An Econometric Model of the Term Structure of Interest Rates Under Regime-Switching Risk

Wu, Shu; Zeng, Yong

doi:10.1007/978-1-4899-7442-6_3

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…The reason for their increased popularity is that they present various attractive features. In particular, the time-varying parameters which are driven by the state variable of the presumed underlying Markov process, lead to models that can accommodate both non-linearities and mean reversions Wu and Zeng (2014); Guidolin and Timmermann (2008). In addition, HM models can act as filtering processes that account for outliers and abrupt changes in financial market behavior Persio] and Frigo (2016); Ang and Timmermann (2012) and flexibly approximate general classes of density functions Timmermann (2000).…”

Section: Other Related Literaturementioning

confidence: 99%

Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models

Koki¹,

Leonardos²,

Piliouras³

2020

Preprint

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In this paper, we consider a variety of multi-state Hidden Markov models for predicting and explaining the Bitcoin, Ether and Ripple returns in the presence of state (regime) dynamics. In addition, we examine the effects of several financial, economic and cryptocurrency specific predictors on the cryptocurrency return series. Our results indicate that the Non-Homogeneous Hidden Markov (NHHM) model with four states has the best one-step-ahead forecasting performance among all competing models for all three series. The dominance of the predictive densities over the single regime random walk model relies on the fact that the states capture alternating periods with distinct return characteristics. In particular, the four state NHHM model distinguishes bull, bear and calm regimes for the Bitcoin series, and periods with different profit and risk magnitudes for the Ether and Ripple series. Also, conditionally on the hidden states, it identifies predictors with different linear and non-linear effects on the cryptocurrency returns. These empirical findings provide important insight for portfolio management and policy implementation.

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Section: Other Related Literaturementioning

confidence: 99%

Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models

Koki¹,

Leonardos²,

Piliouras³

2020

Preprint

View full text Add to dashboard Cite

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“…We note that the Markov switching of regimes in a model can alternatively be formulated via a marked point process (see Last and Brandt [26] for the pertinent notion). There is a relatively narrow literature on this alternative formulation, but examples include Criego and Swishchuk [3], Landén [25], and Wu and Zeng [34].…”

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confidence: 99%

Bond pricing formulas for Markov-modulated affine term structure models

Rodrigo¹,

Mamon²

2021

JIMO

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This article provides new developments in characterizing the class of regime-switching exponential affine interest rate processes in the context of pricing a zero-coupon bond. A finite-state Markov chain in continuous time dictates the random switching of time-dependent parameters of such processes. We present exact and approximate bond pricing formulas by solving a system of partial differential equations and minimizing an error functional. The bond price expression exhibits a representation that shows how it is explicitly impacted by the rate matrix and the time-dependent coefficient functions of the short rate models. We validate the bond pricing formulas numerically by examining a regime-switching Vasicek model.

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Pricing regime-switching risk in an HJM interest rate environment

Elliott

Siu

2016

Quantitative Finance

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An Econometric Model of the Term Structure of Interest Rates Under Regime-Switching Risk

Cited by 3 publications

References 45 publications

Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models

Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models

Bond pricing formulas for Markov-modulated affine term structure models

Pricing regime-switching risk in an HJM interest rate environment

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