Pricing and Evaluating a Bond Portfolio Using a Regime Switching Markov Model

2007 ◽  
Author(s):  
Leela R. Mitra ◽  
Gautam Mitra ◽  
Rogemar Mamon
Keyword(s):  
Markov Model ◽  
Regime Switching ◽  
Bond Portfolio

scholarly journals On the Finite Horizon Optimal Switching Problem with Random Lag

2020 ◽  
Author(s):  
Magnus Perninge
Keyword(s):  
Optimal Control ◽  
Markov Model ◽  
Regime Switching ◽  
Operation Mode ◽  
Finite Horizon ◽  
Optimal Switching ◽  
Component Failure ◽  
Probabilistic Technique ◽  
Switching Mode

AbstractWe consider an optimal switching problem with random lag and possibility of component failure. The random lag is modeled by letting the operation mode follow a regime switching Markov-model with transition intensities that depend on the switching mode. The possibility of failures is modeled by having absorbing components. We show existence of an optimal control for the problem by applying a probabilistic technique based on the concept of Snell envelopes.

Option-based risk management of a bond portfolio under regime switching interest rates

2011 ◽  
Vol 36 (1) ◽  
pp. 47-70 ◽  
Author(s):  
Fabio Antonelli ◽  
Alessandro Ramponi ◽  
Sergio Scarlatti
Keyword(s):  
Risk Management ◽  
Interest Rates ◽  
Regime Switching ◽  
Bond Portfolio

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.

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