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9054 results in Finance and accountancy

Page 213 of 453

Rethinking Housing Bubbles
  • The Role of Household and Bank Balance Sheets in Modeling Economic Cycles
  • Steven D. Gjerstad, Vernon L. Smith
  • Published online:
    05 June 2014
    Print publication:
    12 May 2014
  • In this highly original piece of work, Steven D. Gjerstad and Nobel Laureate Vernon L. Smith analyze the role of housing and its associated mortgage financing as a key element of economic cycles. The authors combine data from both laboratory and real markets to provide insight into the bubble propensity of real-world economic actors and use novel historical analysis on the Great Recession, the Great Depression, and all of the post-World War II recessions to establish the critical roles of housing, private-capital investment, and household and private institutional balance sheets in economic cycles. They develop a model that incorporates household balance sheets and bank balance sheets and offers insights based on this analysis concerning policy going forward, effectively changing the way economists think about economic cycles.

  • 7 - The Bubble Bursts

  • Steven D. Gjerstad, Chapman University, California, Vernon L. Smith, Chapman University, California
  • Book:
    Rethinking Housing Bubbles
    Published online:
    05 June 2014
    Print publication:
    12 May 2014, pp 173-202

  • Summary

    The parties to these kinds of contract are largely sophisticated financial institutions that would appear to be eminently capable of protecting themselves from fraud and counterparty insolvencies.

    – Lawrence Summers, Congressional testimony, July 30, 1998

    With respect to their safety, derivatives, for the most part, are traded among very sophisticated financial institutions and individuals who have considerable incentive to understand them and to use them properly. The Federal Reserve’s responsibility is to make sure that the institutions it regulates have good systems and good procedures for ensuring that their derivatives portfolios are well managed and do not create excessive risk in their institutions.

    – Ben Bernanke, Senate confirmation hearings, November 15, 2005

    We were on the March 22 call with Fitch regarding the subprime securitization market’s difficulties. My associate asked several questions. “What are the key drivers of your rating model?” They responded, FICO scores and home price appreciation (HPA) of low single digit (LSD) or mid single digit (MSD), as HPA has been for the past fifty years. My associate then asked, “What if HPA was flat for an extended period of time?” They responded that their model would start to break down. He then asked, “What if HPA were to decline 1 percent to 2 percent for an extended period of time?” He then asked, “With 2 percent depreciation, how far up the rating’s scale would it harm?” They responded that it might go as high as the AA or AAA tranches.

    – Robert Rodriguez, June 28, 2007

    Every time there’s been a fire, these guys [derivatives traders] have been around it.

    – Nicholas Brady (Lowenstein, 2000, p. 105)

    The collapse of the housing finance market is in many ways the most fascinating – and certainly the most painful – part of the story of the bubble’s unraveling. In previous chapters, we present a brief primer on the private mortgage market as it developed following the popular initiatives of the Clinton and Bush administrations to expand homeownership and eliminate the capital gains tax (capped at $500,000 for each sale) on homes, the policy actions of government sponsored enterprises (GSE), the continuing large inflows of foreign capital, and the unprecedented (at the time) monetary ease of 2002–2004. The focus now turns to the private housing-mortgage market institutions that served to amplify the housing price run-up, particularly in the period after 2001.

  • 6 - What May Have Triggered or Sustained the Housing Bubble (1997–2006)?

  • Steven D. Gjerstad, Chapman University, California, Vernon L. Smith, Chapman University, California
  • Book:
    Rethinking Housing Bubbles
    Published online:
    05 June 2014
    Print publication:
    12 May 2014, pp 146-172

  • Summary

    As home prices rose and hunger for high-yield investments grew, Sadek [founder of Quick Loan Funding] found his niche pushing mortgages to borrowers with poor credit. Such subprime home loans grew to $600 billion, or 21 percent, of all U.S. mortgages last year from $160 billion, or 7 percent, in 2001, according to Inside Mortgage Finance, an industry newsletter. Banks drove that growth because they could bundle subprime loans into securities…“I never made a loan that Wall Street wouldn’t buy,” Sadek says. He worked hard to build the business, he says, and the company did nothing illegal…with the support of Citigroup, which funded the loans, he pioneered lending to homebuyers with credit scores of less than 450.

