A Multivariate Regime-Switching Mean Reverting Process and Its Application to the Valuation of Credit Risk

2014 ◽  
Vol 32 (4) ◽  
pp. 687-710 ◽  
Author(s):  
Yinghui Dong ◽  
Kam C. Yuen ◽  
Chongfeng Wu
Keyword(s):  
Credit Risk ◽  
Regime Switching ◽  
Mean Reverting Process

scholarly journals Pricing Convertible Bonds with Credit Risk under Regime Switching and Numerical Solutions

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Wei-Guo Zhang ◽  
Ping-Kang Liao
Keyword(s):  
Credit Risk ◽  
Regime Switching ◽  
Numerical Solutions ◽  
Dilution Effect ◽  
Convertible Bonds ◽  
Bond Pricing ◽  
Convertible Bond ◽  
Pricing Model ◽  
Black Scholes ◽  
The Impact

This paper discusses the convertible bonds pricing problem with regime switching and credit risk in the convertible bond market. We derive a Black-Scholes-type partial differential equation of convertible bonds and propose a convertible bond pricing model with boundary conditions. We explore the impact of dilution effect and debt leverage on the value of the convertible bond and also give an adjustment method. Furthermore, we present two numerical solutions for the convertible bond pricing model and prove their consistency. Finally, the pricing results by comparing the finite difference method with the trinomial tree show that the strength of the effect of regime switching on the convertible bond depends on the generator matrix or the regime switching strength.

scholarly journals Pricing Options with Credit Risk in Markovian Regime-Switching Markets

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Jinzhi Li ◽  
Shixia Ma
Keyword(s):  
Markov Chain ◽  
Credit Risk ◽  
Regime Switching ◽  
Stock Price ◽  
Arrival Rate ◽  
Reduced Form ◽  
European Option ◽  
Pricing Options ◽  
Markov Modulated ◽  
The Interest Rate

This paper investigates the valuation of European option with credit risk in a reduced form model when the stock price is driven by the so-called Markov-modulated jump-diffusion process, in which the arrival rate of rare events and the volatility rate of stock are controlled by a continuous-time Markov chain. We also assume that the interest rate and the default intensity follow the Vasicek models whose parameters are governed by the same Markov chain. We study the pricing of European option and present numerical illustrations.

scholarly journals Investigating the Determinants of Credit Spread Using a Markov Regime-Switching Model: Evidence from Banks in Taiwan

2021 ◽  
Vol 13 (17) ◽  
pp. 9535
Author(s):  
Su-Lien Lu ◽  
Kuo-Jung Lee
Keyword(s):  
Corporate Governance ◽  
Credit Risk ◽  
Regime Switching ◽  
Credit Spread ◽  
Board Structure ◽  
Governance Mechanism ◽  
Markov Regime Switching ◽  
Regime Switching Model ◽  
Switching Model ◽  
Markov Regime Switching Model

In this study, we investigate the determinants of credit spread using a Markov regime-switching model. We consider corporate governance variables and credit risk to analyze the determinants of credit spread. The corporate governance mechanism is an indicator of company sustainability, and credit spread is the main factor in profits obtained by banks. However, the relationship between credit spread and corporate governance is seldom discussed. We focus on loans from banks in Taiwan between 2000 and 2019 and apply a Markov regime-switching model, which is superior to other models in capturing different effects in various regimes. We specify two regime types: corporate governance and credit risk regimes. Furthermore, we consider four aspects of corporate governance: firm ownership structure, board structure, deviation, and information environment. In this study, the determinants of credit spread are investigated more thoroughly than in previous studies. Moreover, in this study, we examine the effects of monetary policy and economic status on credit spread using a Markov regime-switching model; such models are not employed to their full extent in related studies of credit spread. Empirical results indicate that credit spread has different effects in various regimes. Thus, understanding the determinants of credit spread in different regimes is crucial for financial analysts, investors, economic policymakers, and banks. Consequently, we expect that this study can improve the management and measurement of credit risk and be of value to financial institutions.

scholarly journals Estimating the Impact of Credit Risk Determinants in two Southeast European Countries: A Non-Linear Structural VAR Approach.

2018 ◽  
Vol 10 (1) ◽  
pp. 55-74
Author(s):  
Michail Karoglou ◽  
Konstantinos Mouratidis ◽  
Sofoklis Vogiazas
Keyword(s):  
Credit Risk ◽  
Regime Switching ◽  
Banking System ◽  
Spillover Effects ◽  
Structural Var ◽  
Vector Autoregressive ◽  
Greek Crisis ◽  
Long Run ◽  
Risk Determinants ◽  
The Impact

We study the impact of credit risk determinants on the Romanian and Bulgarian banking systems using a structural Markov Regime-Switching vector autoregressive (MRS-SVAR) analysis. To capture changes in the domestic macroeconomic conditions as well as the spillover effects from the Greek crisis we account for endogenous breaks in the mean and/or volatility dynamics. Our empirical results suggest that an increase of interest rate also increases the Romanian and Bulgarian credit risk in the short-run while in the medium and long-run it reduces it. We also find evidence of spillover effects from the Greek crisis on both the Romanian and Bulgarian banking system, which interestingly, are imminent in the low volatility regime.

scholarly journals Risk Factor Evolution for Counterparty Credit Risk under a Hidden Markov Model

Risks ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 66
Author(s):  
Ioannis Anagnostou ◽  
Drona Kandhai
Keyword(s):  
Markov Model ◽  
Credit Risk ◽  
Exchange Rates ◽  
Hidden Markov Model ◽  
Regime Switching ◽  
Hidden Markov ◽  
Numerical Study ◽  
Commodity Prices ◽  
Counterparty Credit Risk ◽  
The Impact

One of the key components of counterparty credit risk (CCR) measurement is generating scenarios for the evolution of the underlying risk factors, such as interest and exchange rates, equity and commodity prices, and credit spreads. Geometric Brownian Motion (GBM) is a widely used method for modeling the evolution of exchange rates. An important limitation of GBM is that, due to the assumption of constant drift and volatility, stylized facts of financial time-series, such as volatility clustering and heavy-tailedness in the returns distribution, cannot be captured. We propose a model where volatility and drift are able to switch between regimes; more specifically, they are governed by an unobservable Markov chain. Hence, we model exchange rates with a hidden Markov model (HMM) and generate scenarios for counterparty exposure using this approach. A numerical study is carried out and backtesting results for a number of exchange rates are presented. The impact of using a regime-switching model on counterparty exposure is found to be profound for derivatives with non-linear payoffs.

Validation of an integrity-based selection measure for credit risk potential

2009 ◽  
Author(s):  
Kelly D. Dages ◽  
John W. Jones ◽  
Bailey Klinger
Keyword(s):  
Credit Risk ◽  
Risk Potential
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