Controlling and managing potential losses is one of the main objective of the Risk Management. Following Ben Ameur and Prigent (2007) and Chen et al. (2008), and extending the first results by Hamidi et al. (2009) when adopting a risk management approach for defining insurance portfolio...

The paper examines the performance of four multivariate volatility models, namely CCC, VARMA-GARCH, DCC and BEKK, for the crude oil spot and futures returns of two major benchmark international crude oil markets, Brent and WTI, to calculate optimal portfolio weights and optimal hedge ratios, and...

Risk management is critical in decision-making, and mean-variance (MV) trade-off is one of the most common criteria. However, in reinforcement learning (RL) under a dynamic environment, MV control is not as easy as that under a static environment owing to computational difficulties. For MV...

This paper investigates the return-volatility relation by taking into account the model specification problem. The market volatility is modeled to have two components, one due to the diffusion risk and the other due to the jump risk. The model indicates that under the absence of leverage...

As common practice, oil studies in the economic literature are carried out by taking the correct specification of a model as given, and ignoring the problem of estimating overly optimistic confidence sets. This means that model uncertainty is pervasive in the empirical results. In this work I...

We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premia. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based...

Draw-down losses from a previously reached maximum portfolio wealth level, is an important risk measure for investment management. In this study, we present a discrete trading strategy to directly control a portfolio's maximum percentage of drawdown within a target level while maximizing the...

Investors have increasing interests in sophisticated yet transparent analytic tools to handle model uncertainty, tail risk and market dynamics. This paper demonstrates how macroeconomic factor models, based on Bayesian model averaging (BMA), can help address the challenges in some specific...

One of the main challenges investors have to face is model uncertainty. Typically, the dynamic of the assets is modeled using two parameters: the drift vector and the covariance matrix, which are both uncertain. Since the variance/covariance parameter is assumed to be estimated with a certain...

We define risk spillover as the dependence of a given asset variance on the past covariances and variances of other assets. Building on this idea, we propose the use of a highly flexible and tractable model to forecast the volatility of an international equity portfolio. According to the risk...