Credit Risk Theoretical Model on the Base of DCC-GARCH in Time-Varying Parameters Framework

Nikita Moiseev, Aleksander Sorokin, Nataliya Zvezdina (), Alexey Mikhaylov, Lyubov Khomyakova and Mir Sayed Shah Danish
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Nikita Moiseev: Department of Mathematical Methods in Economics, Plekhanov Russian University of Economics, 117997 Moscow, Russia
Aleksander Sorokin: Department of Mathematical Methods in Economics, Plekhanov Russian University of Economics, 117997 Moscow, Russia
Lyubov Khomyakova: Institute for Research of International Economic Relations, Financial University under the Government of Russian Federation, 124167 Moscow, Russia
Mir Sayed Shah Danish: Strategic Research Projects Center, University of the Ryukyus, Nishihara, Okinawa 903-0213, Japan

Mathematics, 2021, vol. 9, issue 19, 1-12

Abstract: The research paper is devoted to developing a mathematical approach for dealing with time-varying parameters in rolling window logit models for credit risk assessment. Forecasting coefficients yields a better model accuracy than a trivial approach of using computed past statistics parameters for the next time period. In this paper, a new method of dealing with time-varying parameters of scoring models is proposed, which is aimed at computing the default probability of a borrower. It was empirically shown that in a continuously changing economic environment factors’ influence on a target variable is also changing. Therefore, forecasting coefficients yields a better financial result than simply applying parameters obtained by accumulated statistics over past time periods. The paper develops a new theoretical approach, incorporating a combination of the ARIMA class model, the DCC-GARCH model and the state–space model, which is more accurate, than using only the ARIMA model. Rigorous simulation testing is provided to confirm the efficiency of the proposed method.

Keywords: default probability; scoring model; logistic regression; time-varying parameters; time series forecasting; ARIMA; DCC-GARCH; Kalman filter; state–space model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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