Volatility changes caused by the trading system:a Markov switching application

An expanding literature exists to suggest that the trading mechanism can influence the volatility of security returns. This study adds to this literature by examining the impact that the introduction of SETS, on the London Stock Exchange, had on the volatility of security returns. Using a Markov switching regime change model security volatility is categorized as being in a regime of either high or low volatility. It is shown that prior to the introduction of SETS securities tended to be in a low volatility regime. At the time SETS was introduced securities moved to a high volatility regime. This suggests that volatility increased when SETS was introduced.

Stochastic volatility and the goodness-of-fit of the Heston model

Recently, Drǎgulescu and Yakovenko proposed an analytical formula for computing the probability density function of stock log returns, based on the Heston model, which they tested empirically. Their research design inadvertently favourably biased the fit of the data to the Heston model, thus overstating their empirical results. Furthermore, Drǎgulescu and Yakovenko did not perform any goodness-of-fit statistical tests. This study employs a research design that facilitates statistical tests of the goodness-of-fit of the Heston model to empirical returns. Robustness checks are also performed. In brief, the Heston model outperformed the Gaussian model only at high frequencies and even so does not provide a statistically acceptable fit to the data. The Gaussian model performed (marginally) better at medium and low frequencies, at which points the extra parameters of the Heston model have adverse impacts on the test statistics. © 2005 Taylor & Francis Group Ltd.

Sticky credit spreads, macroeconomic activity and equity market volatility

We uncover high persistence in credit spread series that can obscure the relationship between the theoretical determinants of credit risk and observed credit spreads. We use a Markovswitching model, which also captures the stability (low frequency changes) of credit ratings, to show why credit spreads may continue to respond to past levels of credit risk, even though the state of the economy has changed. A bivariate model of credit spreads and either macroeconomic activity or equity market volatility detects large and significant correlations that are consistent with theory but have not been observed in previous studies. © 2010 Nova Science Publishers, Inc. All rights reserved.