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We provide results on the existence and uniqueness of equilibrium in dynamically incomplete financial markets in discrete time. Our framework allows for heterogeneous agents, unspanned random endowments and convex trading constraints. In the special case where all agents have preferences of the same type and all random endowments are replicable by trading in the financial market we show that a one-fund theorem holds and give an explicit expression for the equilibrium pricing kernel. If the underlying noise is generated by finitely many Bernoulli random walks, the equilibrium dynamics can be described by a system of coupled backward stochastic difference equations, which in the continuous-time limit becomes a multi-dimensional backward stochastic differential equation. If the market is complete in equilibrium, the system of equations decouples, but if not, one needs to keep track of the prices and continuation values of all agents to solve it. As an example we simulate option prices in the presence of stochastic volatility, demand pressure and short-selling constraints.
We consider a class of generalized capital asset pricing models in continuous time with a finite number of agents and tradable securities. The securities may not be sufficient to span all sources of uncertainty. If the agents have exponential utility functions and the individual endowments are spanned by the securities, an equilibrium exists and the agents’ optimal trading strategies are constant. Affine processes, and the theory of information-based asset pricing are used to model the endogenous asset price dynamics and the terminal payoff. The derived semi-explicit pricing formulae are applied to numerically analyze the impact of the agents’ risk aversion on the implied volatility of simultaneously-traded European-style options.
In the paradigm of VON N EUMANN AND M ORGENSTERN, a representation of affine pref-
erences in terms of an expected utility can be obtained under the assumption of weak continu-
ity. Since the weak topology is coarse, this requirement is a priori far from being negligible.
In this work, we replace the assumption of weak continuity by monotonicity. More precisely,
on the space of lotteries on an interval of the real line, it is shown that any affine preference
order which is monotone with respect to the first stochastic order admits a representation in
terms of an expected utility for some nondecreasing utility function. As a consequence, any
affine preference order on the subset of lotteries with compact support, which is monotone
with respect to the second stochastic order, can be represented in terms of an expected util-
ity for some nondecreasing concave utility function. We also provide such representations
for affine preference orders on the subset of those lotteries which fulfill some integrability
conditions. The subtleties of the weak topology are illustrated by some examples.
We consider backward stochastic differential equations with drivers of quadratic growth (qgBSDE). We prove several statements concerning path regularity and stochastic smooth-
ness of the solution processes of the qgBSDE, in particular we prove an extension of Zhang's path regularity theorem to the quadratic growth setting. We give explicit convergence rates for the difference between the solution of a qgBSDE and its truncation, filling an important gap in numerics for qgBSDE. We give an alternative proof of second order Malliavin differentiability for BSDE with drivers that are Lipschitz continuous (and differentiable), and
then derive the same result for qgBSDE.
We consider the problem of numerical approximation for forward-backward stochastic
differential equations with drivers of quadratic growth (qgFBSDE). To illustrate the significance
of qgFBSDE, we discuss a problem of cross hedging of an insurance related financial
derivative using correlated assets. For the convergence of numerical approximation schemes for
such systems of stochastic equations, path regularity of the solution processes is instrumental.
We present a method based on the truncation of the driver, and explicitly exhibit error estimates
as functions of the truncation height. We discuss a reduction method to FBSDE with globally
Lipschitz continuous drivers, by using the Cole-Hopf exponential transformation. We finally
illustrate our numerical approximation methods by giving simulations for prices and optimal
hedges of simple insurance derivatives.
The LIBOR market model is very popular for pricing inter-
est rate derivatives, but is known to have several pitfalls. In addition, if
the model is driven by a jump process, then the complexity of the drift
term is growing exponentially fast (as a function of the tenor length). In
this work, we consider a Levy-driven LIBOR model and aim at developing accurate and efficient log-Levy approximations for the dynamics of
the rates. The approximations are based on truncation of the drift term
and Picard approximation of suitable processes. Numerical experiments
for FRAs, caps and swaptions show that the approximations perform
very well. In addition, we also consider the log-Levy approximation of
annuities, which offers good approximations for high volatility regimes.
Optimal dual martingales, their analysis and application to new algorithms for Bermudan products
(2012)
In this paper we introduce and study the concept of optimal and surely
optimal dual martingales in the context of dual valuation of Bermudan
options, and outline the development of new algorithms in this context.
