Modeling and forecasting extreme commodity prices: A Markov-Switching based extreme value model

We propose a Markov-Switching Multifractal Peaks-Over-Threshold (MSM-POT) model to capture the dynamic behavior of the random occurrences of extreme events exceeding a high threshold in time series of returns. This approach allows introducing changes of regimes in the conditional mean function of the inter-exceedance times (i.e., the time between two consecutive extreme events) in order to admit the presence of short- and long-term memory patterns. Further, through its multifractal structure, the MSM-POT approach is able to capture the typical stylized facts of extreme events observed in financial time series, such as temporal clustering of the size of exceedances and temporal behavior of tail thickness.