Special Issue "Nonparametric Econometric Methods and Application"

Special Issue Editor

Guest Editor
Prof. Dr. Thanasis Stengos

University Research Chair, Department of Economics and Finance, University of Guelph, Guelph, Canada
Website | E-Mail
Interests: empirical growth; nonparametric econometric methods

Special Issue Information

Dear Colleagues,

An area of very active research in econometrics over the last 30 years has been that of non- and semi-parametric methods. These methods have provided ways to complement more-traditional parametric approaches in terms of robust alternatives, as well as preliminary data analysis. The field has expanded with important advances both in time series and cross sectional frameworks and more recently in panel data settings, allowing for data driven flexibility that has proved invaluable to applied researchers. The methodology has been enhanced by software developments that have made these methods easy to apply, somethings that has opened up a variety of potentially important and relevant applications in all areas of economics, microeconomics, macroeconomics and economic growth, finance, labor, etc. The present Special Issue aims at collecting a number of new contributions both at the theoretical level, as well as in terms of applications.  We hope that this collection of papers will add to this important literature, both at the theoretical and empirical level including areas, such as local smoothing techniques, splines, series estimators, and wavelets.

Prof. Dr. Thanasis Stengos
Guest Editor

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Keywords

  • Nonparametric methods
  • Semiparametric methods
  • Local smoothers
  • Splines
  • Wavelets

Published Papers (1 paper)

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Research

Open AccessArticle Leverage and Volatility Feedback Effects and Conditional Dependence Index: A Nonparametric Study
J. Risk Financial Manag. 2018, 11(2), 29; https://doi.org/10.3390/jrfm11020029
Received: 5 April 2018 / Revised: 30 May 2018 / Accepted: 4 June 2018 / Published: 8 June 2018
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Abstract
This paper studies the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method. Specifically, we propose a conditional dependence index to investigate how the dependence between the two series varies across different
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This paper studies the contemporaneous relationship between S&P 500 index returns and log-increments of the market volatility index (VIX) via a nonparametric copula method. Specifically, we propose a conditional dependence index to investigate how the dependence between the two series varies across different segments of the market return distribution. We find that: (a) the two series exhibit strong, negative, extreme tail dependence; (b) the negative dependence is stronger in extreme bearish markets than in extreme bullish markets; (c) the dependence gradually weakens as the market return moves toward the center of its distribution, or in quiet markets. The unique dependence structure supports the VIX as a barometer of markets’ mood in general. Moreover, applying the proposed method to the S&P 500 returns and the implied variance (VIX2), we find that the nonparametric leverage effect is much stronger than the nonparametric volatility feedback effect, although, in general, both effects are weaker than the dependence relation between the market returns and the log-increments of the VIX. Full article
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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