211 research outputs found
A pricing measure to explain the risk premium in power markets
In electricity markets, it is sensible to use a two-factor model with mean
reversion for spot prices. One of the factors is an Ornstein-Uhlenbeck (OU)
process driven by a Brownian motion and accounts for the small variations. The
other factor is an OU process driven by a pure jump L\'evy process and models
the characteristic spikes observed in such markets. When it comes to pricing, a
popular choice of pricing measure is given by the Esscher transform that
preserves the probabilistic structure of the driving L\'evy processes, while
changing the levels of mean reversion. Using this choice one can generate
stochastic risk premiums (in geometric spot models) but with
(deterministically) changing sign. In this paper we introduce a pricing change
of measure, which is an extension of the Esscher transform. With this new
change of measure we also can slow down the speed of mean reversion and
generate stochastic risk premiums with stochastic non constant sign, even in
arithmetic spot models. In particular, we can generate risk profiles with
positive values in the short end of the forward curve and negative values in
the long end. Finally, our pricing measure allows us to have a stationary spot
dynamics while still having randomly fluctuating forward prices for contracts
far from maturity.Comment: 37 pages, 7 figure
Particle Representation for the Solution of the Filtering Problem. Application to the Error Expansion of Filtering Discretizations
We introduce a weighted particle representation for the solution of the filtering problem based on a suitably chosen variation of the classical de Finetti theorem. This representation has important theoretical and numerical applications. In this paper, we explore some of its theoretical con- sequences. The first is to deduce the equations satisfied by the solution of the filtering problem in three different frameworks: the signal independent Brownian measurement noise model, the spatial observations with additive white noise model and the cluster detection model in spatial point processes. Secondly we use the representation to show that a suitably chosen filtering discretisation converges to the filtering solution. Thirdly we study the leading error coefficient for the discretisation. We show that it satisfies a stochastic partial differential equation by exploiting the weighted particle representation for both the approximation and the limiting filtering solution
Optimal simulation schemes for L\'evy driven stochastic differential equations
We consider a general class of high order weak approximation schemes for
stochastic differential equations driven by L\'evy processes with infinite
activity. These schemes combine a compound Poisson approximation for the jump
part of the L\'evy process with a high order scheme for the Brownian driven
component, applied between the jump times. The overall approximation is
analyzed using a stochastic splitting argument. The resulting error bound
involves separate contributions of the compound Poisson approximation and of
the discretization scheme for the Brownian part, and allows, on one hand, to
balance the two contributions in order to minimize the computational time, and
on the other hand, to study the optimal design of the approximating compound
Poisson process. For driving processes whose L\'evy measure explodes near zero
in a regularly varying way, this procedure allows to construct discretization
schemes with arbitrary order of convergence
PFEM application in fluid structure interaction problems
In the current paper the Particle Finite Element Method (PFEM), an inno-vative numerical method for solving a wide spectrum of problems involving the interaction of ļ¬uid and structures, is brieļ¬y presented. Many examples of the use of the PFEM with GiD support are shown. GiD framework provides a useful pre and post processor for the speciļ¬c features of the method. Its advantages and shortcomings are pointed out in the present wor
Variance and interest rate risk in unit-linked insurance policies
One of the risks derived from selling long term policies that any insurance
company has, arises from interest rates. In this paper we consider a general
class of stochastic volatility models written in forward variance form. We also
deal with stochastic interest rates to obtain the risk-free price for
unit-linked life insurance contracts, as well as providing a perfect hedging
strategy by completing the market. We conclude with a simulation experiment,
where we price unit-linked policies using Norwegian mortality rates. In
addition we compare prices for the classical Black-Scholes model against the
Heston stochastic volatility model with a Vasicek interest rate model.Comment: 21 pages, 7 figure
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