The merit of high-frequency data in portfolio allocation

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a multi-scale spectral decomposition where volatilities, correlation eigenvalues and eigenvectors evolve on different frequencies. In an extensive out-of-sample forecasting study, we show that the proposed approach yields less risky and more diversified portfolio allocations as prevailing methods employing daily data. These performance gains hold over longer horizons than previous studies have shown

International Capital Markets Structure, Preferences and Puzzles: The US-China Case

A canonical two country-two good model with standard preferences does not address three classic international macroeconomic puzzles as well as two well-known asset pricing puzzles. Specifically, under financial autarky, it does not account for the high real exchange rate (RER) volatility relative to consumption volatility (RER volatility puzzle), the negative RER-consumption differentials correlation (Backus-Smith anomaly), the relatively low cross- country consumption correlation (consumption correlation puzzle), the low risk-free rate (risk-free rate puzzle) and the high equity risk premium (equity premium puzzle) in the data. In this paper, we show that instead a two country-two good model with recursive preferences, international complete markets and correlated long-run innovations can address all five puzzles for a relatively large range of parameter values, specifically in the case of the US and China. Therefore, in contrast to other IBC models, its performance does not rely on any financial market imperfections

Long-Horizon Consumption Risk and the Cross-Section of Returns: New Tests and International Evidence

This paper investigates whether measuring consumption risk over long horizons can improve the empirical performance of the Consumption CAPM for size and value premia in international stock markets (US, UK, and Germany). In order to account for commonalities in size and book-tomarket sorted portfolios, we also include industry portfolios in our set of test assets. Our results show that, contrary to the findings of Parker and Julliard (2005), the model falls short of providing an accurate description of the cross-section of returns under our modified empirical approach. At the same time, however, measuring consumption risk over longer horizons typically yields lower risk-aversion estimates. Thus, our results suggest that more plausible parameter estimates - as opposed to lower pricing errors - can be regarded as the main achievement of the long-horizon Consumption CAPM