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Journals
Econometrics
Special Issues
Recent Developments of Financial Econometrics
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Recent Developments of Financial Econometrics

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: closed (29 February 2016) | Viewed by 49788

Special Issue Editors

Dr. Fredj Jawadi

E-Mail Website
Guest Editor
University of Evry Val d’Essonne, 2 rue Facteur Cheval, Baîtment La Poste, Bureau 226 Evry 91025, France
Interests: Applied econometrics; Nonlinear Dynamics; financial econometrics and financial macroeconomics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tony S. Wirjanto

E-Mail Website
Guest Editor
1. School of Accounting & Finance (SAF), Faculty of Arts, University of Waterloo, Waterloo, ON N2L 3G1, Canada
2. Department of Statistics & Actuarial Science (SAS), Faculty of Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: econometrics and statistics; financial econometrics and financial statistics; financial time series; mathematical finance; finance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marc S. Paolella

E-Mail Website
Guest Editor
Department of Banking and Finance, University of Zurich, Zurich, Switzerland
Interests: computational statistics; volatility modeling; large-scale multivariate density prediction of financial asset returns; portfolio optimization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nuttanan Wichitaksorn

E-Mail Website
Guest Editor
1Thailand Development Research Institute, Bangkok, Thailand
2 School of Mathematics and Statistics, University of Canterbury, New Zealand
Interests: Bayesian Econometrics and Statistics with applications in economics, finance, health science, and social science

Special Issue Information

Dear Colleagues,

In the aftermath of the 2008/2009 global financial crises, several international capital markets experienced severe losses. In order to limit these losses and improve risk control, the financial market authorities adopted new regulatory measures to strengthen the financial systems, control algorithm and flash trading, improve market organization, and advance risk management. The availability of high frequency market data and the development of recent econometric models are of real interest in assessing the efficiency of these new regulatory measures and to test their appropriateness. Moreover, this can also help identify the main characteristics of the financial market data, resolve the issues raised by high frequency data, improve the understanding of price formation, and assess the risk dynamics.

In the light of this, as one of the organizers, guest editor Dr. Jawadi is delighted to inform that the 2nd International Workshop on “Financial Markets and Nonlinear Dynamics” (FMND) will be held in Paris on June 4–5, 2015. The aim of the workshop is to discuss innovative modelling approaches that can serve as valuable frameworks to deal with these issues, with a particular interest for nonlinear models. The workshop aims at bringing together academics and professionals (economists, financiers, and econometricians) to discuss these issues and to present their recent theoretical and empirical findings. It will also serve as a valuable platform for discussing innovative and thought provoking ideas on nonlinear high frequency data modelling. For more information, please refer to www.fmnd.fr.

This special issue is also dedicated to selected papers from a research conference on Financial Econometrics and Quantitative Risk Management, organized by guest editors Drs. Paolella and Wichitaksorn, and sponsored by a joint collaboration with Chulalongkorn University Department of Banking and Finance, the Thailand Development Research Institute (TDRI), and a grant from the Swiss Federal Institute of Technology (ETH) Federal Department of Economic Affairs. The confirmed keynote speaker is Professor Paul Embrechts, ETH. The event will take place on August 21, 2015 at the TDRI in Bangkok. Topics of interest include, but are not limited to, studies of financial risk measures such as Value at Risk and Expected Shortfall, modern methods for portfolio allocation, credit risk modeling, insurance mathematics, option pricing, high-frequency data, and bank systemic risk.

The special issue aims to publish papers that might include (but are not restricted to) theoretical, experimental and empirical research in the following areas:

  • Financial Econometrics
  • Threshold Modeling
  • Switching Regime Models
  • GARCH Modeling
  • Copula Techniques
  • Simulation Methods
  • Non Parametric Models
  • Dynamic Conditional Moments
  • Bayesian Analysis

Dr. Fredj Jawadi
Prof. Dr. Tony S. Wirjanto
Prof.Marc S. Paolella
Dr. Nuttanan Wichitaksorn
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. Econometrics is an international peer-reviewed open access quarterly 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.


Reference Literature for this Special Issue

Gourieroux, C.; Monfort, A.; Pegoraro, F.; Renne, J.-P. Regime switching and bond pricing. J. Financ. Econom. 2014, 12, 237–277.

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

Published Papers (6 papers)

