Detecting time-variation in corporate bond index returns: A smooth transition regression model

Non-linear time series models, such as regimeswitching (RS), have become increasingly popular in economics. In the literature, regime-switching recurrent reinforcement learning (RS-RRL), a combined technique of statistical modeling and machine learning, has been proposed to build financial trading platforms and enhance trading profits by modeling the nonlinear dynamics of stock returns with smooth transition autoregressive (STAR) models. In this paper, we address the transition variable selection issue in the RS-RRL trading system. Four indicators, namely volume, relative strength index, implied volatility and conditional volatility are considered as possible options for transition variable selection in RS-RRL. Of the four indicators, it is found that the RS-RRL trading system with the volume indicator produces a better Sharpe ratio than others.

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“…The value of c usually falls within the 15%/85% quantiles of the historical values of s t (see [18]). …”

Section: A the Optimization Problemmentioning

confidence: 97%

Transition variable selection for regime switching recurrent reinforcement learning

Maringer

Zhang

2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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“…Our empirical analysis draws heavily on the rich literature in random walk and causality tests (Granger, 1969;Campbell et al, 1997). Similar to Chen and Maringer (2011), we account for nonlinearity in corporate bond index returns. Standard methods of return predictability tests are not robust to nonlinear dependence.…”

Section: Introductionmentioning

confidence: 99%

Are corporate bond market returns predictable?

Hong

Lin

2012

Journal of Banking & Finance

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“…Our study is, therefore, related to Acharya et al (2010) and other studies (e.g. Amihud, 2002;de Jong and Driessen, 2004;Lin et al, 2011;Chen and Maringer, 2011) examining the relationship between market liquidity and asset realised returns. 25…”

Section: (See Panels a And B Inmentioning

confidence: 76%

Common Factors in the Performance of European Corporate Bonds - Evidence before and after the Financial Crisis

Aussenegg

Goetz²,

Jelic

2015

Eur Financial Management

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We examine monthly excess returns for 23 Euro-denominated corporate bond indices and propose a new specification for bond asset pricing models. Specifically, we separate level and slope components of term and default risk factors and examine liquidity risk. Our results suggest that level and slope risk factors, derived from complete interest rate and default spread term structures, significantly improve the explanatory power of the Fama and French (1993) 2-factor model. We also demonstrate different sensitivities of risk factors before and after recent financial crisis. The results are robust to calendar seasonality and the consideration of equity market returns.Hypothesis 1: Only a few risk factors are sufficient to explain the common movement of European corporate bond returns.Duffee (1998) reports the importance of a term factor of the interest rate curve, defined as the performance difference between a 30 year Treasury bond and the 3 month Libor

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Detecting time-variation in corporate bond index returns: A smooth transition regression model

Cited by 17 publications

References 41 publications

Transition variable selection for regime switching recurrent reinforcement learning

Transition variable selection for regime switching recurrent reinforcement learning

Are corporate bond market returns predictable?

Common Factors in the Performance of European Corporate Bonds - Evidence before and after the Financial Crisis

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