26,969 research outputs found
Pricing Exotic Options in a Path Integral Approach
In the framework of Black-Scholes-Merton model of financial derivatives, a
path integral approach to option pricing is presented. A general formula to
price European path dependent options on multidimensional assets is obtained
and implemented by means of various flexible and efficient algorithms. As an
example, we detail the cases of Asian, barrier knock out, reverse cliquet and
basket call options, evaluating prices and Greeks. The numerical results are
compared with those obtained with other procedures used in quantitative finance
and found to be in good agreement. In particular, when pricing at-the-money and
out-of-the-money options, the path integral approach exhibits competitive
performances.Comment: 21 pages, LaTeX, 3 figures, 6 table
Sequential Monte Carlo Methods for Option Pricing
In the following paper we provide a review and development of sequential
Monte Carlo (SMC) methods for option pricing. SMC are a class of Monte
Carlo-based algorithms, that are designed to approximate expectations w.r.t a
sequence of related probability measures. These approaches have been used,
successfully, for a wide class of applications in engineering, statistics,
physics and operations research. SMC methods are highly suited to many option
pricing problems and sensitivity/Greek calculations due to the nature of the
sequential simulation. However, it is seldom the case that such ideas are
explicitly used in the option pricing literature. This article provides an
up-to date review of SMC methods, which are appropriate for option pricing. In
addition, it is illustrated how a number of existing approaches for option
pricing can be enhanced via SMC. Specifically, when pricing the arithmetic
Asian option w.r.t a complex stochastic volatility model, it is shown that SMC
methods provide additional strategies to improve estimation.Comment: 37 Pages, 2 Figure
Pricing and Hedging Asian Basket Options with Quasi-Monte Carlo Simulations
In this article we consider the problem of pricing and hedging
high-dimensional Asian basket options by Quasi-Monte Carlo simulation. We
assume a Black-Scholes market with time-dependent volatilities and show how to
compute the deltas by the aid of the Malliavin Calculus, extending the
procedure employed by Montero and Kohatsu-Higa (2003). Efficient
path-generation algorithms, such as Linear Transformation and Principal
Component Analysis, exhibit a high computational cost in a market with
time-dependent volatilities. We present a new and fast Cholesky algorithm for
block matrices that makes the Linear Transformation even more convenient.
Moreover, we propose a new-path generation technique based on a Kronecker
Product Approximation. This construction returns the same accuracy of the
Linear Transformation used for the computation of the deltas and the prices in
the case of correlated asset returns while requiring a lower computational
time. All these techniques can be easily employed for stochastic volatility
models based on the mixture of multi-dimensional dynamics introduced by Brigo
et al. (2004).Comment: 16 page
Hedged Monte-Carlo: low variance derivative pricing with objective probabilities
We propose a new `hedged' Monte-Carlo (HMC) method to price financial
derivatives, which allows to determine simultaneously the optimal hedge. The
inclusion of the optimal hedging strategy allows one to reduce the financial
risk associated with option trading, and for the very same reason reduces
considerably the variance of our HMC scheme as compared to previous methods.
The explicit accounting of the hedging cost naturally converts the objective
probability into the `risk-neutral' one. This allows a consistent use of purely
historical time series to price derivatives and obtain their residual risk. The
method can be used to price a large class of exotic options, including those
with path dependent and early exercise features.Comment: LaTeX, 10 pp, 3 eps figures (in text
The History of the Quantitative Methods in Finance Conference Series. 1992-2007
This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.
Convenient Multiple Directions of Stratification
This paper investigates the use of multiple directions of stratification as a
variance reduction technique for Monte Carlo simulations of path-dependent
options driven by Gaussian vectors. The precision of the method depends on the
choice of the directions of stratification and the allocation rule within each
strata. Several choices have been proposed but, even if they provide variance
reduction, their implementation is computationally intensive and not applicable
to realistic payoffs, in particular not to Asian options with barrier.
Moreover, all these previously published methods employ orthogonal directions
for multiple stratification. In this work we investigate the use of algorithms
producing convenient directions, generally non-orthogonal, combining a lower
computational cost with a comparable variance reduction. In addition, we study
the accuracy of optimal allocation in terms of variance reduction compared to
the Latin Hypercube Sampling. We consider the directions obtained by the Linear
Transformation and the Principal Component Analysis. We introduce a new
procedure based on the Linear Approximation of the explained variance of the
payoff using the law of total variance. In addition, we exhibit a novel
algorithm that permits to correctly generate normal vectors stratified along
non-orthogonal directions. Finally, we illustrate the efficiency of these
algorithms in the computation of the price of different path-dependent options
with and without barriers in the Black-Scholes and in the Cox-Ingersoll-Ross
markets.Comment: 21 pages, 11 table
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