Modeling extreme European Market Risk

This research explores whether the risk of having extreme losses in the European stock market can be predicted by incorporating macro-economic variables. To answer this question, we investigated the tail distribution of weekly stock losses on six different European indices (AEX, DAX, CAC40, PSI-20, IBEX35 and FTSE MIB). We set up an Extreme Value Theory (EVT) machine learning framework using shrinkage regression techniques, such as LASSO. Our results show that when adding a limited amount of macro-economic covariates to the tail distribution of weekly losses, the prediction for the VaR is improved for five of the six indices. Inflation, short-term interest rate, industrial production and the USD/EUR exchange rate appear to have predicting power for the tail risk. No predicting power is found when using unemployment and the long-term interest rate. This study implies the importance of macro-economic information when estimating financial risks of investments in the European stock market, which can be of added value for investors and regulators.