On It\^o-Taylor expansion for stochastic differential equations with Markovian switching and its application in $\gamma\in\{n/2:n \in\mathbb{N}\}$-order scheme
Tejinder Kumar, Chaman Kumar

TL;DR
This paper develops an Itô-Taylor expansion for stochastic differential equations with Markovian switching, enabling higher-order numerical schemes with proven convergence rates, addressing a gap in the literature caused by the complexity of combined continuous and discrete dynamics.
Contribution
The paper introduces the first Itô-Taylor expansion for SDEs with Markovian switching and constructs explicit high-order schemes with proven convergence rates.
Findings
Derived the Itô-Taylor expansion for SDEwMS under regularity conditions.
Constructed an explicit numerical scheme with strong convergence rate γ.
Results reduce to classical SDE cases when the Markov chain is trivial.
Abstract
The coefficients of the stochastic differential equations with Markovian switching (SDEwMS) additionally depend on a Markov chain and there is no notion of differentiating such functions with respect to the Markov chain. In particular, this implies that the It\^o-Taylor expansion for SDEwMS is not a straightforward extension of the It\^o-Taylor expansion for stochastic differential equations (SDEs). Further, higher-order numerical schemes for SDEwMS are not available in the literature, perhaps because of the absence of the It\^o-Taylor expansion. In this article, first, we overcome these challenges and derive the It\^o-Taylor expansion for SDEwMS, under some suitable regularity assumptions on the coefficients, by developing new techniques. Secondly, we demonstrate an application of our first result on the It\^o-Taylor expansion in the numerical approximations of SDEwMS. We derive an…
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Taxonomy
TopicsStochastic processes and financial applications · Insurance, Mortality, Demography, Risk Management · Probability and Risk Models