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Research

730 KiB  
Article
Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability
by Marc S. Paolella
Econometrics 2016, 4(2), 25; https://doi.org/10.3390/econometrics4020025 - 5 May 2016
Cited by 20 | Viewed by 8819
Abstract
A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration is proposed. Several powerful tests for the (asymmetric) stable Paretian distribution with tail index 1 < α < 2 are used for assessing the appropriateness of the stable [...] Read more.
A fast method for estimating the parameters of a stable-APARCH not requiring likelihood or iteration is proposed. Several powerful tests for the (asymmetric) stable Paretian distribution with tail index 1 < α < 2 are used for assessing the appropriateness of the stable assumption as the innovations process in stable-GARCH-type models for daily stock returns. Overall, there is strong evidence against the stable as the correct innovations assumption for all stocks and time periods, though for many stocks and windows of data, the stable hypothesis is not rejected. Full article
(This article belongs to the Special Issue Recent Developments of Financial Econometrics)
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336 KiB  
Article
Recovering the Most Entropic Copulas from Preliminary Knowledge of Dependence
by Ba Chu and Stephen Satchell
Econometrics 2016, 4(2), 20; https://doi.org/10.3390/econometrics4020020 - 29 Mar 2016
Cited by 1 | Viewed by 5927
Abstract
This paper provides a new approach to recover relative entropy measures of contemporaneous dependence from limited information by constructing the most entropic copula (MEC) and its canonical form, namely the most entropic canonical copula (MECC). The MECC can effectively be obtained by maximizing [...] Read more.
This paper provides a new approach to recover relative entropy measures of contemporaneous dependence from limited information by constructing the most entropic copula (MEC) and its canonical form, namely the most entropic canonical copula (MECC). The MECC can effectively be obtained by maximizing Shannon entropy to yield a proper copula such that known dependence structures of data (e.g., measures of association) are matched to their empirical counterparts. In fact the problem of maximizing the entropy of copulas is the dual to the problem of minimizing the Kullback-Leibler cross entropy (KLCE) of joint probability densities when the marginal probability densities are fixed. Our simulation study shows that the proposed MEC estimator can potentially outperform many other copula estimators in finite samples. Full article
(This article belongs to the Special Issue Recent Developments of Financial Econometrics)
633 KiB  
Article
A Method for Measuring Treatment Effects on the Treated without Randomization
by P.A.V.B. Swamy, Stephen G. Hall, George S. Tavlas, I-Lok Chang, Heather D. Gibson, William H. Greene and Jatinder S. Mehta
Econometrics 2016, 4(2), 19; https://doi.org/10.3390/econometrics4020019 - 25 Mar 2016
Cited by 4 | Viewed by 6444
Abstract
This paper contributes to the literature on the estimation of causal effects by providing an analytical formula for individual specific treatment effects and an empirical methodology that allows us to estimate these effects. We derive the formula from a general model with minimal [...] Read more.
This paper contributes to the literature on the estimation of causal effects by providing an analytical formula for individual specific treatment effects and an empirical methodology that allows us to estimate these effects. We derive the formula from a general model with minimal restrictions, unknown functional form and true unobserved variables such that it is a credible model of the underlying real world relationship. Subsequently, we manipulate the model in order to put it in an estimable form. In contrast to other empirical methodologies, which derive average treatment effects, we derive an analytical formula that provides estimates of the treatment effects on each treated individual. We also provide an empirical example that illustrates our methodology. Full article
(This article belongs to the Special Issue Recent Developments of Financial Econometrics)
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1192 KiB  
Article
Forecasting Value-at-Risk under Different Distributional Assumptions
by Manuela Braione and Nicolas K. Scholtes
Econometrics 2016, 4(1), 3; https://doi.org/10.3390/econometrics4010003 - 11 Jan 2016
Cited by 39 | Viewed by 13291
Abstract
Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR). We provide a comprehensive look at the problem by considering the impact [...] Read more.
Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR). We provide a comprehensive look at the problem by considering the impact that different distributional assumptions have on the accuracy of both univariate and multivariate GARCH models in out-of-sample VaR prediction. The set of analyzed distributions comprises the normal, Student, Multivariate Exponential Power and their corresponding skewed counterparts. The accuracy of the VaR forecasts is assessed by implementing standard statistical backtesting procedures used to rank the different specifications. The results show the importance of allowing for heavy-tails and skewness in the distributional assumption with the skew-Student outperforming the others across all tests and confidence levels. Full article
(This article belongs to the Special Issue Recent Developments of Financial Econometrics)
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1327 KiB  
Article
Forecasting Interest Rates Using Geostatistical Techniques
by Giuseppe Arbia and Michele Di Marcantonio
Econometrics 2015, 3(4), 733-760; https://doi.org/10.3390/econometrics3040733 - 9 Nov 2015
Cited by 3 | Viewed by 7690
Abstract
Geostatistical spatial models are widely used in many applied fields to forecast data observed on continuous three-dimensional surfaces. We propose to extend their use to finance and, in particular, to forecasting yield curves. We present the results of an empirical application where we [...] Read more.
Geostatistical spatial models are widely used in many applied fields to forecast data observed on continuous three-dimensional surfaces. We propose to extend their use to finance and, in particular, to forecasting yield curves. We present the results of an empirical application where we apply the proposed method to forecast Euro Zero Rates (2003–2014) using the Ordinary Kriging method based on the anisotropic variogram. Furthermore, a comparison with other recent methods for forecasting yield curves is proposed. The results show that the model is characterized by good levels of predictions’ accuracy and it is competitive with the other forecasting models considered. Full article
(This article belongs to the Special Issue Recent Developments of Financial Econometrics)
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623 KiB  
Article
Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting
by Stanislav Anatolyev and Stanislav Khrapov
Econometrics 2015, 3(3), 610-632; https://doi.org/10.3390/econometrics3030610 - 10 Aug 2015
Cited by 1 | Viewed by 6296
Abstract
Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance targeting, which reduces the degree of parameterization and facilitates estimation. We compare the two approaches and investigate, via simulations, how non-normality features of the return distribution affect the [...] Read more.
Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance targeting, which reduces the degree of parameterization and facilitates estimation. We compare the two approaches and investigate, via simulations, how non-normality features of the return distribution affect the quality of estimation of the volatility equation and corresponding value-at-risk predictions. We find that most GARCH coefficients and associated predictions are more precisely estimated when no variance targeting is employed. Bias properties are exacerbated for a heavier-tailed distribution of standardized returns, while the distributional asymmetry has little or moderate impact, these phenomena tending to be more pronounced under variance targeting. Some effects further intensify if one uses ML based on a leptokurtic distribution in place of normal QML. The sample size has also a more favorable effect on estimation precision when no variance targeting is used. Thus, if computational costs are not prohibitive, variance targeting should probably be avoided. Full article
(This article belongs to the Special Issue Recent Developments of Financial Econometrics)
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