Methodological Principles of Simulating Asymmetrical Volatility of Corporate Credit Market Dynamics

Abstract

Forecasting and modelling of price dynamics of financial instruments and their volatility is an essential element of technical analysis of financial markets. The real sector is also interested in changes in volatility as it seeks to maintain stability in financial and commodity markets. The article aims to develop methodological approaches to modelling the dynamics and volatility of the Ukrainian corporate credit market using asymmetric GARCH approaches. It has been established that the risky nature of financial markets is a prerequisite for analyzing and modeling the volatility of their dynamics in order to correctly respond to possible spikes in volatility, as well as to predict their duration. The analysis was based on daily data on interest rates on the corporate credit market. A graph of the initial time series, autocorrelation functions was plotted, the series was checked for stationarity by the Dickey–Fuller test, which led to its differentiation and subsequent formation of the optimal ARIMA specification. When checking the residuals for autocorrelation and the ARCH effect, positive results were obtained, which led to the use of the GARCH model. Going through various GARCH specifications made it possible to choose GJR-GARCH for modeling, which takes into account the asymmetry of the impact of information shocks on the profitability management of active bank operations. The resulting model was tested by the Leung–Box test, the ARCH LM test, and the Pearson test for the optimality of the specification. The model was compared with actual time series data. All the results confirmed the correctness of the built models, which allows them to be used for analysis and forecasting for further periods.

Keywords

• interest rate
• arima model
• garch model
• asymmetric distribution
• information shocks
• volatility