We provide a characterization theorem, a theorem which gives conditions
for a martingale to be surely optimal, and a stability theorem concerning martingales which are near to be surely optimal in a sense. Guided
by these results we develop a framework of backward algorithms for constructing such a martingale. In turn this martingale may then be utilized
for computing an upper bound of the Bermudan product. The methodology is pure dual in the sense that it doesn't require certain (input)
approximations to the Snell envelope.
In an Ito-Levy environment we outline a particular regression based
backward algorithm which allows for computing dual upper bounds with-
out nested Monte Carlo simulation. Moreover, as a by-product this algorithm also provides approximations to the continuation values of the
product, which in turn determine a stopping policy. Hence, we may obtain lower bounds at the same time.
In a first numerical study we demonstrate a backward dual regression algorithm in a Wiener environment that is easy to implement and
is regarding accuracy comparable with the method of Belomestny et. al.
(2009).
With an emphasis on generators with quadratic growth in the control variable we consider
measure solutions of BSDE, a solution concept corresponding to the notion of risk neutral
measure in mathematical finance. In terms of measure solutions, solving a BSDE reduces
to martingale representation with respect to an underlying filtration. Measure solutions
related to measures equivalent to the historical one provide classical solutions. We derive
the existence of measure solutions in scenarios in which the generating functions are just
continuous, of at most linear growth in the control variable (corresponding to generators of
at most quadratic growth in the usual sense), and with a random bound in the time parameter
whose stochastic integral is a BMO martingale. Our main tools include a stability property
of sequences of measure solutions, for which a limiting solution is obtained by means of the
weak convergence of measures.
Particle methods have become indispensible in conformation dynamics to compute transition rates in protein folding, binding processes and molecular design, to mention a few. Conformation dynamics requires at a decomposition of a molecule's position space into metastable conformations. In this paper, we show how this decomposition can be obtained via the design of either ``soft'' or ``hard'' molecular conformations. We show, that the soft approach results in a larger metastabilitiy of the decomposition and is thus more advantegous. This is illustrated by a simulation of Alanine Dipeptide.
This paper deals with the computation of regular coderivatives of
solution maps associated with a frequently arising class of generalized equations.
The constraint sets are given by (not necessarily convex) inequalities,
and we do not assume linear independence of gradients to active constraints.
The achieved results enable us to state several versions of sharp necessary optimality
conditions in optimization problems with equilibria governed by such
generalized equations. The advantages are illustrated by means of examples.
The article investigates the relation between global solutions of hyperbolic balance laws and viscous balance laws on the circle. It is thematically located at the crossroads of hyperbolic and parabolic partial differential equations with one-dimensional space variable and periodic boundary conditions. The two equations are given by:
u_t+f(u)_x=g(u)
and
u_t+f(u)_x=e u_{xx}+g(u).
The main result of the paper corrects a result on the persistence of heteroclinic connections by Fan and Hale from 1995 when viscosity vanishes: The "Connection Lemma" states that a connection can only persist if the zero number of the source state is a multiple of the zero number of the target state. The "Cascading Theorem" then yields convergence of heteroclinic connections to a sequence of heteroclinic connections and stationary solutions in case of non-persistence.
In addition a full description of the connection problem of rotating waves on the parabolic attractor is given.
A capillary surface in a negative gravitational field describes the shape of the surface of a hanging drop in a capillary tube with wetting material on the bottom. Mathematical modeling leads to the volume- and obstacle-constrained minimization of a nonconvex nonlinear energy functional of mean curvature type which is unbounded from below. In 1984 Huisken proved the existence and regularity of local minimizers of this energy under the condition on gravitation being sufficiently weak. We prove convergence of a first order finite element approximation of these minimizers. Numerical results demonstrating the theoretic convergence order are given.
Flows over time and generalized flows are two advanced network flow models of utmost importance, as they incorporate two crucial features occurring in numerous real-life networks. Flows over time feature time as a problem dimension and allow to realistically model the fact that commodities (goods, information, etc.) are routed through a network over time. Generalized flows allow for gain/loss factors on the arcs that model physical transformations of a commodity due to leakage, evaporation, breeding, theft, or interest rates. Although the latter effects are usually time-bound, generalized flow models featuring a temporal dimension have never been studied in the literature.
In this paper we introduce the problem of computing a generalized maximum flow over time in networks with both gain factors and transit times on the arcs. While generalized maximum flows and maximum flows over time can be computed efficiently, our combined problem turns out to be NP-hard and even completely non-approximable. A natural special case is given by lossy networks where the loss rate per time unit is identical on all arcs. For this case we present a (practically efficient) FPTAS that also reveals a surprising connection to so-called earliest arrival flows.
