We study a bivariate mean reverting stochastic volatility model , finding an explicit expression for the decay of cross-asset correlations over time. We compare our result with the empirical time series of the Dow Jones Industrial Average and the Financial Times Stock Exchange 100 in the period 1984-2013 , finding an excellent agreement. The main features of the model consist in the jumps in the volatilities and a nonlinear mean reversion. Based on these features , we propose an algorithm for the detection of jumps in the volatility .
We consider a mean-reverting stochastic volatility model which satisfies some relevant stylized facts of financial markets. We introduce an algorithm for the detection of peaks in the volatility profile, that we apply to the time series of Dow Jones Industrial Average and Financial Times Stock Exchange 100 in the period 1984-2013 . Based on empirical results , we propose a bivariate version of the model, for which we find an explicit expression for the decay over time of cross-asset correlations between absolute returns. We compare our theoretical predictions with empirical estimates on the same financial time series, finding an excellent agreement .
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study a bivariate mean reverting
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, finding an explicit expression for the decay of cross-asset correlations over time. We compare our result with the empirical
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, finding an excellent agreement. The main features of the model consist in the jumps in the volatilities and a nonlinear mean reversion. Based on these features
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an algorithm for the detection of jumps in the volatility
a bivariate version of the model, for which we find an explicit expression for the decay over time of cross-asset correlations between absolute returns. We compare our theoretical predictions with empirical estimates on the same financial time series, finding an excellent agreement