Optimal consumption and investment with bounded downside risk for power utility functions

We investigate optimal consumption and investment problems for a Black-Scholes market under uniform restrictions on Value-at-Risk and Expected Shortfall. We formulate various utility maximization problems, which can be solved explicitly. We compare the optimal solutions in form of optimal value, optimal control and optimal wealth to analogous problems under additional uniform risk bounds. Our proofs are partly based on solutions to Hamilton-Jacobi-Bellman equations, and we prove a corresponding verification theorem. This work was supported by the European Science Foundation through the AMaMeF programme.Comment: 36 page

A note on a result of Liptser-Shiryaev

Given two stochastic equations with different drift terms, under very weak assumptions Liptser and Shiryaev provide the equivalence of the laws of the solutions to these equations by means of Girsanov transform. Their assumptions involve both the drift terms. We are interested in the same result but with the main assumption involving only the difference of the drift terms. Applications of our result will be presented in the finite as well as in the infinite dimensional setting.Comment: 22 pages; revised and enlarged versio

Observability and nonlinear filtering

This paper develops a connection between the asymptotic stability of nonlinear filters and a notion of observability. We consider a general class of hidden Markov models in continuous time with compact signal state space, and call such a model observable if no two initial measures of the signal process give rise to the same law of the observation process. We demonstrate that observability implies stability of the filter, i.e., the filtered estimates become insensitive to the initial measure at large times. For the special case where the signal is a finite-state Markov process and the observations are of the white noise type, a complete (necessary and sufficient) characterization of filter stability is obtained in terms of a slightly weaker detectability condition. In addition to observability, the role of controllability in filter stability is explored. Finally, the results are partially extended to non-compact signal state spaces

Large closed queueing networks in semi-Markov environment and its application

The paper studies closed queueing networks containing a server station and $k$ client stations. The server station is an infinite server queueing system, and client stations are single-server queueing systems with autonomous service, i.e. every client station serves customers (units) only at random instants generated by a strictly stationary and ergodic sequence of random variables. The total number of units in the network is $N$. The expected times between departures in client stations are $(N\mu_j)^{-1}$. After a service completion in the server station, a unit is transmitted to the $j$th client station with probability $p_{j}$ $(j=1,2,...,k)$, and being processed in the $j$th client station, the unit returns to the server station. The network is assumed to be in a semi-Markov environment. A semi-Markov environment is defined by a finite or countable infinite Markov chain and by sequences of independent and identically distributed random variables. Then the routing probabilities $p_{j}$ $(j=1,2,...,k)$ and transmission rates (which are expressed via parameters of the network) depend on a Markov state of the environment. The paper studies the queue-length processes in client stations of this network and is aimed to the analysis of performance measures associated with this network. The questions risen in this paper have immediate relation to quality control of complex telecommunication networks, and the obtained results are expected to lead to the solutions to many practical problems of this area of research.Comment: 35 pages, 1 figure, 12pt, accepted: Acta Appl. Mat

Maximum Likelihood and Gaussian Estimation of Continuous Time Models in Finance

Published in Handbook of financial time series, 2008, https://doi.org/10.1007/978-3-540-71297-8_22</p

Maximum likelihood and Gaussian estimation of continuous time models in finance

Ministry of Education, Singapore under its Academic Research Funding Tier

Large Deviations For Unbounded Additive Functionals Of Markov Process With Discrete Time (non compact case)

. We combine the Donsker and Varadhan large deviation principle (l.d.p.) for the occupation measure of Markov process with certain results of Deuschel and Strook to obtain the l.d.p. for unbounded functionals. Our approach relies on the concept of exponential tightness and the Puhalskii theorem. Three illustrative examples are considered. Key words: Exponential tightness, Large deviations 1. Introduction and main result 1. Consider an ergodic Markov process ¸ = (¸ k ) k0 having R as its state space, 0 (dx) as the distribution of the initial point ¸ 0 , and = (dx) as the invariant measure. The transition probability ß(x; dy) is assumed to satisfy the Feller condition. From application point of view it is interesting to get the large deviations for functionals of the type ( 1 n P n\Gamma1 k=0 g(¸ k ); n 1) with a continuous unbounded function g = g(x). There exist different ways of solving this problem (see Gartner [8], Dueschel and Stroock [3], Veretennikov [13], Acosta [1], Elli..