Nonparametric Analysis of Economic and Financial Time Series 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 (31 March 2021) | Viewed by 1994

Special Issue Editor


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Guest Editor
Management School, The University of Liverpool, Liverpool L69 3BX, UK
Interests: granger causality analysis; nonparametric analysis of time series; asset pricing; risk management and portfolio optimization; econometrics and finance

Special Issue Information

Dear Colleagues,

Economics/Financial Econometrics is unable to handle properly the nonlinearity in the causal relationship between economic and financial variables, a major shortcoming. In finance for example, this might have important consequences on portfolio selection and risk management as these depend on the underlying model linking returns to the risk factors. The misspecification of this model by assuming that the returns are linearly related to the risk factors has an undesirable impact on portfolio weights and risk assessment. In addition to ignoring nonlinearity, applied economic and financial research tends to use mean regressions for examining the relationships between the variables of interest. However, in the mean regression the dependence is only due to the mean dependence, thus these studies ignore the dependence in conditional quantiles as well as high order moments (such as variance, skewness, kurtosis, etc.)

This call for paper seeks to publish applied work that use nonparametric analysis to handle the nonlinearity and other aspects (quantiles, high-order moments, etc.) in the causal relationship between economic and/or financial variables. In particular, we are interested in papers related to the following topics:

  • Papers that use nonparametric mean regressions to model the relationship between economic and/or financial time series data. We look for papers that have a clear motivation behind the use of nonparametric approach: a motivation that could be either empirical, theoretical, or both.
  • Papers that use nonparametric quantile regressions. In particular, those papers that model risk using information from economic and/or financial time series data. Again, we look for papers that have a clear motivation behind the use of nonparametric approach.
  • More general papers on nonparametric analysis, including those that use nonparametric distribution analysis, nonparametric tests, and many other topics are also welcome if they are immediately applicable to the special issue topics of interest.

To be considered for publication in the special issue, please submit your manuscript via the online submission portal. All submissions will be peer-reviewed.

Any questions about the special issue can be directed to Abderrahim Taamouti at [email protected].

Prof. Dr. Abderrahim Taamouti
Guest Editor

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.

Keywords

  • Nonparametric Analysis
  • Time Series Data
  • Nonparametric Mean Regression
  • Nonparametric Quantile Regression
  • Nonparametric Distributions Analysis

Published Papers (1 paper)

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Research

18 pages, 349 KiB  
Article
Large Deviations for a Class of Multivariate Heavy-Tailed Risk Processes Used in Insurance and Finance
by Miriam Hägele and Jaakko Lehtomaa
J. Risk Financial Manag. 2021, 14(5), 202; https://0-doi-org.brum.beds.ac.uk/10.3390/jrfm14050202 - 02 May 2021
Cited by 2 | Viewed by 1563
Abstract
Modern risk modelling approaches deal with vectors of multiple components. The components could be, for example, returns of financial instruments or losses within an insurance portfolio concerning different lines of business. One of the main problems is to decide if there is any [...] Read more.
Modern risk modelling approaches deal with vectors of multiple components. The components could be, for example, returns of financial instruments or losses within an insurance portfolio concerning different lines of business. One of the main problems is to decide if there is any type of dependence between the components of the vector and, if so, what type of dependence structure should be used for accurate modelling. We study a class of heavy-tailed multivariate random vectors under a non-parametric shape constraint on the tail decay rate. This class contains, for instance, elliptical distributions whose tail is in the intermediate heavy-tailed regime, which includes Weibull and lognormal type tails. The study derives asymptotic approximations for tail events of random walks. Consequently, a full large deviations principle is obtained under, essentially, minimal assumptions. As an application, an optimisation method for a large class of Quota Share (QS) risk sharing schemes used in insurance and finance is obtained. Full article
(This article belongs to the Special Issue Nonparametric Analysis of Economic and Financial Time Series Data)
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