Google Scholar
Pesaran, M. H. (1997), ‘The Role of Economic Theory in Modelling the Long Run’, Economic Journal, 107, 178–91.CrossRefGoogle Scholar