Option Pricing and Dynamic Discrete Time Hedging for Regime-Switching Geometric Random Walks Models

Rémillard, Bruno; Hocquard, Alexandre; Lamarre, Hugo; Papageorgiou, Nicolas

doi:10.2139/ssrn.1591146

Cited by 9 publications

(4 citation statements)

References 47 publications

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“…, n + 1. All these functions can be approximated using the methodology developed in Rémillard et al (2010) for the regime-switching case. Implementation of the hedging strategy then requires predicting h t .…”

Section: Implementation Issuesmentioning

confidence: 99%

“…These models are particular cases of regime-switching models with only one regime. We do not consider regime-switching models since it has been done in Rémillard et al (2010), where the daily log-returns of the S&P 500 are analyzed.…”

Section: Examples Of Applicationmentioning

confidence: 99%

“…Theses models can display serial dependence in the log-returns and may account for changing volatility over time. For implementation issues, including estimation and goodness-of-fit tests, see, e.g., Rémillard et al (2010).…”

Section: Verification Of the Assumptions Of Lemma 211mentioning

confidence: 99%

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Optimal hedging in discrete time

Rémillard¹,

Rubenthaler²

2013

Quantitative Finance

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Section: Implementation Issuesmentioning

confidence: 99%

Section: Examples Of Applicationmentioning

confidence: 99%

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Optimal hedging in discrete time

Rémillard¹,

Rubenthaler²

2013

Quantitative Finance

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“…has the Markov property with respect to G. 7 An alternative recursion formula is presented in Rémillard et al (2010a). However, the current formula is preferred for two main reasons.…”

Section: 2mentioning

confidence: 99%

Optimal Hedging When the Underlying Asset Follows a Regime-Switching Markov Process

François

Gauthier

Godin³

2012

SSRN Journal

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Abstract:We develop a flexible discrete-time hedging methodology that minimizes the expected value of any desired penalty function of the hedging error within a general regimeswitching framework. A numerical algorithm based on backward recursion allows for the sequential construction of an optimal hedging strategy. Numerical experiments comparing this and other methodologies show a relative expected penalty reduction ranging between 0.9% and 12.6% with respect to the best benchmark.

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