Efficient Estimation of Firm-Specific Betas and its Benefits for Asset Pricing Tests and Portfolio Choice

We improve both the specification and estimation of firm-specific betas. Time variation in betas is modeled by combining a parametric specification based on economic theory with a non-parametric approach based on data-driven filters. We increase the precision of individual beta estimates by setting up a hierarchical Bayesian panel data model that imposes a common structure on parameters. We show that these accurate beta estimates lead to a large increase in the cross-sectional explanatory power of the conditional CAPM. Using the betas to forecast the covariance matrix of returns also results in a significant improvement in the out-of-sample performance of minimum variance portfolios.asset pricing; portfolio choice; time-varying betas; Bayesian econometrics; panel data

When Unit Roots Matter: Excess Volatility and Excess Smoothness of Long-Term Interest Rates

This paper re-examines volatility tests of the expectations model of the term structure of interest rates. In a multivariate vector autoregression (var) including interest rates, prices, money and output, we find that the long-term interest rate overreacts to all transitory shocks, and underreacts to all permanent shocks, irrespective of the number of unit roots and the cointegration structure in the system

An empirical application of stochastic volatility models

This paper studies the empirical performance of stochastic volatility models for twenty years of weekly exchange rate data for four major currencies. We concentrate on the effects of the distribution of the exchange rate innovations for both parameter estimates and for estimates of the latent volatility series. The density of the log of squared exchange rate innovations is modelled as a flexible mixture of normals. We use three different estimation techniques: quasi-maximum likelihood, simulated EM, and a Bayesian procedure. The estimated models are applied for pricing currency options. The major findings of the paper are that: (1) explicitly incorporating fat-tailed innovations increases the estimates of the persistence of volatility dynamics; (2) the estimation error of the volatility time series is very large; (3) this in turn causes standard errors on calculated option prices to be so large that these prices are rarely significantly different from a model with constant volatility

Long-term strategic asset allocation: An out-of-sample evaluation

We evaluate the out-of-sample performance of a long-term investor who follows an optimized dynamic trading strategy. Although the dynamic strategy is able to benefit from predictability out-of-sample, a short-term investor using a single-period market timing strategy would have realized an almost identical performance. The value of intertemporal hedge demands in strategic asset allocation appears negligible. The result is caused by the estimation error in predicting the predictors. A myopic investor only needs to predict one-period-ahead expected returns, but hedge demands also require accurate predictions of the predictor variables. To reduce the problem of errors in optimized portfolio weights, we consider Bayesian procedures. Myopic and dynamic portfolios are similarly affected by such modifications, and differences in performance become even smaller

A Bayesian analysis of the unit root in real exchange rates

We propose a posterior odds analysis of the hypothesis of a unit root in real exchange rates. From a Bayesian viewpoint the random walk hypothesis for real exchange rates is a posteriori as probable as a stationary AR(1) process for four out of eight time series investigated. The French franc/German mark is clearly stationary, while the Japanese yen/US dollar is most likely a random walk. In contrast, classical tests are unable to reject the unit root for any of these series

Direct Estimation of the risk neutral factor dynamics of affine term structure models

This paper proposes panel data tests of gaussian affine term structure models. Yield curve data for different moments in time are pooled with the factors treated as fixed effects. With fixed effects the time series properties of the price of risk can be ignored. Results of tests with us interest rate data show that the gaussian model is able to capture the cross sectional structure of yields as well as unrestricted factor loadings from principal components analysis. However, estimates of the mean reversion parameters in a 3-factor model differ significantly when the model is estimated from yield levels or forward differences, which is inconsistent with the gaussian model

Nonlinear dynamics in Nasdaq dealer quotes

Contains fulltext : 46636.pdf (publisher's version ) (Closed access)21 p