The Economic Value of Predicting Stock Index Returns and Volatility.

In this paper, we analyze the economic value of predicting index returns as well as volatility. On the basis of fairly simple linear models, estimated recursively, we produce genuine out-of-sample forecasts for the return on the S\&P 500 index and its volatility. Using monthly data from 1954 to 1998, we test the statistical significance of return and volatility predictability and examine the economic value of a number of alternative trading strategies. We find strong evidence for market timing in both returns and volatility. Joint tests indicate no dependence between return and volatility timing, while it appears easier to forecast returns when volatility is high. For a mean-variance investor, this predictability is economically profitable, even if short sales are not allowed and transaction costs are quite large.

A Guide to Modern Econometrics

Models of Autoregressive Conditional Heteroscedasticity (ARCH) and their generalizations are widely used in ap-plied econometric research, especially for analysis of financial markets. We bring to our reader’s attention a consul-tation on this topic prepared from the book of Marno Verbeek “A Guide to Modern Econometrics” appearing soon in the Publishing House “Nauchnaya Kniga”ARCH; models

Evaluating Portfolio Value-at-Risk using Semi-Parametric GARCH Models

In this paper we examine the usefulness of multivariate semi-parametric GARCH models for portfolio selection under a Value-at-Risk (VaR) constraint. First, we specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly well, while parametric models in several cases have unacceptable failure rates. Interestingly, distributional assumptions appear to have a much larger impact on the performance of the VaR estimates than the particular parametric specification chosen for the GARCH equations. Finally, we examine the economic value of the multivariate GARCH models by determining optimal portfolios based on maximizing expected returns subject to a VaR constraint, over a period of 500 consecutive days. Again, the superiority and robustness of the semi-parametric model is confirmed.multivariate GARCH, semi-parametric estimation, Value-at-Risk, asset allocation.

Evaluating Portfolio Value-at-Risk using Semi-Parametric GARCH Models

In this paper we examine the usefulness of multivariate semi-parametric GARCH models for portfolio selection under a Value-at-Risk (VaR) constraint. First, we specify and estimate several alternative multivariate GARCH models for daily returns on the S\&P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly well, while parametric models in several cases have unacceptable failure rates. Interestingly, distributional assumptions appear to have a much larger impact on the performance of the VaR estimates than the particular parametric specification chosen for the GARCH equations. Finally, we examine the economic value of the multivariate GARCH models by determining optimal portfolios based on maximizing expected returns subject to a VaR constraint, over a period of 500 consecutive days. Again, the superiority and robustness of the semi-parametric model is confirmedmultivariate GARCH, semi-parametric estimation, Value-at-Risk, asset allocation

Alternative transformations to eliminate fixed effects

In a panel data model with fixed individual effects, a number of alternative transformations are available to eliminate these effects such that the slope parameters can be estimated from ordinary least squares on transformed data. In this note we show that each transformation leads to algebraically the same estimator if the transformed data are used efficiently (i.e. if GLS is applied). If OLS is used, however, differences may occur and the routinely computed variances, even after degrees of freedom correction, are incorrect. In addition, it may matter whether “redundant” observations are used or not

On the estimation of a fixed effects model with selectivity bias

In case of sample selectivity the maximum likelihood estimator of the parameters in a model with fixed effects will not be consistent when the number of time periods is small. In this paper, we present a transformation to eliminate the fixed individual effects and show that the corresponding marginal maximum likelihood estimator is computationally feasible and can be used to estimate the remaining parameters consistently even if number of time periods is finite

An Empirical Analysis of Affine Term Structure Models Using the Generalized Method of Moments

In this paper we formulate two tractable two-factor affine term structure models, imposing weak assumptions on the distributions of the measurement errors involved in the different yields. Exploiting the implied moment conditions, the models are estimated by the generalized method of moments using weekly term structure data for Germany, Japan, the UK and the USA. Despite our relatively weak assumptions, for each of these countries the overidentifying restrictions tests indicate that the estimated two-facotr models should be rejected. Apparently, the fact that many affine term structure models are rejected empirically, is unlikely to be due to the assumptions about the joint distribution of the measurement errors, but more likely to the lack of flexibility to explain certain aspects of the term structure.

Missing measurements in econometric models with no auxiliary relations

In this paper it is argued that maximizing the complete data (log) likelihood function with respect to the missing data and the unknown parameters will not improve the efficiency of the estimators but may affect consistency instead. If no auxiliary relations are available or additional assumptions are made, the maximum likelihood estimator based on the observed data is (asymptotically) the most efficient estimator

Pseudo Panels and Repeated Cross-Sections

In many countries there is a lack of genuine panel data where specific individuals or firms are followed over time. However, repeated cross-sectional surveys may be available, where a random sample is taken from the population at consecutive points in time. In this paper we discuss the identification and estimation of panel data models from repeated cross sections. In particular, attention will be paid to linear models with fixed individual effects, to models containing lagged dependent variables and to discrete choice models