The Determinants of Equity Risk and Their Forecasting Implications: A Quantile Regression Perspective

Abstract

Several market and macro-level variables influence the evolution of equity risk in addition to the well-known volatility persistence. However, the impact of those covariates might change depending on the risk level, being different between low and high volatility states. By combining equity risk estimates, obtained from the Realized Range Volatility, corrected for microstructure noise and jumps, and quantile regression methods, we evaluate the forecasting implications of the equity risk determinants in different volatility states and, without distributional assumptions on the realized range innovations, we recover both the points and the conditional distribution forecasts. In addition, we analyse how the the relationships among the involved variables evolve over time, through a rolling window procedure. The results show evidence of the selected variables' relevant impacts and, particularly during periods of market stress, highlight heterogeneous effects across quantiles.

Risk optimization of industrial interprise

“…In addition, the contribution to solving the risk optimization problem by such scientists should be noted (Bajgoric, 2011;Better et al, 2008;Bonaccolto & Caporin, 2016;Dźwigoł, 2018;Dźwigoł & Dźwigoł-Barosz, 2018;Dźwigoł & Wolniak, 2018;Dzwigoł et al, 2019;Eskandari & Fuschi & Tvaronavičienė, 2016;Rabelo, 2007;Fukushima, 2006;Hroznyi et al, 2018;Kamioska, 2018;Karpenko et al, 2018;Kouvelis & Yu, 1997;Kelly, 2002;Kuzmak et al, 2018;Kvilinskyi & Kravchenko, 2016;Кwilinski, 2017, 2018a, 2018b, 2018c, 2019Kwilinski et al, 2019aKwilinski et al, , 2019bMackevičius et al, 2018;Okoli et al, 2016;Paulusch, 2017;Ramalingama, 2018;Savchenko et al, 2019;Tarasova, 2018;Tkachenko et al, 2019;Zeng & Skibniewski, 2013;Zhou et al, 2016).…”