4,487 research outputs found
Solutions of Backward Stochastic Differential Equations on Markov Chains
We consider backward stochastic differential equations (BSDEs) related to
finite state, continuous time Markov chains. We show that appropriate solutions
exist for arbitrary terminal conditions, and are unique up to sets of measure
zero. We do not require the generating functions to be monotonic, instead using
only an appropriate Lipschitz continuity condition.Comment: To appear in Communications on Stochastic Analysis, August 200
Filters and smoothers for self-exciting Markov modulated counting processes
We consider a self-exciting counting process, the parameters of which depend
on a hidden finite-state Markov chain. We derive the optimal filter and
smoother for the hidden chain based on observation of the jump process. This
filter is in closed form and is finite dimensional. We demonstrate the
performance of this filter both with simulated data, and by analysing the
`flash crash' of 6th May 2010 in this framework
Comparisons for backward stochastic differential equations on Markov chains and related no-arbitrage conditions
Most previous contributions to BSDEs, and the related theories of nonlinear
expectation and dynamic risk measures, have been in the framework of continuous
time diffusions or jump diffusions. Using solutions of BSDEs on spaces related
to finite state, continuous time Markov chains, we develop a theory of
nonlinear expectations in the spirit of [Dynamically consistent nonlinear
evaluations and expectations (2005) Shandong Univ.]. We prove basic properties
of these expectations and show their applications to dynamic risk measures on
such spaces. In particular, we prove comparison theorems for scalar and vector
valued solutions to BSDEs, and discuss arbitrage and risk measures in the
scalar case.Comment: Published in at http://dx.doi.org/10.1214/09-AAP619 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Anticipated backward stochastic differential equations
In this paper we discuss new types of differential equations which we call
anticipated backward stochastic differential equations (anticipated BSDEs). In
these equations the generator includes not only the values of solutions of the
present but also the future. We show that these anticipated BSDEs have unique
solutions, a comparison theorem for their solutions, and a duality between them
and stochastic differential delay equations.Comment: Published in at http://dx.doi.org/10.1214/08-AOP423 the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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