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Stochastic Modeling and Statistical Analysis of Financial Data
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Stochastic Modeling and Statistical Analysis of Financial Data

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematics and Finance".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 5317

Special Issue Editors

Prof. Dr. Liqun Wang

E-Mail Website
Guest Editor
Department of Statistics, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Interests: estimation in nonlinear models; measurement error; boundary crossing probability; first passage time; statistical computation
Prof. Dr. Klaus Pötzelberger

E-Mail Website
Guest Editor
Institute for Statistics and Mathematics, Vienna University of Economics and Business, 1020 Vienna, Austria
Interests: mathematical statistics; probability theory; stochastic processes; financial mathematics

Special Issue Information

Dear Colleagues,

Financial statistics is a fast-evolving area of research. Sound stochastic modeling and computational methodologies are crucial for understanding and forecasting financial and economic data. Non-parametric methods and statistical machine learning techniques draw more and more attention in academia, as well as in business and in various industries. Moreover, the availability of big data requires efficient computational methods.

The aim of this Special Issue is to gather papers from leading experts in the area of stochastic modeling and statistical methods and computing for financial data analysis and forecasting. The topics of special interest in this Special Issue include, but are not limited to, the following:

  • Stochastic and econometric modeling and methods;
  • Statistical computation and optimization;
  • Statistical learning and data analytic methods;
  • Barrier option pricing and computing;
  • First-passage time for diffusion processes;
  • Boundary crossing probability and applications.

Dr. Liqun Wang
Prof. Dr. Klaus Pötzelberger
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

12 pages, 412 KiB  
Article
Generalized Method of Moments Estimation of Realized Stochastic Volatility Model
by Luwen Zhang and Li Wang
J. Risk Financial Manag. 2023, 16(8), 377; https://doi.org/10.3390/jrfm16080377 - 16 Aug 2023
Cited by 1 | Viewed by 1663
Abstract
The purpose of this paper is to study the generalized method of moments (GMM) estimation procedures of the realized stochastic volatility model; we give the moment conditions for this model and then obtain the estimation of parameters. Then, we apply these moment conditions [...] Read more.
The purpose of this paper is to study the generalized method of moments (GMM) estimation procedures of the realized stochastic volatility model; we give the moment conditions for this model and then obtain the estimation of parameters. Then, we apply these moment conditions to the realized stochastic volatility model to improve the volatility prediction effect. This paper selects the Shanghai Composite Index (SSE) as the original data of model research and completes the volatility prediction under a realized stochastic volatility model. Markov chain Monte Carlo (MCMC) estimation and quasi-maximum likelihood (QML) estimation are applied to the parameter estimation of the realized stochastic volatility model to compare with the GMM method. And the volatility prediction accuracy of these three different methods is compared. The results of empirical research show that the effect of model prediction using the parameters obtained by the GMM method is close to that of the MCMC method, and the effect is obviously better than that of the quasi-maximum likelihood estimation method. Full article
(This article belongs to the Special Issue Stochastic Modeling and Statistical Analysis of Financial Data)
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29 pages, 409 KiB  
Article
Multitouch Options
by Tristan Guillaume
J. Risk Financial Manag. 2023, 16(6), 300; https://doi.org/10.3390/jrfm16060300 - 14 Jun 2023
Viewed by 1101
Abstract
In this article, the multitouch option, also called the n-touch option (or the “baseball” option when n=3) is analyzed and valued in closed form. This is a kind of barrier option that has been traded for a long [...] Read more.
In this article, the multitouch option, also called the n-touch option (or the “baseball” option when n=3) is analyzed and valued in closed form. This is a kind of barrier option that has been traded for a long time on the markets, but that does not yet admit a known valuation formula. The multitouch option sets a gradual knock-out/knock-in mechanism based on the number of times the underlying asset has crossed a predefined barrier in various time intervals before expiry. The higher the number of predefined time intervals during which the barrier has been touched, the lower the value of a knock-out contract at expiry, and conversely for a knock-in one. Multitouch options can be viewed as an extension of step barrier options, preserving the ability of the latter to adjust the exposure to risk over time, while eliminating the notorious danger of “sudden death” that holders of step barrier options are faced with. They are thus less risky and more flexible than step barrier options, and all the more so when compared to standard barrier options. This article also provides closed-form valuation of multitouch options with nonstandard features such as an outside barrier or a barrier defined as a continuous function of time. Full article
(This article belongs to the Special Issue Stochastic Modeling and Statistical Analysis of Financial Data)
20 pages, 2110 KiB  
Article
On the Kavya–Manoharan–Burr X Model: Estimations under Ranked Set Sampling and Applications
by Osama H. Mahmoud Hassan, Ibrahim Elbatal, Abdullah H. Al-Nefaie and Mohammed Elgarhy
J. Risk Financial Manag. 2023, 16(1), 19; https://doi.org/10.3390/jrfm16010019 - 28 Dec 2022
Cited by 9 | Viewed by 1764
Abstract
A new two-parameter model is proposed using the Kavya–Manoharan (KM) transformation family and Burr X (BX) distribution. The new model is called the Kavya–Manoharan–Burr X (KMBX) model. The statistical properties are obtained, involving the quantile (QU) function, [...] Read more.
A new two-parameter model is proposed using the Kavya–Manoharan (KM) transformation family and Burr X (BX) distribution. The new model is called the Kavya–Manoharan–Burr X (KMBX) model. The statistical properties are obtained, involving the quantile (QU) function, moment (MOs), incomplete MOs, conditional MOs, MO-generating function, and entropy. Based on simple random sampling (SiRS) and ranked set sampling (RaSS), the model parameters are estimated via the maximum likelihood (MLL) method. A simulation experiment is used to compare these estimators based on the bias (BI), mean square error (MSER), and efficiency. The estimates conducted using RaSS tend to be more efficient than the estimates based on SiRS. The importance and applicability of the KMBX model are demonstrated using three different data sets. Some of the useful actuarial risk measures, such as the value at risk and conditional value at risk, are discussed. Full article
(This article belongs to the Special Issue Stochastic Modeling and Statistical Analysis of Financial Data)
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