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 Series’ Journal 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