We revisit the apparent historical success of technical trading rules on daily prices of the DJIA index from 1897 to 2011, and use the False Discovery Rate as a new approach to data snooping. The advantage of the FDR over existing methods is that it selects more outperforming rules which allows...

We provide a closed-form solution to an optimal investment and consumption problem for a constant absolute risk aversion (CARA) agent, who faces execution costs when trading correlated risky assets with return predictability. The optimal investment strategy indicates that the agent should trade...

We show that option prices predict future stock returns only when stock returns are ex-ante predictable using public signals from the stock market itself. Directional option trading cannot explain these results, suggesting that they are not driven by informed trading or superior ability of...

We consider the problem of optimal dynamic trading in the presence of predictable returns and proportional transaction costs for an investor choosing among multiple assets. The value of each security equals the expected value of holding the asset plus the value of all options to trade. We...

This paper proposes a novel approach to the combination of conditional covariance matrix forecasts based on the use of the Generalized Method of Moments (GMM). It is shown how the procedure can be generalized to deal with large dimensional systems by means of a two-step strategy. The finite...

We propose a new approach to model high and low frequency components of equity correlations. Our framework combines a factor asset pricing structure with other specifications capturing dynamic properties of volatilities and covariances between a single common factor and idiosyncratic returns....

This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter...

This paper proposes a range-based dynamic conditional correlation (DCC) model combined by the return-based DCC model and the conditional autoregressive range (CARR) model. The substantial gain in efficiency of volatility estimation can boost the accuracy for estimating time-varying covariances....

This paper investigates the merits of high-frequency intraday data when forming minimum variance portfolios and minimum tracking error portfolios with daily rebalancing from the individual constituents of the S&P 100 index. We focus on the issue of determining the optimal sampling frequency,...