We consider a basic subproblem which arises in line planning,
and is of particular importance in the context of a high system
load or robustness: How much can be routed maximally along all possible
lines? The essence of this problem is the Path Constrained Network
Flow (PCN) problem. We explore the complexity of this problem and
its dual. In particular we show for the primal that it is as hard to
approximate as MAX CLIQUE and for the dual that it is as hard to
approximate as SET COVER. We also prove that the PCN problem is
hard for special graph classes, interesting both from a complexity and
from a practical perspective. Finally, we present a special graph class
for which there is a polynomial-time algorithm.
We consider a sorting problem from railway optimization
called train classification: incoming trains are split up into their single
cars and reassembled to form new outgoing trains. Trains are subject
to delay, which may turn a prepared sorting schedule infeasible for the
disturbed situation. The classification methods applied today deal with
this issue by completely disregarding the input order of cars, which provides
robustness against any amount of disturbance but also wastes the
potential contained in the a priori knowledge about the input.
We introduce a new method that provides a feasible sorting schedule for
the expected input and allows to
flexibly insert additional sorting steps
if the schedule has become infeasible after revealing the disturbed input.
By excluding disruptions that almost never occur from our consideration,
we obtain a classification process that is quicker than the current railway
practice but still provides robustness against realistic delays. In fact, our
algorithm allows
flexibly trading off fast classification against high degrees
of robustness depending on the respective need. We further explore
this
flexibility in experiments on real-world traffic data, underlining our
algorithm improves on the methods currently applied in practice.
The knapsack problem is one of the basic problems in combinatorial optimization. In real-world applications it is often part of a more complex problem. Examples are machine capacities in production planning or bandwidth restrictions in telecommunication network design. Due to unpredictable future settings or erroneous data, parameters of such a subproblem are subject to uncertainties.
In high risk situations a robust approach should be chosen to deal with these uncertainties.
Unfortunately, classical robust optimization outputs solutions with little profit by prohibiting any adaption of the solution when the actual realization of the uncertain parameters is known.
This ignores the fact that in most settings minor changes to a previously determined solution are possible. To overcome these drawbacks we allow a limited recovery of a previously fixed item set as soon as the data are known by deleting at most k items and adding up to l new items.
We consider the complexity status of this recoverable robust knapsack problem and extend the classical concept of cover inequalities to obtain stronger polyhedral descriptions. Finally, we present two extensive computational studies to investigate the influence of parameters k and l to the objective and evaluate the effectiveness of our new class of valid inequalities.
In this paper, we investigate the recoverable robust knapsack problem,
where the uncertainty of the item weights follows the approach of Bertsimas and
Sim. In contrast to the robust approach, a limited recovery action is allowed,
i.e., up to k items may be removed when the actual weights are known. This problem
is motivated by the assignment of traffic nodes to antennas in wireless network
planning. Starting from an exponential min-max optimization model, we derive an
integer linear programming formulation of quadratic size. In a preliminary computational
study, we evaluate the gain of recovery using realistic planning data.
In this paper we investigate two different recoverable robust models to deal with cost uncertainties in a shortest path problem. Recoverable robustness extends the classical concept of robustness to deal with uncertainties by incorporating limited recovery actions after the
full data are revealed. Our first model focuses on the case where the recovery actions are quite restricted: after a simple path is fixed in the first stage, in the second stage, after all data are revealed, any path containing at most k new arcs may be chosen.
Thus, the parameter k can be interpreted as a mediator between
robust optimization - no changes allowed - and optimization
on the fly - an arbitrary solution can be chosen. Considering three
classical scenario sets, which model uncertainties in the cost function,
we show that this new problem is strongly NP-hard in all
these cases and is not approximable, unless P=NP.
This is in contrast to the robust shortest path problem, where, for
example, an optimal solution can be computed efficiently for interval
and Gamma-scenarios. For series-parallel graphs and interval scenarios,
we present a polynomial time algorithm for this recoverable robust
setting.
In our second model the recovery set, i.e., the set of paths selectable
in the second stage is not limited, but deviating from the previous
choice comes at extra cost. Thus, a path chosen in the first stage
produces renting costs modeled as an alpha-fraction of the scenario
cost. For an arc taken in the second stage the remaining cost needs
to be paid in addition to some extra inflation cost modeled by a beta-fraction
of the scenario cost, if the arc was not reserved beforehand. The
complexity status of this problem is similar to the robust case. Yet,
for Gamma-scenarios the problem is again strongly NP-hard,
but can be approximated.