Volatility Linkages across Equity, Money and Bond Markets: An Implied Volatility Approach

Information and volatility linkages across energy and financial markets

“…In contrast, the estimated variance of the log information flows σ h , k 2 is typically the highest for crude oil among energy markets (0.981 or 0.975), indicating greater kurtosis in the return distribution of crude oil than natural gas/clean energy. In general, the variance parameters reported here are greater than those reported in Fleming et al (1998) and Wang (2009), probably due to the different sample period considered in this study. Specifically, the sample period in this study covers the most recent financial crisis and the post-crisis recovery period.…”

“…Moreover, the information flow correlations of stocks-bonds, stocks-money, and bonds-money (for a sample period from 25 April 2008 to 10 March 2017) are much higher than those reported in Fleming et al (1998) (for a sample period from 3 January 1983 to 31 August 1995) and Wang (2009) (for a sample period from 5 January 1998 to 31 December 2007). This reveals that the information linkages across these markets have strengthened over time.…”

“…This suggests that implied volatilities in these markets are highly persistent, which raises concerns that a correlation study between these series may be susceptible to spurious effects. Following Wang (2009), this study implements a Monte Carlo simulation procedure to detect and control for spurious effects. Underlying each simulation run is the following simple regression model…”

“…This study first follows Wang (2009) in using implied volatility to assess volatility linkages across markets. The intuition behind the implied volatility approach lies in its design and forecasting performance.…”

“…Kyle, 1985; Ross, 1989), this study proposes that information linkages across the markets of interest reside in volatility correlations rather than return correlations. To measure these linkages, this study applied two different approaches, informally with a simple correlation analysis based on implied volatility (Wang, 2009) and formally with the general method of moments (GMM) method used by Fleming et al (1998). The speculative trading model of Fleming et al (1998) directly estimates the contemporaneous correlation between the log information flows in different markets, which measures the strength of the volatility linkages across markets.…”