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In this paper, we study the dual representation for generalized multiple stopping problems,
hence the pricing problem of general multiple exercise options. We derive a dual representation which allows for cashflows which are subject to volume constraints modeled by
integer valued adapted processes and refraction periods modeled by stopping times. As
such, this extends the works by Schoenmakers (2010), Bender (2011a), Bender (2011b),
Aleksandrov and Hambly (2010), and Meinshausen and Hambly (2004) on multiple exercise
options, which either take into consideration a refraction period or volume constraints, but
not both simultaneously. We also allow more flexible cashflow structures than the additive
structure in the above references. For example some exponential utility problems are covered
by our setting. We supplement the theoretical results with an explicit Monte Carlo algorithm
for constructing confidence intervals for the price of multiple exercise options and exemplify
it by a numerical study on the pricing of a swing option in an electricity market.
We present a generic non-nested Monte Carlo procedure for computing true upper bounds for Bermudan products, given an approximation of the Snell envelope. The pleonastic ``true'' stresses that, by construction, the estimator is biased above the Snell envelope. The key idea is a regression estimator for the Doob martingale part of the approximative Snell envelope, which preserves the martingale property. The so constructed martingale may be employed for computing dual upper bounds without nested simulation. In general, this martingale can also be used as a control variate for simulation of conditional expectations. In this context, we develop a variance reduced version of the nested primal-dual estimator (Anderson & Broadie (2004)) and nested consumption based (Belomestny & Milstein (2006)) methods . Numerical experiments indicate the efficiency of the non-nested Monte Carlo algorithm and the variance reduced nested one.
In this paper we lay the foundation for a numerical algorithm to
simulate high-dimensional coupled FBSDEs under weak coupling or
monotonicity conditions. In particular we prove convergence of a
time discretization and a Markovian iteration. The iteration
differs from standard Picard iterations for FBSDEs in that the
dimension of the underlying Markovian process does not increase
with the number of iterations. This feature seems to be
indispensable for an efficient iterative scheme from a numerical
point of view. We finally suggest a fully explicit numerical
algorithm and present some numerical examples with up to
10-dimensional state space.
We show that pricing a big class of relevant options by hedging
and no-arbitrage can be extended beyond semimartingale models. To
this end we construct a subclass of self-financing portfolios that
contains hedges for these options, but does not contain arbitrage
opportunities, even if the stock price process is a
non-semimartingale of some special type.
Moreover, we show that the option prices depend
essentially only on a path property of the stock price process,
viz. on the quadratic variation. As a consequence, we can
incorporate many stylized facts to a pricing model without
changing the option prices.
We introduce a forward scheme to simulate backward SDEs. Compared
to existing schemes, we avoid high order nestings of conditional
expectations backwards in time. In this way the error, when
approximating the conditional expectation, in dependence of the
time partition is significantly reduced. Besides this generic
result, we present an implementable algorithm and provide an error
analysis for it. Finally, we demonstrate the strength of the new
algorithm by solving some financial problems numerically.
In Kolodko & Schoenmakers (2004) and Bender & Schoenmakers (2004) a policy iteration was introduced which allows to achieve tight lower approximations of the price for early exercise options via a nested Monte-Carlo simulation in a Markovian setting. In this paper we enhance the algorithm by a scenario selection method. It is demonstrated by numerical examples that the scenario selection can significantly reduce the number of actually performed inner simulations, and thus can heavily speed up the method (up to factor 10 in some examples). Moreover, it is shown that the modified algorithm retains the desirable properties of the original one such as the monotone improvement property, termination after a finite number of iteration steps, and numerical stability.
We propose a valuation method for callable structures in a multi-factor Libor model which are path-dependent in the sense that, after calling, one receives a sequence of cash-flows in the future, instead of a well specified cash-flow at the calling date. The method is based on a Monte Carlo procedure for standard Bermudans recently developed in Kolodko & Schoenmakers (2004), and is applied to the cancelable snowball interest rate swap. The proposed procedure is quite generic, straightforward to implement, and can be easily adapted to other related path-dependent products.
We present a new iterative procedure for solving the multiple stopping
problem in discrete time and discuss the stability of the algorithm.
The algorithm produces monotonically increasing approximations of the
Snell envelope, which coincide with the Snell envelope after finitely many
steps. Contrary to backward dynamic programming, the algorithm allows
to calculate approximative solutions with only a few nestings of conditional
expectations and is, therefore, tailor-made for a plain Monte-Carlo
implementation.
We apply theoretical results of S. Peng on supersolutions for BS-DEs
to the problem of finding optimal superhedging strategies in a
Black-Scholes market under constraints. Constraints may be imposed
simultaneously on wealth process and portfolio. They may be nonconvex,
time-dependent, and random. Constraints on the portfolio may
e.g. be formulated in terms of the amount of money invested, the portfolio
proportion, or the number of shares held.