Time Varying Correlation Estimation Using Probabilistic Fuzzy Systems

Accurate financial risk analysis has drawn considerable attention after the recent financial crisis. Several regulatory agencies recently documented the need for proper assessment and reporting of financial risk for banks and other financial institutions. It is stressed that risk analysis should take into account changing risk properties over time. For a set of financial assets, risk analysis relies on the correlation and covariance structure among these returns from these assets. Therefore analyzing changes in the correlations and covariances of assets is essential to document changing risk properties. In this paper we show that a pfs can be used to model unobserved time-varying correlation between financial returns. The method is applied to simulated data and real data of daily nasdaq and hsi stock returns. We show that the pfs application improves over the conventional moving window approximation of time-varying correlation by decreasing the sensitivity of the results to the selection of the window length.