Parameter estimation risk in asset pricing and risk management: A Bayesian approach

Tunaru, Radu; Zheng, Teng

doi:10.1016/j.irfa.2017.08.004

Cited by 16 publications

(10 citation statements)

References 49 publications

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“…By choosing a certain consistency threshold T> 0, we can assess the consistency of experts. The consistency threshold is selected on the basis of rational considerations and hypothetical capabilities of experts [20], [24].…”

Section: Methodsmentioning

confidence: 99%

Diagnostics of the State of Safety-Oriented Enterprise Management System Using Neural Networks

Havlovska¹,

Koptieva²,

Babchynska³

et al. 2022

TEM Journal

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Enterprise management is based on the need to make and justify management decisions that contribute to its development. It is almost impossible to determine the risk of a particular managerial decision, and excessive risk in the implementation of individual projects can lead to loss of business. Therefore, management faces the need to find a balance between benefits and risks, at which, on the one hand, it will be possible to develop a company and, on the other hand, adhere to postulates of safetyoriented management. Since management decisions cannot be foreseen for all possible situations and combinations of risk-benefit ratios, a universal model is proposed. It implies a golden ratio, depending on the limited number of current conditions, that would satisfy an enterprise management from the standpoint of sufficient justification on a decision. The article proposes a probabilistic neural network architecture and Matlab parameters of a probabilistic neural network for diagnosing the states of a safety-oriented control system. The proposed model in the form of a probabilistic neural network generates a response to input data on previous month under estimation, and forms an optimal state for a next month.

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Section: Methodsmentioning

confidence: 99%

Diagnostics of the State of Safety-Oriented Enterprise Management System Using Neural Networks

Havlovska¹,

Koptieva²,

Babchynska³

et al. 2022

TEM Journal

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show abstract

“…The Bayesian inference approach is widely used with great successes in various real-world problems. In particular, by recently the development of analytical techniques such as Markov chain Monte Carlo methods (MCMC) (for detail, see [18,19]), it has found application in a wide range of activities such as quantitative finance, stochastic epidemic, biometrics, remote sensing, heat conductivity, sesmic inversion, machine learning [20][21][22][23][24][25][26][27][28][29].…”

Section: Bayesian Inference Approach For Estimating Parametersmentioning

confidence: 99%

Estimating Parameters in Mathematical Model for Societal Booms through Bayesian Inference Approach

Ota¹,

Mizutani²

2020

MCA

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In this study, based on our previous study in which the proposed model is derived based on the SIR model and E. M. Rogers’s Diffusion of Innovation Theory, including the aspects of contact and time delay, we examined the mathematical properties, especially the stability of the equilibrium for our proposed mathematical model. By means of the results of the stability in this study, we also used actual data representing transient and resurgent booms, and conducted parameter estimation for our proposed model using Bayesian inference. In addition, we conducted a model fitting to five actual data. By this study, we reconfirmed that we can express the resurgences or minute oscillations of actual data by means of our proposed model.

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“…Owing to the recent developments in Bayesian inference work, including Bayesian inference approach by efficient sampling methods such as Markov Chain Monte Carlo (MCMC), we can apply the Bayesian inference technique to inverse problems in remote sensing [11], seismic inversion [21], heat conduction problems [29], [30] and various other realworld problems. Moreover, several prior publications such as [6,14,15,28,27] are related to option pricing based on Bayesian inference. In those publications, the option prices are usually computed by using the analytical solution (or so-called Black-Scholes formula) or applying of Monte Carlo simulation of original stochastic differential equation under an assumption which the volatility is constant.…”

Section: Introductionmentioning

confidence: 99%

Parameters identification for an inverse problem arising from a binary option using a Bayesian inference approach

Ota¹,

Yu²,

Maki³

2022

Preprint

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No-arbitrage property provides a simple method for pricing financial derivatives. However, arbitrage opportunities exist among different markets in various fields, even for a very short time. By knowing that an arbitrage property exists, we can adopt a financial trading strategy. This paper investigates the inverse option problems (IOP) in the extended Black-Scholes model. We identify the model coefficients from the measured data and attempt to find arbitrage opportunities in different financial markets using a Bayesian inference approach, which is presented as an IOP solution. The posterior probability density function of the parameters is computed from the measured data. The statistics of the unknown parameters are estimated by a Markov Chain Monte Carlo (MCMC) algorithm, which exploits the posterior state space. The efficient sampling strategy of the MCMC algorithm enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown trend and volatility coefficients from the measured data.

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Parameter estimation risk in asset pricing and risk management: A Bayesian approach

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 16 publications

References 49 publications

Diagnostics of the State of Safety-Oriented Enterprise Management System Using Neural Networks

Diagnostics of the State of Safety-Oriented Enterprise Management System Using Neural Networks

Estimating Parameters in Mathematical Model for Societal Booms through Bayesian Inference Approach

Parameters identification for an inverse problem arising from a binary option using a Bayesian inference approach

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