    Citigroup spokesman Stephen Cohen said the bank doesn’t comment on its relationships with clients.

    “We made most of our money from selling loans to banks,” Sadek says…like many subprime companies, [he] specialized in…thirty-year mortgages that start with lower “teaser” interest rates and ratchet higher after two years.

    A key selling point was the 50 percent rise in home prices nationally from 2001 to 2006.

    Bob Ivry, Bloomberg News, December 18, 2007

    Since late 1997, more than $2 billion worth of community reinvestment loans have been packaged and marketed into securities…Every dollar taken off the originator’s books through securitization is a dollar available for new loans…securitization reflects a growing confidence among secondary market investors – many of whom, after all, are under no CRA obligation of their own – in the quality of these loans. I think that it proves we’re on the right track.

    John D. Hawke, Jr.; Comptroller of the Currency; May 5, 1999

  • 10 - Learning from Foreign Economic Crises

  • Steven D. Gjerstad, Chapman University, California, Vernon L. Smith, Chapman University, California
  • Book:
    Rethinking Housing Bubbles
    Published online:
    05 June 2014
    Print publication:
    12 May 2014, pp 251-267

  • Summary

    So, any interesting model must be a dynamic stochastic general equilibrium [DSGE] model. From this perspective, there is no other game in town. …If you have an interesting and a coherent story to tell, you can do so within a DSGE model. If you cannot, it probably is incoherent.

    … [T]he models are not well suited to analyze extremely rare events.

    – V. V. Chari, Testimony before the Committee on Science and Technology, U.S. House of Representatives, July 20, 2010

    All of the papers employ simple applied dynamic general equilibrium models … to decompose changes in output into three portions: one due to changes in inputs of labor, another due to changes in inputs of capital, and the third due to the changes in efficiency with which these factors are used.

    –Timothy J. Kehoe and Edward C. Prescott, from the introduction to Great Depressions of the Twentieth Century

    Introduction and Overview

    Our overriding objective in this book has been to show that unusual credit flows frequently produce asset bubbles; when those assets are highly leveraged, immobile, and illiquid, borrowers and the financial system often suffer severe balance sheet deterioration in a collapse. Moreover, the economy is left saturated with an excess of produced assets, so that output is reduced. We have described the Great Recession as the result of a balance sheet crisis. In this chapter, we examine several even more severe downturns and show that all resulted from a collapse in the production of fixed assets. The opening quotes for this chapter argue for the view that the source of severe economic downturns lies in reductions to inputs of labor and capital and a reduction in the efficiency of these factors of production. Although this view is not “the only game in town,” it is arguably the dominant view in macroeconomics. In this book, we have sought to present evidence that supports an alternative view. Our argument is that when a bubble has pushed asset prices above fundamental value, production of those assets grows rapidly, but when asset prices become noticeably disconnected from fundamentals, financing for purchases of those assets collapses, asset prices collapse, and their production collapses too. When that happens, the economic downturn can be severe. The economic collapse does have noticeable effects on inputs of labor and capital and their efficiency, but changes to those factors are not causal.

  • 2 - Goods and Services Markets versus Asset Markets

  • Steven D. Gjerstad, Chapman University, California, Vernon L. Smith, Chapman University, California
  • Book:
    Rethinking Housing Bubbles
    Published online:
    05 June 2014
    Print publication:
    12 May 2014, pp 20-48

  • Summary

    Cassano agreed to meet with all the big Wall Street firms and discuss the logic of their deals – to investigate how a bunch of shaky loans could be transformed into AAA-rated bonds. Together with [Eugene] Park and a few others, Cassano set out on a series of meetings with Morgan Stanley, Goldman Sachs, and the rest – all of whom argued how unlikely it was for housing prices to fall all at once. “They all said the same thing,” says one of the traders present. “They’d go back to historical real-estate prices over 60 years and say they had never fallen all at once.”

