Identifying historical financial crisis: Bayesian stochastic search variable selection in logistic regression

This work investigates the factors that contribute to financial crises. We first study the Dow Jones index performance by grouping the daily adjusted closing value into a two-month window and finding several critical quantiles in each window. Then, we identify severe downturn in these quantiles and find that the 5th quantile is the best to identify financial crises. We then matched these quantiles with historical financial crises and gave a basic explanation about them. Next, we introduced all exogenous factors that could be related to the crises. Then, we applied a rapid Bayesian variable selection technique - Stochastic Search Variable Selection (SSVS) using a Bayesian logistic regression model. Finally, we analyzed the result of SSVS, leading to the conclusion that that the dummy variable we created for disastrous hurricane, crude oil price and gold price (GOLD) should be included in the model.