Stochastic Correlation in Risk Analytics: A Financial Perspective

Risk analytics has been popularized by some of the today’s most successful companies through new theories such as enterprise risk management. Maximizing the benefit from investments on projects can be more based on the correlation structure dynamically from various different sources. It becomes very important to assess the forecasting performance of the stochastic correlation models to achieve higher predictive power for risk analytics. We conduct evaluations of stochastic correlation modeling in risk analytics from a financial perspective in this paper. We compare the out-of-sample forecasting performance of exponentially weighted moving average (EWMA) model of RiskMetrics, the dynamic conditional correlation (DCC) multivariate GARCH, the orthogonal GARCH (OGARCH), and the generalized orthogonal GARCH (GOGARCH) using the data from various markets. We find that OGARCH has the best performance and all the multivariate GARCH models outperform EWMA model in risk measurement.