Detecting Regime Shifts in Credit Spreads

Abstract

Using an innovative random regime shift detection methodology, we identify and confirm two distinct regime types in the dynamics of credit spreads: a level regime and a volatility regime. The level regime is long lived and shown to be linked to Federal Reserve policy and credit market conditions, whereas the volatility regime is short lived and, apart from recessionary periods, detected during major financial crises. Our methodology provides an independent way of supporting structural equilibrium models and points toward monetary and credit supply effects to account for the persistence of credit spreads and their predictive power over the business cycle.

Endogenous Hidden Markov Regimes in Operational Loss Data: Application to the Recent Financial Crisis

“…The most promising would be to verify the stability of the results using different regime detection methods (Maalaoui, Chun et al, 2014). How can an approach to detect regimes in real time improve the results, and in particular take the asymmetry detected in this article into account?…”

Dependence of credit spread and macro-conditions based on an alterable structure model

“…This paper is similar to [ 19 , 20 ] in terms of method or content, but due to the large difference in the corporate bonds market between China and the United States, this paper is distinctly different from them. China's economy has exceeded the United States from the scale (PPP), but the development of market economy and financial market are at an early stage, and the corporate bonds market was even established about ten years ago.…”

“…Ghysels et al used structural switching model to identify the obvious structural changes between risk and yield [ 18 ]. Based on enterprise level considerations, Chun et al investigated the cyclical impact of default rate and liquidity on bonds spreads [ 19 ], while Chun et al analyzed the credit supply, monetary policy and economic cycle and other factors’ local impact on the spreads by considering the structure change, [ 20 ], but ignored the existence of interaction among various factors that might affect the structural switching, and then drew a different conclusion.…”

“…Therefore, the actual default rate of the bonds is very low, and the price of the bonds is more influenced by macro factors than corporate characteristics. Chun et al focused on the cyclical impact of the United States’ macro-level factors on corporate bonds [ 20 ], but due to the immature market in China, the situation in China is much more complicated. China's corporate bonds spreads are not only influenced by money supply, institutional change, stock market, also by its own market, so a correct understanding of the factors affecting the bonds spreads is helpful for China to improve and manage the bonds market, and contributes to investors’ clear identification of bonds investment risk.…”

“…Therefore, in view of the characteristics of China's bonds market, this paper analyzes the influence of multiple macro-level factors on corporate bonds spreads in the context of structural transformation. The differences between this paper and [ 20 ] are: 1, due to the data characteristics, the structural transformation model of this paper is based on a generalized error distribution (GED); 2, this paper comprehensively considers the structural transformation characteristics of multiple macro-level factors, while [ 20 ] focused on the structural transformation of individual factors; 3, and considered the macro level factors on the spreads effect, without considering other unobservable factors. But in this paper, the influence of micro level and the unobservable factors on the model is controlled by the change of bonds spreads.…”