69,565 research outputs found
Quantile estimation with adaptive importance sampling
We introduce new quantile estimators with adaptive importance sampling. The
adaptive estimators are based on weighted samples that are neither independent
nor identically distributed. Using a new law of iterated logarithm for
martingales, we prove the convergence of the adaptive quantile estimators for
general distributions with nonunique quantiles thereby extending the work of
Feldman and Tucker [Ann. Math. Statist. 37 (1996) 451--457]. We illustrate the
algorithm with an example from credit portfolio risk analysis.Comment: Published in at http://dx.doi.org/10.1214/09-AOS745 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Stochastic finite differences and multilevel Monte Carlo for a class of SPDEs in finance
In this article, we propose a Milstein finite difference scheme for a
stochastic partial differential equation (SPDE) describing a large particle
system. We show, by means of Fourier analysis, that the discretisation on an
unbounded domain is convergent of first order in the timestep and second order
in the spatial grid size, and that the discretisation is stable with respect to
boundary data. Numerical experiments clearly indicate that the same convergence
order also holds for boundary-value problems. Multilevel path simulation,
previously used for SDEs, is shown to give substantial complexity gains
compared to a standard discretisation of the SPDE or direct simulation of the
particle system. We derive complexity bounds and illustrate the results by an
application to basket credit derivatives
Analysis of operational risk of banks – catastrophe modelling
Nowadays financial institutions due to regulation and internal motivations care more intensively
on their risks. Besides previously dominating market and credit risk new trend is to handle operational risk systematically. Operational risk is the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. First we show the basic features of operational risk and its modelling and regulatory approaches, and after we will analyse
operational risk in an own developed simulation model framework. Our approach is based on the
analysis of latent risk process instead of manifest risk process, which widely popular in risk
literature. In our model the latent risk process is a stochastic risk process, so called Ornstein-
Uhlenbeck process, which is a mean reversion process. In the model framework we define catastrophe as breach of a critical barrier by the process. We analyse the distributions of catastrophe frequency, severity and first time to hit, not only for single process, but for dual process as well. Based on our first results we could not falsify the Poisson feature of frequency, and long tail feature of severity. Distribution of “first time to hit” requires more sophisticated analysis. At the end of paper we examine advantages of simulation based forecasting, and finally we concluding with the possible, further research directions to be done in the future
Estimating the Counterparty Risk Exposure by using the Brownian Motion Local Time
In recent years, the counterparty credit risk measure, namely the default
risk in \emph{Over The Counter} (OTC) derivatives contracts, has received great
attention by banking regulators, specifically within the frameworks of
\emph{Basel II} and \emph{Basel III.} More explicitly, to obtain the related
risk figures, one has first obliged to compute intermediate output functionals
related to the \emph{Mark-to-Market} (MtM) position at a given time T being a positive, and finite, time horizon. The latter implies an
enormous amount of computational effort is needed, with related highly time
consuming procedures to be carried out, turning out into significant costs. To
overcome latter issue, we propose a smart exploitation of the properties of the
(local) time spent by the Brownian motion close to a given value
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