    – Michael Lewis, Vanity Fair, July 2009

    It’s a pretty unlikely possibility. We’ve never had a decline in house prices on a nationwide basis. So, what I think is more likely is that house prices will slow, maybe stabilize, might slow consumption spending a bit. I don’t think it’s gonna drive the economy too far from its full employment path, though…

    – Ben Bernanke, CNBC Interview, July 1, 2005

    Two Types of Markets: The Good and the Sometimes Ugly

    This chapter summarizes findings from two distinct types of experimental markets that are directly relevant to understanding the sources of both stability and instability in the macroeconomy: (1) the class of nondurable consumed goods and services that constitute about 75 percent of U.S. private expenditures (i.e., GDP minus government expenditures); and (2) asset markets, particularly those in which the items traded have long lives and whose market value, therefore, may be importantly influenced by the future price expectations of the participants (prominent examples include houses, securities, and commercial real estate). The parallels between the laboratory and the economy in each of these two cases suggest underlying modes and principles of human behavior that are similar – conditional on the differing characteristics of the items being traded in these two broad categories of economic activity.

  • A yield-macro model for actuarial use in the United Kingdom

  • Şule Şahin, Andrew J.G. Cairns, Torsten Kleinow, A. David Wilkie
  • Journal:
    Annals of Actuarial Science / Volume 8 / Issue 2 / September 2014
    Published online by Cambridge University Press:
    07 May 2014, pp. 320-350

  • We construct yield curve models for the UK nominal, real and implied inflation spot rates considering the linkage between their term structures and some macroeconomic variables, in particular, realised inflation and real GDP growth. The paper extends the benchmark “yield-only” model proposed by Şahin et al. (2014) by exploring the bidirectional relations between the yield curve factors and the macroeconomic variables and proposes a “yield-macro” model. Although a simple autoregressive order one process fits the yield curve factors quite well the insertion of some macroeconomic variables such as realised inflation and real GDP growth improves the models significantly. We also model macroeconomic variables that take the term structures into account and compare the yield-macro model with Wilkie’s model both philosophically and empirically.

  • 6 - Univariate time series modelling and forecasting

  • Chris Brooks, University of Reading
  • Book:
    Introductory Econometrics for Finance
    Published online:
    09 August 2018
    Print publication:
    02 May 2014, pp 251-304

  • Summary

    Learning outcomes

    In this chapter, you will learn how to

  • • Explain the defining characteristics of various types of stochastic processes

  • • Identify the appropriate time series model for a given data series

  • • Produce forecasts for autoregressive moving average (ARMA) and exponential smoothing models

  • • Evaluate the accuracy of predictions using various metrics

  • • Estimate time series models and produce forecasts from them in EViews

    • Introduction

    Univariate time series models are a class of specifications where one attempts to model and to predict financial variables using only information contained in their own past values and possibly current and past values of an error term. This practice can be contrasted with structural models, which are multivariate in nature, and attempt to explain changes in a variable by reference to the movements in the current or past values of other (explanatory) variables. Time series models are usually a-theoretical, implying that their construction and use is not based upon any underlying theoretical model of the behaviour of a variable. Instead, time series models are an attempt to capture empirically relevant features of the observed data that may have arisen from a variety of different (but unspecified) structural models. An important class of time series models is the family of autoregressive integrated moving average (ARIMA) models, usually associated with Box and Jenkins (1976). Time series models may be useful when a structural model is inappropriate. For example, suppose that there is some variable yt whose movements a researcher wishes to explain.

