Good News, Bad News, Volatility, and Betas

Abstract

We investigate the conditional covariances of stock returns using bivariate exponential ARCH (EGARCH) models. These models allow market volatility, portfolio‐specific volatility, and beta to respond asymmetrically to positive and negative market and portfolio returns, i.e., “leverage” effects. Using monthly data, we find strong evidence of conditional heteroskedasticity in both market and non‐market components of returns, and weaker evidence of time‐varying conditional betas. Surprisingly while leverage effects appear strong in the market component of volatility, they are absent in conditional betas and weak and/or inconsistent in nonmarket sources of risk.

Pricing of risk and volatility dynamics on an emerging stock market: evidence from both aggregate and disaggregate data

“…In line with the financial literature (e.g. see Braun et al, 1995;Cheung & Ng, 1992;Engle & Patton, 2001;Ewing et al, 2005 andMandimika &Chinzara, 2012 among others), this study also applied the EGARCH model to examine the asymmetric response of stock returns volatility, which is commonly known as the asymmetric and leverage effect. The following is the general equation representing EGARCH model (Ewing et al, 2005 andMandimika &Chinzara, 2012):…”

“…Furthermore, the use of monthly data is in accordance with strong financial literature (e.g. see Bloom, 2009;Braun, Nelson, & Sunier, 1995;Chinzara, 2011;Doukas, Hall, & Lang, 2003;Faff & Brailsford, 1999;Hansson & Hordahl, 1998;Khan, Muneer, & Anuar, 2013;Lanne & Luoto, 2008;Manolis, Stelios, & Angelos, 2002;Sadorsky, 2001 andWest &Worthington, 2006). Then, as is practice in the financial literature, the return series will be expressed in logarithmic difference between the two successive prices acquiring the continuous compounding returns (i.e.…”

Estimating Systematic Risk Using Time Varying Distributions

“…This article extends the work of Bekaert and Wu (2000) and Braun et al (1995) by providing additional evidence and new insights into the dynamics of systematic risk. Specifically, it exploits the time variation in the variance-covariance matrix of individual asset returns and market portfolio returns to estimate conditional betas.…”

“…Time variation and potential asymmetry in betas are examined by Braun, Nelson and Sunier (1995), BNS henceforth. BNS use a bivariate EGARCH model to estimate dynamic (time-varying) betas using monthly returns of beta-based and SIC-based portfolios.…”

“…2 Recent work by Bekaert and Wu (2000) provides more concrete evidence regarding time variation and asymmetry in betas. They use a version of the BEKK-GARCH type model which does not restrict correlations to be constant aÁ la Braun et al (1995). 3 Moreover, they link potential asymmetries to measures of leverage as suggested by theory.…”