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 discuss the problems of strategy selection in the framework of mathematical finance, stochastic control, and risk management in finance and economics. A problem setting that takes in to account a possibility of short term forecasting for market parameters is suggested. In this setting,...

We use a dynamic model averaging (DMA) approach to construct forecasts of individual equity returns for a large cross-section of stocks contained in the SP500, FTSE100, DAX30, CAC40 and SPX30 headline indices, taking value, momentum, and quality factors as predictor variables. Fixing the set of...

This paper considers the problem of model uncertainty in the case of multi-asset volatility models and discusses the use of model averaging techniques as a way of dealing with the risk of inadvertently using false models in portfolio management. Evaluation of volatility models is then considered...

This study predicts stock market volatility and applies them to the standard problem in finance, namely, asset allocation. Based on machine learning and model averaging approaches, we integrate the drivers’ predictive information to forecast market volatilities. Using various evaluation...

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...

Commercially available factor models provide good predictions of short-horizon (e.g. one day or one week) portfolio volatility, based on estimated portfolio factor loadings and responsive estimates of factor volatility. These predictions are of significant value to certain short-term investors,...

Risk forecasting is crucial for informed investment decision-making. Moreover, the salience of investment risk increases during economically uncertain times. In this paper, we study how sell-side analysts form expectations of firm risk, under different macroeconomic conditions (low versus high...

We employ and examine vine copulas in modeling symmetric and asymmetric dependency structures and forecasting financial returns. We analyze the asset allocations performed during the 2008–2009 financial crisis and test different portfolio strategies such as maximum Sharpe ratio, minimum...