  • 2 - Mathematical and statistical foundations

  • Chris Brooks, University of Reading
  • Book:
    Introductory Econometrics for Finance
    Published online:
    09 August 2018
    Print publication:
    02 May 2014, pp 28-74

  • Summary

    Learning outcomes

    In this chapter, you will learn how to

  • • Work with powers, exponents and logarithms

  • • Use sigma (Σ) and pi (Π) notation

  • • Apply simple rules to differentiate functions

  • • Work with matrices

  • • Calculate the trace, inverse and eigenvalues of a matrix

  • • Construct minimum variance and mean-variance efficient portfolios

  • • Compute summary statistics for a data series

  • • Manipulate expressions using the expectations, variance and covariance operators

    • This chapter covers the mathematical and statistical building blocks that are essential for a good understanding of the rest of the book. Those with some prior background in algebra and introductory statistics may skip this chapter without loss of continuity, but hopefully the material will also constitute a useful refresher for those who have studied mathematics but a long time ago!

    Functions

    2.1.1 Straight lines

    The ultimate objective of econometrics is usually to build a model, which may be thought of as a simplified version of the true relationship between two or more variables that can be described by a function. A function is simply a mapping or relationship between an input or set of inputs, and an output. We usually write that y, the output, is a function f of x, the input: y = f(x).

  • 13 - Simulation methods

  • Chris Brooks, University of Reading
  • Book:
    Introductory Econometrics for Finance
    Published online:
    09 August 2018
    Print publication:
    02 May 2014, pp 591-625

  • Summary

    Learning outcomes

    In this chapter, you will learn how to

  • • Design simulation frameworks to solve a variety of problems in finance

  • • Explain the difference between pure simulation and bootstrapping

  • • Describe the various techniques available for reducing Monte Carlo sampling variability

  • • Implement a simulation analysis in EViews

    • Motivations

    There are numerous situations, in finance and in econometrics, where the researcher has essentially no idea what is going to happen! To offer one illustration, in the context of complex financial risk measurement models for portfolios containing large numbers of assets whose movements are dependent on one another, it is not always clear what will be the effect of changing circumstances. For example, following full European Monetary Union (EMU) and the replacement of member currencies with the euro, it is widely believed that European financial markets have become more integrated, leading the correlation between movements in their equity markets to rise. What would be the effect on the properties of a portfolio containing equities of several European countries if correlations between the markets rose to 99%? Clearly, it is probably not possible to be able to answer such a question using actual historical data alone, since the event (a correlation of 99%) has not yet happened.

    The practice of econometrics is made difficult by the behaviour of series and inter-relationships between them that render model assumptions at best questionable.

  • 8 - Modelling long-run relationships in finance

  • Chris Brooks, University of Reading
  • Book:
    Introductory Econometrics for Finance
    Published online:
    09 August 2018
    Print publication:
    02 May 2014, pp 353-414

  • Summary

    Learning outcomes

    In this chapter, you will learn how to

  • • Highlight the problems that may occur if non-stationary data are used in their levels form

  • • Test for unit roots

  • • Examine whether systems of variables are cointegrated

  • • Estimate error correction and vector error correction models

  • • Explain the intuition behind Johansen's test for cointegration

  • • Describe how to test hypotheses in the Johansen framework

  • • Construct models for long-run relationships between variables in EViews

    • Stationarity and unit root testing

    8.1.1 Why are tests for non-stationarity necessary?

    There are several reasons why the concept of non-stationarity is important and why it is essential that variables that are non-stationary be treated differently from those that are stationary. Two definitions of non-stationarity were presented at the start of chapter 6. For the purpose of the analysis in this chapter, a stationary series can be defined as one with a constant mean, constant variance and constant autocovariances for each given lag. Therefore, the discussion in this chapter relates to the concept of weak stationarity. An examination of whether a series can be viewed as stationary or not is essential for the following reasons:

  • The stationarity or otherwise of a series can strongly influence its behaviour and properties. To offer one illustration, the word ‘shock’ is usually used to denote a change or an unexpected change in a variable or perhaps simply the value of the error term during a particular time period.[…]

Page 213 of 453