204 research outputs found
Cointegration and Forward and Spot Exchange Rate Regressions
No full textIn this paper we investigate in detail the relationship between models of cointegration between the current spot exchange rate, st, and the current forward rate, ft, and models of cointegration between the future spot rate, st+1, and ft and the implications of this relationship for tests of the forward rate unbiasedness hypothesis (FRUH). We argue that simple models of cointegration between st and ft more easily capture the stylized facts of typical exchange rate data than simple models of cointegration between st+1 and ft and so serve as a natural starting point for the analysis of exchange rate behavior. We show that simple models of cointegration between st and ft imply rather complicated models of cointegration between st+1 and ft. As a result, standard methods are often not appropriate for modeling the cointegrated behavior of (st+1, ft)' and we show that the use of such methods can lead to erroneous inferences regarding the FRUH.cointegration, exchange rates, forward rate unbiasedness, weak exogeneity
State Space Modeling Using SsfPack in S+FinMetrics 3.0
No full textThis paper presents two illustrations of state space modeling in S-PLUS using the SsfPack 3.0 routines implemented in S+FinMetrics 3.0. The state space modeling functions in S+FinMetrics/SsfPack are extremely flexible and powerful and can be used for a wide variety of linear Gaussian state space models and for some non-linear and non-Gaussian state space models.
Modeling financial time series with S-plus
No full textThe field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics This is the first book to show the power of S-PLUS for the analysis of time series data It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department at the University of Washington, and is co-director of the nascent Professional Master's Program in Computational Finance He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T Buechel Award for Outstanding Teaching He is an associate editor of the Journal of Business and Economic Statistics and Studies in Nonlinear Dynamics and Econometrics He has published papers in the leading econometrics journals, including Econometrica, Econometric Theory, the Journal of Business and Economic Statistics, Journal of Econometrics, and the Review of Economics and Statistics Jiahui Wang is a Research Scientist at Insightful Corporation He received a PhD in Economics from the university of Washington in 1997 He has published in leading econometrics journals such as Econometrica and Journal of Business and Economic Statistics, and is the Principal Investigator of National Science Foundation SBIR grants In 2002 Dr Wang was selected as one of the "2000 Outstanding Scholars of the 21st Century" by International Biographical Centr
A new method of projection-based inference in GMM with weakly identified nuisance parameters
No full textProjection-based methods of inference on subsets of parameters are useful for obtaining tests that do not over-reject the true parameter values. However, they are also often criticized for being conservative. We show that the usual method of pro jection can be modifed to obtain tests that are as powerful as the conventional tests for subsets of parameters. Like the usual projection-based methods, one can always put an upper bound to the rate at which the new method over-rejects the true value of the parameters of interest. The new method is described in the context of GMM with possibly weakly identifed parameters.
VAR estimation and forecasting when data are subject to revision
No full textConventional VAR estimation and forecasting ignores the fact that economic data are often subject to revision many months or years after their initial release. This paper shows how VAR analysis can be modified to account for such revisions. The proposed approach assumes that government statistical releases are efficient with a finite lag. It takes no stand on whether earlier revisions are “noise” or “news.” The technique is illustrated using data on employment and the unemployment rate, real GDP and the unemployment rate, and real GDP and the GDP/consumption ratio. In each case, the proposed procedure outperforms conventional VAR analysis and the more-restrictive methods for handling the data-revision problem that are found in the existing literature.
Long Memory and Structural Changes in the Forward Discount: An Empirical Investigation
No full textMany empirical studies find a negative correlation between the returns on the nominal spot exchange rate and the lagged forward discount. This forward discount anomaly implies that the current forward rate is a biased predictor of the future spot rate. A large number of studies in the existing literature try to explain this anomaly, and recent work has tried to explain the anomaly as a statistical artifact based on (1) the long memory behavior of the forward discount; or (2) the existence of structural breaks in the forward discount. In this paper, we evaluate the evidence for long memory and structural change in the forward discount. Our approach is as follows. First, we nonparametrically estimate the long memory parameter for a number of forward discount series without allowing for structural breaks. Second, we test for and estimate a multiple mean break model and then adjust for the structural breaks in the forward discount. Finally, we re-estimate the long memory parameter on the mean-break adjusted data. We show that allowing for structural breaks drastically reduces the persistence of the forward discount. However, after removing the breaks, we still find evidence of stationary long memory in all of the forward discount series. Our results have important implications for understanding the statistical properties of the forward discount, because we confirm not only the presence of long memory behavior in the forward discount but also the importance of structural breaks.Long Memory, Structural Changes, Forward Discount
Essays on return predictability and yield factors
No full textThesis (Ph.D.)--University of Washington, 2014This dissertation includes three chapters in which the first two are on return predictability and the third is on yield curve and yield factors. The abstract of each of them is as follows: 1), This paper proposes using capital gains instead of total returns in return predictability tests. Total return predictability can be inferred from capital gain predictability since total returns with dividends are highly correlated with returns based on capital gains only. An exact linear relationship exists among log dividend growth, log capital gain and log dividend price ratio. This exact linear relationship has similar implication as the Campbell-Shiller (1988) linear approximation but is more precise and easier for predictability tests. I verify the standard empirical findings on return predictability using capital gain predictability. Separation of price change and dividend change also leads to a new finding: shocks to dividend growth is shown to have significant positive correlation with shocks to dividend price ratio in the vector autoregressive regression (VAR) rather than close to zero as shown in previous literature. 2), This paper tests the return predictability of the cyclical and trend components in the log dividend price ratio. The log dividend ratio is found to have a near-unit root trend factor if the expectation of the future discount factor is highly persistent. We use Bayesian analysis and the Kalman filter to extract the strictly stationary and near-random-walk components in the log dividend price ratio. The extracted cyclical process can predict one-year ahead total returns during the post-war period and one-year ahead dividend growth rates during the pre-war and war period with notable R^2. We also demonstrate a reverse of predictability: returns become more predictable while dividend growth rates become more unpredictable. 3), This paper examines the fourth principal component of the yields matrix, which is largely ignored in macro-finance forecasting applications, in the context of predicting excess bond returns. Using yields data from the Fama-Bliss and the Federal Reserve, we present the significant in-sample and out-of-sample predictive power of models including the fourth yield factor. Additionally, the "return-forecasting factor" in Cochrane and Piazzesi (2005) is shown to be a restricted linear combination of all yield factors and to be highly correlated with the second and fourth factors. We interpret the fourth yield factor as a factor representing "S-shape" (the shape of a sigmoid curve) and demonstrate the connection between the S-shape factor and the yield curve
The Relationship between the Beveridge-Nelson Decomposition andUnobserved Component Models with Correlated Shocks
No full textMany researchers believe that the Beveridge-Nelson decomposition leads to permanent and transitory components whose shocks are perfectly negatively correlated. Indeed, some even consider it to be a property of the decomposition. We demonstrate that the Beveridge-Nelson decomposition does not provide definitive information about the correlation between permanent and transitory shocks in an unobserved components model. Given an ARIMA model describing the evolution of U.S. real GDP, we show that there are many state space representations that generate the Beveridge-Nelson decomposition. These include unobserved components models with perfectly correlated shocks and partially correlated shocks. In our applications, the only knowledge we have about the correlation is that it lies in a restricted interval that does not include zero. Although the filtered estimates of the trend and cycle are identical for models with different correlations, the observationally equivalent unobserved components models produce different smoothed estimates.
- …
