Forecasting Value-at-Risk in turbulent stock markets via the local regularity of the price process

Abstract

A new computational approach based on the pointwise regularity exponent of the price time series is proposed to estimate Value at Risk. The forecasts obtained are compared with those of two largely used methodologies: the variance-covariance method and the exponentially weighted moving average method. Our findings show that in two very turbulent periods of financial markets the forecasts obtained using our algorithm decidedly outperform the two benchmarks, providing more accurate estimates in terms of both unconditional coverage and independence and magnitude of losses.

Cluster Analysis on Locally Asymptotically Self-Similar Processes with Known Number of Clusters

“…This study is motivated by modeling using locally asymptotically self-similar processes as well. We refer the readers to [45][46][47][48][49][50].…”