We study the term structure of variance (total risk), systematic and idiosyncratic risk. Consistent with the expectations hypothesis, we find that, for the entire market, the slope of the term structure of variance is mainly informative about the path of future variance. Thus, there is little...

A novel dynamic asset-allocation approach is proposed where portfolios as well as portfolio strategies are updated at every decision period based on their past performance. For modeling, a general class of models is specified that combines a dynamic factor and a vector autoregressive model and...

We propose a dynamic factor state-space model for high-dimensional covariance matrices of asset returns. It uses observed risk factors and assumes that the latent covariance matrix of assets and factors is observed through their realized covariance matrix with a Wishart measurement density. The...

A new model class for univariate asset returns is proposed which involves the use of mixtures of stable Paretian distributions, and readily lends itself to use in a multivariate context for portfolio selection. The model nests numerous ones currently in use, and is shown to outperform all its...

This paper studies a basket of risk statistics that are widely used to measure investment performance. Those risk statistics were used to rank the performance of the assets. The dependent information was removed from the set of risk measures that were used in the test. The risk statistics were...

The last financial crisis sheds dramatically light on the instability threatened by systemic risk. In this regard no common view appears to exist on the definition, the measurement and real impact on financial system. This paper aims to analyze the relation between systemic risk and portfolio...

The well-known difficulties in obtaining satisfactory results with Markowitz' intuitive portfolio theory have lead to an innumerable amount of proposed advancements by researchers and practitioners. As different as these approaches are, they typically appear to exhibit a satisfactory...

This paper applies machine learning algorithms to the modeling of realized betas for the purposes of forecasting stock systematic risk. Forecast horizons range from 1 week up to 1 month. The machine learning algorithms employed are ridge regression, decision tree learning, adaptive boosting,...

This paper focuses on the effects of the Fed's monetary policy on stock and bond returns co-movement and their implications to risk-based asset allocation. Using a regime-switching model that controls for the economic effects of monetary policy we identify three co-movement regimes. We document...

In this paper we investigate the predictive power of cross-sectional volatility, skewness and kurtosis for future stock returns. Adding to the work of Maio (2016), who finds cross-sectional volatility to forecast a decline in the equity premium with high predictive power in-sample as well as...