27,512 research outputs found
Risk-sensitive investment in a finite-factor model
A new jump diffusion regime-switching model is introduced, which allows for
linking jumps in asset prices with regime changes. We prove the existence and
uniqueness of the solution to the risk-sensitive asset management criterion
maximisation problem in this setting. We provide an ODE for the optimal value
function, which may be efficiently solved numerically. Relevant probability
measure changes are discussed in the appendix. The approach of Klebaner and
Lipster (2014) is used to prove the martingale property of the relevant density
processes.Comment: 23 pages, 1 figur
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Finite Horizon Portfolio Selection
We study the problem of maximising expected utility of terminal wealth
over a nite horizon, with one risky and one riskless asset available, and
with trades in the risky asset subject to proportional transaction costs.
In a discrete time setting, using a utility function with hyperbolic risk
aversion, we prove that the optimal trading strategy is characterised by
a function of time (t), which represents the ratio of wealth held in the
risky asset to that held in the riskless asset. There is a time varying no
transaction region with boundaries b(t) < s(t), such that the portfo-
lio is only rebalanced when (t) is outside this region. The results are
consistent with similar studies of the in nite horizon problem with in-
termediate consumption, where the no transaction region has a similar,
but time independent, characterisation. We solve the problem numerically
and compute the boundaries of the no transaction region for typical model
parameters. We show how the results can be used to implement option
pricing models with transaction costs based on utility maximisation over
a nite horizo
Average Continuous Control of Piecewise Deterministic Markov Processes
This paper deals with the long run average continuous control problem of
piecewise deterministic Markov processes (PDMP's) taking values in a general
Borel space and with compact action space depending on the state variable. The
control variable acts on the jump rate and transition measure of the PDMP, and
the running and boundary costs are assumed to be positive but not necessarily
bounded. Our first main result is to obtain an optimality equation for the long
run average cost in terms of a discrete-time optimality equation related to the
embedded Markov chain given by the post-jump location of the PDMP. Our second
main result guarantees the existence of a feedback measurable selector for the
discrete-time optimality equation by establishing a connection between this
equation and an integro-differential equation. Our final main result is to
obtain some sufficient conditions for the existence of a solution for a
discrete-time optimality inequality and an ordinary optimal feedback control
for the long run average cost using the so-called vanishing discount approach.Comment: 34 page
Portfolio Optimization under Partial Information with Expert Opinions: a Dynamic Programming Approach
This paper investigates optimal portfolio strategies in a market where the
drift is driven by an unobserved Markov chain. Information on the state of this
chain is obtained from stock prices and expert opinions in the form of signals
at random discrete time points. As in Frey et al. (2012), Int. J. Theor. Appl.
Finance, 15, No. 1, we use stochastic filtering to transform the original
problem into an optimization problem under full information where the state
variable is the filter for the Markov chain. The dynamic programming equation
for this problem is studied with viscosity-solution techniques and with
regularization arguments.Comment: 31 page
Numerical method for expectations of piecewise-determistic Markov processes
We present a numerical method to compute expectations of functionals of a
piecewise-deterministic Markov process. We discuss time dependent functionals
as well as deterministic time horizon problems. Our approach is based on the
quantization of an underlying discrete-time Markov chain. We obtain bounds for
the rate of convergence of the algorithm. The approximation we propose is
easily computable and is flexible with respect to some of the parameters
defining the problem. Two examples illustrate the paper.Comment: 41 page
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