Adaptive stochastic risk estimation of firm operating profit

This paper presents an adaptive stochastic approach to model and estimate the risk measures of the operating profit of a non-financial business. On the one hand, exogenous financial market variables specific to a country, including foreign exchange, interest, and inflation rates, are stochastically modeled via ARCH/GARCH, Vasicek, and regime-switching mean-reverting processes, respectively. Then, the dependency among all financial variables is captured via a residual Student-t copula. On the other hand, as opposed to traditional business planning whereby a few discrete base/best/worst-case scenarios are developed through a limited number of fixed models, nine distinct revenue and five non-revenue models, including ARIMA, principal component analysis (PCA), and principal component regression (PCR) are introduced to be adaptively selected to calculate the operating profit of a business. Finally, the stochastic exogenous financial market and operating profit model of the business are integrated to estimate various risk measures, including the CVaR, of the operating profit. The paper concludes with a case study on the adaptive generation of a stochastic business operating model and estimation of risk measures of the operating profit of a sample, publicly-traded corporation.