Structural exchange rate modeling has proven extremely difficult during the recent post-1973 float. The disappointment climaxed with the papers of Meese and Rogoff (1983a, 1983b), who showed that a "naive" random walk model distinctly dominated received theoretical models in terms of predictive performance for the major dollar spot rates. One purpose of this monograph is to seek the reasons for this failure by exploring the temporal behavior of seven major dollar exchange rates using nonstructural time-series methods. The Meese-Rogoff finding does not mean that exchange rates evolve as random walks; rather it simply means that the random walk is a better stochastic approximation than any of their other candidate models. In this monograph, we use optimal model specification techniques, including formal unit root tests which allow for trend, and find that all of the exchange rates studied do in fact evolve as random walks or random walks with drift (to a very close approximation). This result is consistent with efficient asset markets, and provides an explanation for the Meese-Rogoff results. Far more subtle forces are at work, however, which lead to interesting econometric problems and have implications for the measurement of exchange rate volatility and moment structure. It is shown that all exchange rates display substantial conditional heteroskedasticity. A particularly reasonable parameterization of this conditional heteroskedasticity, which captures the observed clustering of prediction error variances, is developed in Chapter 2.
|Publisher:||Springer Berlin Heidelberg|
|Series:||Lecture Notes in Economics and Mathematical Systems , #303|
|Edition description:||Softcover reprint of the original 1st ed. 1988|
|Product dimensions:||6.69(w) x 9.61(h) x 0.01(d)|
Table of Contents1 Introduction.- 2 Conditional Heteroskedasticity In Economic Time Series.- 2.1) Introduction and Summary.- 2.2) Autoregressive Conditionally Heteroskedastic Processes.- 2.2.1) Conditional Moment Structure.- 2.2.2) Unconditional Moment Structure.- 2.3) Temporal Aggregation of ARCH Processes.- 2.4) Estimation and Hypothesis Testing.- 2.5) The Asymptotic Distributions of Some Common Serial Correlation Test Statistics in the Presence of ARCH.- 2.5.1) Background.- 2.5.2) Correcting the Bartlett Standard Error Bands.- 2.5.3) On the Existence of EX4t.- 2.5.4) The Box-Pierce and Ljung-Box Statistics.- 2.5.5) Conclusions.- 2.6) Concluding Remarks.- 3 Weekly Univariate Nominal Exchange Rate Fluctuations.- 3.1) Introduction.- 3.2) Moving Sample Moments as Volatility Measures.- 3.3) The Data.- 3.4) Model Formulation.- 3.5) Empirical Results.- 3.6) Conclusions.- Appendix to Chapter 3 Testing For Unit Roots.- A3.1) The First-Order Case.- A3.2) Higher-Order Processes.- A3.3) General ARMA Representations.- 4 Monthly Univariate Nominal Exchange Rate Fluctuations.- 4.1) Introduction.- 4.2) Empirical Analysis.- 4.3) Comparison With Some Well-Known Results From Finance.- 4.4) Concluding Remarks.- 5 Real Exchange Rate Movements.- 5.1) Introduction.- 5.2) Forms of Purchasing Power Parity.- 5.3) The Relationship Between the Three Key Parity Conditions.- 5.3.a) Background.- 5.3.b) The Parity Conditions.- 5.3.c) Conclusions Regarding the Parity Conditions.- 5.4) On The Stochastic Behavior of Deviations From PPP.- 5.5) Empirical Analysis.- 5.6) Conclusions.- References.