Adaptive stochastic risk estimation of firm operating profit

• Published in: Economia e politica industriale 2021-09, Vol.48 (3), p.463-504
• Main Authors: Akca, Ahmet, Çanakoğlu, Ethem
• Format: Article
• Language: English
• Subjects: Business Strategy/Leadership; Economics; Economics and Finance; Entrepreneurship; Industrial economics; Industrial Organization; Principal components analysis; R & D/Technology Policy; Securities markets
• ISSN: 0391-2078; 1972-4977
• DOI: 10.1007/s40812-021-00184-z

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.