Numerous econometric studies report that financial asset volatilities and correlations are time-varying and predictable. Over the past decade, this knowledge has stimulated increasing interest in various dynamic portfolio risk control techniques. The two basic types of risk control techniques...

Numerous empirical studies demonstrate the superiority of dynamic strategies with volatility weighting over time mechanism. These strategies control the portfolio risk over time by adjusting the risk exposure according to updated volatility forecasts. Yet, in order to reap all benefits promised...

Providing a more accurate covariance matrix forecast can substantially improve the performance of optimized portfolios. Using out-of-sample tests, in this paper, we evaluate alternative covariance matrix forecasting methods by looking at (1) their forecast accuracy, (2) their ability to track...

In this paper we document that at the aggregate stock market level the unexpected volatility is negatively related to expected future returns and positively related to future volatility. We demonstrate how the predictive ability of unexpected volatility can be utilized in dynamic asset...

The portfolio performance measures based on the Value-at-Risk (VaR) concept have gained widespread popularity and are often used in empirical studies. Unfortunately, we have noticed that in majority of empirical studies a VaR-based performance measure is used inconsistently. The goal of this...

Despite the ever-growing interest in trend following and a series of publications in academic journals, there is still a great shortage of theoretical results on the properties of trend following rules. Our paper fills this gap by comparing and contrasting the two most popular trend following...

Recent literature on stock return predictability suggests that it varies substantially across economic states being strongest during bad economic times. In line with this evidence, we document that stock volatility predictability is also state dependent. In particular, using a large data set of...

Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities. Still, little is known about the accuracy of volatility forecasts and the horizon of volatility predictability. This paper aims to fill these gaps in the literature. We begin this...

Using historical data that spans almost 150 years, we examine whether there is a long-run equilibrium relationship between the stock's earnings and bond yields. The novelty of our econometric methodology consists in using a vector error correction model where we allow multiple structural breaks...

There is evidence that volatility forecasting models that use intraday data provide better forecast accuracy as compared with that delivered by the models that use daily data. Exactly how much better is still unknown. The present paper fills this gap in the literature and extends previous...