Valuing volatility spillovers

Abstract

We show that volatility spillovers are large enough to matter to investors. We demonstrate that standard deviations of returns to mean-variance portfolios of European equities fall by 1-1.5% at daily, weekly, and monthly rebalancing horizons when volatility spillovers are included in covariance forecasts. We estimate the conditional second moment matrix of (synchronized) daily index returns for the London, Frankfurt and Paris stock markets via two asymmetric dynamic conditional correlation models (A-DCC): the unrestricted model includes volatility spillovers and the restricted model does not. We combine covariance forecasts from the restricted and unrestricted models with a wide range of assumed returns relatives via a polar coordinates method, and compute out-of-sample realized portfolio returns and variances for testing. Diebold-Mariano tests confirm that most risk reductions are statistically significant. Stochastic dominance tests indicate that portfolios accounting for volatility spillover would be preferred by risk averse agents.

Understanding volatility transmission mechanism among the cds markets: Europe & North America versus Brazil & Turkey

“…An autoregressive conditional heteroskedasticity (ARCH) specification developed by Engle (1982) and, later, generalized by Bollerslev et al (1988) has been widely used in modeling the volatility of high-frequency financial timeseries data. Multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) models have been commonly used to estimate the spillover effects in mean and volatility among different markets (see Hassan & Malik 2007, Frank & Hesse 2009, Savva 2009, Milunovich & Thorp 2006. In line with related literature, we follow the same approach and use MGARCH model to identify the volatility transmission relationship between the markets identified in the preceding section.…”

Illiquidity, return and risk in G7 stock markets: Interdependencies and spillovers

“…Econometric results on spillovers are very useful for international portfolio diversification (e.g. Milunovich and Thorp, 2006), but the identification of causality -and the success of portfolio diversification itself-needs to account for the fact that financial markets are open systems, with social coordinates and indeterminate institutional evolution.…”

An introduction to oil market volatility analysis

“…On a broader economic level, the volatility of oil prices has a significant impact on economic activity, and oil price changes are important explanatory variables in movements in stock returns. It is found that price turbulence is often transmitted from larger to small markets (Milunovich and Thorp, ). Pindyck () reported that as to be expected, crude oil volatility transfers to the natural gas market, but not vice versa.…”