Mathematics

The drawdown process $Y=\bar{X} - X$ of a completely asymmetric L\'{e}vy process $X$ is given by $X$ reflected at its running supremum $\bar{X}$.In this paper we explicitly express the law of the sextuple $(\tau_a,\bar{G}_{\tau_a},\underline{X}_{\tau_a},\bar{X}_{\tau_a},Y_{\tau_a-},Y_{\tau_a}-a)$ in terms of the scale function and the L\'evy measure of $X$, where $\tau_a$ denotes the first-passage time of $Y$ over the level $a>0$, $\bar{G}_{\tau_a}$ is the time of the last supremum of $X$ prior to $\tau_a$ and $\underline{X}$ is the running infimum of $X$. We also explicitly identify the distribution of the drawup $\hat{Y}_{\tau_a}$ at the moment $\tau_a$, where $\hat{Y} = X-\underline{X}$, and derive the probability of a large drawdown preceding a small rally. These results are applied to the Carr & Wu \cite{CarrWu} model for S&P 500.

"We investigate the problem of optimal dividend distribution for a company in the presence of regime shifts. We consider a company whose cumulative net revenues evolve as a Brownian motion with positive drift that is modulated by a finite state Markov chain, and model the discount rate as a deterministic function of the current state of the chain. In this setting the objective of the company is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which is taken to be the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) are zero. We show that, if the drift is positive in each state, it is optimal to adopt a barrier strategy at certain positive regime-dependent levels, and provide an explicit characterization of
the value function as the fixed point of a contraction. In the case that the drift is small and negative in one state, the optimal strategy takes a different form, which we explicitly identify if there are two regimes. We also provide a numerical illustration of the sensitivities of the optimal barriers and the influence of regime-switching."

In this note we generalise the Phillips theorem on the subordination of Feller processes by Levy subordinators to the class of additive subordinators (i.e. subordinators with independent but possibly nonstationary increments). In the case where the original Feller process is Levy we also express the time-dependent characteristics of the subordinated process in terms of the characteristics of the Levy process and the additive subordinator.

We derive the exact asymptotics of $P(\sup_{u\leq t}X(u) > x)$ if $x$ and $t$ tend to infinity with $x/t$ constant, for a L\'{e}vy process $X$ that admits exponential moments. The proof is based on a renewal argument and a two-dimensional renewal theorem of H\"{o}glund (1990).

We present an explicit solution to the Skorokhod embedding problem for spectrally negative L\'evy processes. Given a process $X$ and a target measure $\mu$ satisfying an explicit admissibility condition we define functions $\f_\pm$ such that the stopping time $T = \inf\{t>0: X_t \in \{-\f_-(L_t), \f_+(L_t)\}\}$ induces $X_T\sim \mu$. We also treat versions of $T$ which take into account the sign of the excursion straddling time $t$. We prove that our stopping times are minimal and we describe criteria under which they are integrable. We compare our solution with the one proposed by Bertoin and Le Jan (1992) and we compute explicitly their general quantities in our setup.
Our method relies on some new explicit calculations relating scale functions and the It\^o excursion measure of $X$. More precisely, we compute the joint law of the maximum and minimum of an excursion away from 0 in terms of the scale function.

In this paper we study the joint ruin problem for two insurance companies that divide between them both claims and premia in some specified proportions (modeling two branches of the same insurance company or an insurance and re-insurance company). Modeling the risk processes of the insurance companies by Cram\'{e}r-Lundberg processes we obtain the Laplace transform in space of the probability that either of the insurance companies is ruined in finite time. Subsequently, for exponentially distributed claims, we derive an explicit analytical expression for this joint ruin probability by explicitly inverting this Laplace transform. We also provide a characterization of the Laplace transform of the joint ruin time.

Consider two insurance companies (or two branches of the same company) that divide between them both claims and premia in some specified proportions. We model the occurrence of claims according to a renewal process. One ruin problem considered is that of the corresponding two-dimensional risk process first leaving the positive quadrant; another is that of entering the negative quadrant. When the claims arrive according to a Poisson process, we obtain a closed form expression for the ultimate ruin probability. In the general case, we analyze the asymptotics of the ruin probability when the initial reserves of both companies tend to infinity under a Cram\'{e}r light-tail assumption on the claim size distribution.

In this paper we consider the problem of pricing a perpetual American put option in an exponential regime-switching L\'{e}vy model. For the case of the (dense) class of phase-type jumps and finitely many regimes we derive an explicit expression for the value function. The solution of the corresponding first passage problem under a state-dependent level rests on a path transformation and a new matrix Wiener-Hopf factorization result for this class of processes.

This paper concerns an optimal dividend distribution problem for an
insurance company which risk process evolves as a spectrally
negative Levy process (in the absence of dividend payments).
The management of the company is assumed to control timing and
size of dividend payments. The objective is to maximize the sum of
the expected cumulative discounted dividend payments received until the moment of ruin and a penalty payment at the moment of ruin which is an increasing function of the size of the shortfall at ruin; in addition, there may be a fixed cost for taking out dividends.
A complete solution is presented to the corresponding stochastic
control problem. It is established that the value-function is the unique stochastic solution and the pointwise smallest stochastic supersolution of the associated HJB equation.  Furthermore, a necessary and sufficient condition is identified for optimality
of a single dividend-band strategy, in terms of a particular Gerber-Shiu function. A number of concrete examples are analyzed.

Consider a spectrally one-sided Levy process X and reflect it at its past infimum I. Call this process Y . For spectrally positive X, Avram et al. [2] found an explicit expression for the law of the first time that Y = X I crosses a finite positive level a. Here we determine the Laplace transform of this crossing time for Y , if X is spectrally negative. Subsequently, we find an expression for the resolvent measure for Y killed upon leaving [0, a]. We determine the exponential decay parameter # for the transition probabilities of Y killed upon leaving [0, a], prove that this killed process is #-positive and specify the #-invariant function and measure. Restricting ourselves to the case where X has absolutely continuous transition probabilities, we also find the quasi-stationary distribution of this killed process. We construct then the process Y confined to [0, a] and proof some properties of this process.

A distorted expectation is a Choquet expectation with respect to the capacity induced by a concave probability distortion. Distorted expectations are encountered in various static settings, in risk theory, mathematical finance and mathematical economics. There are a number of different ways to extend a distorted expectation to a multi-period setting, which are not all time-consistent. One time-consistent extension is to define the non-linear expectation by backward recursion, applying the distorted expectation stepwise, over single periods. In a multinomial random walk model we show that this non-linear expectation is stable when the number of intermediate periods increases to infinity: Under a suitable scaling of the probability distortions and provided that the tick-size and time step-size converge to zero in such a way that the multinomial random walks converge to a Levy process, we show that values of random variables under the multi-period distorted expectations converge to the values under a continuous-time non-linear expectation operator, which may be identified with a certain type of Peng's g-expectation. A coupling argument is given to show that this operator reduces to a classical linear expectation when restricted to the set of pathwise increasing claims. Our results also show that a certain class of g-expectations driven by a Brownian motion and a Poisson random measure may be computed numerically by recursively defined distorted expectations.