A Survey of Sequential Monte Carlo Methods for Economics and Finance

Creal, Drew

doi:10.1080/07474938.2011.607333

Cited by 245 publications

(197 citation statements)

References 140 publications

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“…[40], [20], [16], and their theoretical properties have been extensively studied in [13], [16], [11], [30]. For a recent survey in the topic, with focus on economics, finance and insurance applications the reader is referred to [12] and [18]. For an application to rare events in a financial context, see [10].…”

Section: Sequential Monte Carlo Methods (Smc)mentioning

confidence: 99%

Sequential Monte Carlo Samplers for Capital Allocation Under Copula-Dependent Risk Models

2014

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In this paper we assume a multivariate risk model has been developed for a portfolio and its capital derived as a homogeneous risk measure. The Euler (or gradient) principle, then, states that the capital to be allocated to each component of the portfolio has to be calculated as an expectation conditional to a rare event, which can be challenging to evaluate in practice. We exploit the copuladependence within the portfolio risks to design a Sequential Monte Carlo Samplers based estimate to the marginal conditional expectations involved in the problem, showing its efficiency through a series of computational examples.

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Section: Sequential Monte Carlo Methods (Smc)mentioning

confidence: 99%

Sequential Monte Carlo Samplers for Capital Allocation Under Copula-Dependent Risk Models

2014

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“…In the sequel we provide a brief overview of a class of Monte Carlo methods, named Sequential Monte Carlo (SMC). For a recent survey in the topic, with focus on economics, finance and insurance applications the reader is referred to Creal (2012) and Del Moral et al (2013). For a generic introductory review we refer the reader to Doucet and Johansen (2009)…”

Section: Smc Samplers and Capital Allocationmentioning

confidence: 99%

Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks

2017

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Abstract:The main objective of this work is to develop a detailed step-by-step guide to the development and application of a new class of efficient Monte Carlo methods to solve practically important problems faced by insurers under the new solvency regulations. In particular, a novel Monte Carlo method to calculate capital allocations for a general insurance company is developed, with a focus on coherent capital allocation that is compliant with the Swiss Solvency Test. The data used is based on the balance sheet of a representative stylized company. For each line of business in that company, allocations are calculated for the one-year risk with dependencies based on correlations given by the Swiss Solvency Test. Two different approaches for dealing with parameter uncertainty are discussed and simulation algorithms based on (pseudo-marginal) Sequential Monte Carlo algorithms are described and their efficiency is analysed.

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“…A large body of literature on particle filter has been produced. Interested readers can see Creal (2012) for a review. For the filtering step, we adapt the classical bootstrap filter (Gordon et al 1993), which utilizes the transition density to generate random draws.…”

Section: Estimation and Inferencementioning

confidence: 99%

A Particle Filter Approach to Multiprocess Dynamic Models with Application to Hormone Data

Liu

2015

Stat Biosci

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We extend the multiprocess dynamic models to the general non-Gaussian and nonlinear setting. Under this framework, we propose specific models to simultaneously model hormone smooth basal trend and pulsatile activities. The pulse input is modeled by two processes: one as a point mass at zero and one as a gamma distributed random variable. This gamma-driven approach ensures the pulse estimates to be nonnegative, which is an intrinsic characteristic of hormone dynamics. The smooth trend is modeled by smoothing splines. Both additive and multiplicative observational errors are investigated. Parameters are estimated by maximizing the marginal likelihood. Baseline and pulses are estimated by posterior means. For implementation, particle filter is adopted. Unlike the traditional condensation method where a single distribution is used to approximate a mixture of distributions, this particle filter approach allows the model components to be accurately evaluated at the expense of computational resources. The specific models are applied to a cortisol series. The finite sample performance is evaluated by a simulation. The data application and the simulation show that the biological characteristics can be incorporated and be accurately estimated under the proposed framework.

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A Survey of Sequential Monte Carlo Methods for Economics and Finance

Cited by 245 publications

References 140 publications

Sequential Monte Carlo Samplers for Capital Allocation Under Copula-Dependent Risk Models

Sequential Monte Carlo Samplers for Capital Allocation Under Copula-Dependent Risk Models

Bayesian Modelling, Monte Carlo Sampling and Capital Allocation of Insurance Risks

A Particle Filter Approach to Multiprocess Dynamic Models with Application to Hormone Data

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