methodology and computing in applied probability

Methodology and Computing in Applied Probability

Subject Area and Category

• Mathematics (miscellaneous)
• Statistics and Probability

Springer Netherlands

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Methodology And Computing In Applied Probability Latest Publications

Total documents, published by springer-verlag.

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Asymptotic Finite-Time Ruin Probabilities for a Bidimensional Delay-Claim Risk Model with Subexponential Claims

On the risk of ruin in a sis type epidemic, some expressions of a generalized version of the expected time in the red and the expected area in red, hitting time problems of sticky brownian motion and their applications in optimal stopping and bond pricing, correction to: high-dimensional quadratic classifiers in non-sparse settings, correction to: asymptotic normality for inference on multisample, high-dimensional mean vectors under mild conditions, deep learning for constrained utility maximisation.

AbstractThis paper proposes two algorithms for solving stochastic control problems with deep learning, with a focus on the utility maximisation problem. The first algorithm solves Markovian problems via the Hamilton Jacobi Bellman (HJB) equation. We solve this highly nonlinear partial differential equation (PDE) with a second order backward stochastic differential equation (2BSDE) formulation. The convex structure of the problem allows us to describe a dual problem that can either verify the original primal approach or bypass some of the complexity. The second algorithm utilises the full power of the duality method to solve non-Markovian problems, which are often beyond the scope of stochastic control solvers in the existing literature. We solve an adjoint BSDE that satisfies the dual optimality conditions. We apply these algorithms to problems with power, log and non-HARA utilities in the Black-Scholes, the Heston stochastic volatility, and path dependent volatility models. Numerical experiments show highly accurate results with low computational cost, supporting our proposed algorithms.

Variance Bounding of Delayed-Acceptance Kernels

AbstractA delayed-acceptance version of a Metropolis–Hastings algorithm can be useful for Bayesian inference when it is computationally expensive to calculate the true posterior, but a computationally cheap approximation is available; the delayed-acceptance kernel targets the same posterior as its associated “parent” Metropolis-Hastings kernel. Although the asymptotic variance of the ergodic average of any functional of the delayed-acceptance chain cannot be less than that obtained using its parent, the average computational time per iteration can be much smaller and so for a given computational budget the delayed-acceptance kernel can be more efficient. When the asymptotic variance of the ergodic averages of all $$L^2$$ L 2 functionals of the chain are finite, the kernel is said to be variance bounding. It has recently been noted that a delayed-acceptance kernel need not be variance bounding even when its parent is. We provide sufficient conditions for inheritance: for non-local algorithms, such as the independence sampler, the discrepancy between the log density of the approximation and that of the truth should be bounded; for local algorithms, two alternative sets of conditions are provided. As a by-product of our initial, general result we also supply sufficient conditions on any pair of proposals such that, for any shared target distribution, if a Metropolis-Hastings kernel using one of the proposals is variance bounding then so is the Metropolis-Hastings kernel using the other proposal.

A Numerical Method for Hedging Bermudan Options under Model Uncertainty

Stochastic analysis of rumor spreading with multiple pull operations, export citation format, share document.

Methodology and Computing in Applied Probability

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Impact Factor : 0.900 (based on Web of Science 2022)

• # 57 / 112 (Q3) in Statistics & Probability

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Journal Performance & Insights

6% from 2019

• Impact factor of this journal has increased by 8% in last year.
• This journal’s impact factor is in the top 10 percentile category.
• CiteRatio of this journal has decreased by 6% in last years.
• This journal’s CiteRatio is in the top 10 percentile category.

2% from 2019

• SJR of this journal has decreased by 15% in last years.
• This journal’s SJR is in the top 10 percentile category.
• SNIP of this journal has decreased by 2% in last years.
• This journal’s SNIP is in the top 10 percentile category.

Methodology and Computing in Applied Probability

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Methodology and Computing in Applied Probability will publish high quality research and review articles in the areas of applied probability that emphasize methodology and computing. Of special interest are articles in important areas of applications that include detailed case studies. Applied probability is a broad research area that is of interest to many scientists in diverse disciplines including: anthropology, biology, communication theory, economics, epidemiology, finance, linguistics, meteorology, operations research, psychology, quality control, reliability theory, sociology and statistics.The following alphabetical listing of topics of interest to the journal is not intended to be exclusive but to demonstrate the editorial policy of attracting papers which represent a broad range of interests: AlgorithmsApproximationsAsymptotic Approximations & ExpansionsCombinatorial & Geometric ProbabilityCommunication NetworksExtreme Value TheoryFinanceImage AnalysisInequalitiesInformation TheoryMathematical PhysicsMolecular BiologyMonte Carlo MethodsOrder StatisticsQueuing TheoryReliability TheoryStochastic Processes Read Less

Methodology and Computing in Applied Probability will publish high quality research and review articles in the areas of applied probability that emphasize methodology and computing. Of special interest are articles in important areas of applications that include detailed case ...... Read More

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