Risk management with multiple VaR constraints

We study a utility maximization problem under multiple Value-at-Risk (VaR)-type constraints. The optimization framework is particularly important for financial institutions which have to follow short-time VaR-type regulations under some realistic regulatory frameworks like Solvency II, but need to serve long-term liabilities. Deriving closed-form solutions, we show that risk management using multiple VaR constraints is more useful for loss prevention at intertemporal time instances compared with the well-known result of the one-VaR problem in Basak and Shapiro (Rev Financ Stud 14:371–405, 2001), confirming the numerical analysis of Shi and Werker (J Bank Finance 36(12):3227–3238, 2012). In addition, the multiple-VaR solution at maturity on average dominates the one-VaR solution in a wide range of intermediate market scenarios, but performs worse in good and very bad market scenarios. The range of these very bad market scenarios is however rather limited. Finally, we show that it is preferable to reach a fixed terminal state through insured intertemporal states rather than through extreme up and down movements, showing that a multiple-VaR framework induces a preference for less volatility.     

An iterative approximation procedure for the distribution of the maximum of a random walk

Let I(F) be the distribution function (d.f.) of the maximum of a random walk whose i.i.d. increments have the common d.f. F and a negative mean. We derive a recursive sequence of embedded random walks whose underlying d.f.'s F_k converge to the d.f. of the first ladder variable and satisfy FF₁F₂ on [0,∞) and I(F)=I(F₁)=I(F₂)=. Using these random walks we obtain improved upper bounds for the difference of I(F) and the d.f. of the maximum of the random walk after finitely many steps.     

Generating function analysis of some joint distributions for Poisson loss systems

We present a new approach to derive joint distributions for stationary Poisson loss systems. In particular, for M/M/m/0 and M/M/1/n we find the Laplace transforms (with respect to time t) of the probability that at time t there are i customers in the system and during [0,t], j customers are refused admission; for M/M/m/0 we further determine the LT of the probability that the system was full for less than s time units during [0,t] and serves i customers at time t. Explicit formulas for the corresponding moments are also given.     

FIFO Versus LIFO Issuing Policies for Stochastic Perishable Inventory Systems

We consider an inventory system for perishable items in which the arrival times of the items to be stored and the ones of the demands for those items form independent Poisson processes. The shelf lifetime of every item is finite and deterministic. Every demand is for a single item and is satisfied by one of the items on the shelf, if available. A demand remains unsatisfied if it arrives at an empty shelf. The aim of this paper is to compare two issuing policies: under FIFO (‘first in, first out’) any demand is satisfied by the item with the currently longest shelf life, while under LIFO (‘last in, first out’) always the youngest item on the shelf is assigned first. We determine the long-run net average profit as a function of the system parameters under each of the two policies, taking into account the revenue earned from satisfied demands, the cost of shelf space, penalties for unsatisfied demands, and the purchase cost of incoming items. The analytical results are used in several numerical examples in which the optimal input rate and the maximum expected long-run average profit under FIFO and under LIFO are determined and compared. We also provide a sensitivity analysis of the optimal solution for varying parameter values.     

Integral representations and convergence of the renewal density

We derive integral representations for the renewal density u associated with a square integrable probability density p on [0,∞) having finite expected value μ. These representations express u in terms of the real and the imaginary part of the Fourier transform of p, considered as a function on the lower complex half plane. We use them to give simple global integrability conditions on p under which limt→∞(u(t)−p(t))=1/μ.     

Erneuerungssätze für Prozesse mit stationären unabhängigen Zuwächsen

Ohne Zusammenfassung     

The stochastic shortest-path problem for Markov chains with infinite state space with applications to nearest-neighbor lattice chains

The aim of this paper is to solve the basic stochastic shortest-path problem (SSPP) for Markov chains (MCs) with countable state space and then apply the results to a class of nearest-neighbor MCs on the lattice state space $\mathbb Z \times \mathbb Z $ whose only moves are one step up, down, to the right or to the left. The objective is to control the MC, by suppressing certain moves, so as to minimize the expected time to reach a certain given target state. We characterize the optimal policies for SSPPs for general MCs with countably infinite state space, the main tool being a verification theorem for the value function, and give an algorithmic construction. Then we apply the results to a large class of examples: nearest-neighbor MCs for which the state space $\mathbb Z \times \mathbb Z $ is split by a vertical line into two regions inside which the transition probabilities are the same for every state. We give a necessary and sufficient condition for the so-called distance-diminishing policy to be optimal. For the general case in which this condition does not hold we develop an explicit finite construction of an optimal policy.