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Econometrics
Special Issues
Volatility Modeling
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Volatility Modeling

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

Deadline for manuscript submissions: closed (30 June 2018) | Viewed by 23488

Special Issue Editors

Prof. Dr. Deniz Erdemlioglu

E-Mail Website
Guest Editor
Department of Finance, IESEG School of Management, 59000 Lille, France
Interests: financial econometrics; applied time series analysis; high-frequency data and volatility modeling; international finance, tail risk measurement
Prof. Dr. Olivier Scaillet

E-Mail Website
Guest Editor
Université de Genève, Swiss Finance Institute, Geneva, Switherland
Interests: financial econometrics; term structure modeling; risk management in finance and insurance; derivative pricing; asset allocation; encompassing theory; nonparametric statistics and specification tests
Prof. Dr. Kamil Yilmaz

E-Mail Website
Guest Editor
Koc University, Istanbul, Turkey
Interests: financial econometrics; financial and macroeconomic connectedness; applied econometrics; financial contagion; volatility modeling; network theory

Special Issue Information

Dear Colleagues,

The subprime and European sovereign crises have shown the need for better econometric techniques to understand asset price fluctuations, market uncertainty and global risk spillovers. The availability of high frequency data and the rise of high frequency trading also raise new research and policy questions. Researchers and econometricians continue to develop new tools and methods that help explain how volatility varies over time and whether or not volatility risk is transmitted across regions and asset classes. Given these recent advances, the primary objective of this Special Issue is to foster the latest research progress on volatility dynamics with the main theme on the econometric analysis of financial volatility modeling, measurement and forecasting from both theoretical and applied/empirical perspectives. We invite submissions on topics that are broadly related (but not limited) to:

  • Volatility analysis with high-frequency data,
  • Measurement of volatility risk and premium,
  • Determinants of volatility: the role of news announcements,
  • Volatility networks and (inter)connectedness,
  • Price volatility and market microstructure,
  • Econometric assessment of market volatility and monetary policy,
  • Multivariate volatility estimation and co-jumps,
  • High frequency trading (HFT) and price volatility,
  • Volatility of volatility and jumps: new tests and inference
  • Volatility forecasting and macroeconomic uncertainty
  • Non-linear volatility models and implications for portfolio optimization

Deniz Erdemlioglu
Olivier Scaillet
Kamil Yilmaz
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.

Keywords

  • Stochastic volatility
  • Volatility models
  • multivariate volatility
  • volatility measurement and forecasting
  • Volatility in continuous-time
  • Volatility jumps
  • monetary policy and market volatility

Published Papers (3 papers)

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Research

27 pages, 581 KiB  
Article
A Multivariate Kernel Approach to Forecasting the Variance Covariance of Stock Market Returns
by Ralf Becker, Adam Clements and Robert O'Neill
Econometrics 2018, 6(1), 7; https://doi.org/10.3390/econometrics6010007 - 17 Feb 2018
Cited by 1 | Viewed by 7798
Abstract
This paper introduces a multivariate kernel based forecasting tool for the prediction of variance-covariance matrices of stock returns. The method introduced allows for the incorporation of macroeconomic variables into the forecasting process of the matrix without resorting to a decomposition of the matrix. [...] Read more.
This paper introduces a multivariate kernel based forecasting tool for the prediction of variance-covariance matrices of stock returns. The method introduced allows for the incorporation of macroeconomic variables into the forecasting process of the matrix without resorting to a decomposition of the matrix. The model makes use of similarity forecasting techniques and it is demonstrated that several popular techniques can be thought as a subset of this approach. A forecasting experiment demonstrates the potential for the technique to improve the statistical accuracy of forecasts of variance-covariance matrices. Full article
(This article belongs to the Special Issue Volatility Modeling)
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730 KiB  
Article
Time-Varying Window Length for Correlation Forecasts
by Yoontae Jeon and Thomas H. McCurdy
Econometrics 2017, 5(4), 54; https://doi.org/10.3390/econometrics5040054 - 11 Dec 2017
Cited by 2 | Viewed by 7981
Abstract
Forecasting correlations between stocks and commodities is important for diversification across asset classes and other risk management decisions. Correlation forecasts are affected by model uncertainty, the sources of which can include uncertainty about changing fundamentals and associated parameters (model instability), structural breaks and [...] Read more.
Forecasting correlations between stocks and commodities is important for diversification across asset classes and other risk management decisions. Correlation forecasts are affected by model uncertainty, the sources of which can include uncertainty about changing fundamentals and associated parameters (model instability), structural breaks and nonlinearities due, for example, to regime switching. We use approaches that weight historical data according to their predictive content. Specifically, we estimate two alternative models, ‘time-varying weights’ and ‘time-varying window’, in order to maximize the value of past data for forecasting. Our empirical analyses reveal that these approaches provide superior forecasts to several benchmark models for forecasting correlations. Full article
(This article belongs to the Special Issue Volatility Modeling)
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574 KiB  
Article
Business Time Sampling Scheme with Applications to Testing Semi-Martingale Hypothesis and Estimating Integrated Volatility
by Yingjie Dong and Yiu-Kuen Tse
Econometrics 2017, 5(4), 51; https://doi.org/10.3390/econometrics5040051 - 13 Nov 2017
Cited by 2 | Viewed by 6926
Abstract
We propose a new method to implement the Business Time Sampling (BTS) scheme for high-frequency financial data. We compute a time-transformation (TT) function using the intraday integrated volatility estimated by a jump-robust method. The BTS transactions are obtained using the inverse of the [...] Read more.
We propose a new method to implement the Business Time Sampling (BTS) scheme for high-frequency financial data. We compute a time-transformation (TT) function using the intraday integrated volatility estimated by a jump-robust method. The BTS transactions are obtained using the inverse of the TT function. Using our sampled BTS transactions, we test the semi-martingale hypothesis of the stock log-price process and estimate the daily realized volatility. Our method improves the normality approximation of the standardized business-time return distribution. Our Monte Carlo results show that the integrated volatility estimates using our proposed sampling strategy provide smaller root mean-squared error. Full article
(This article belongs to the Special Issue Volatility Modeling)
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