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@article{Degiannakis2018ForecastingGS, title={Forecasting global stock market implied volatility indices}, author={Stavros Degiannakis and George Filis and Hossein Hassani}, journal={Journal of Empirical Finance}, year={2018}, volume={46}, pages={111-129}, url={https://api.semanticscholar.org/CorpusID:158621569}}
  • Stavros Degiannakis, G. Filis, Hossein Hassani
  • Published 1 March 2018
  • Economics, Business
  • Journal of Empirical Finance

31 Citations

Highly Influential Citations

1

Background Citations

8

Methods Citations

6

Results Citations

1

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31 Citations

Forecasting the aggregate stock market volatility in a data-rich world
    Li LiuFeng MaQing ZengYaojie Zhang

    Economics

  • 2020

ABSTRACT In this article, we utilize the basic lasso and elastic net models to revisit the predictive performance of aggregate stock market volatility in a data-rich world. Motivated by the existing

Does the US stock market information matter for European equity market volatility: a multivariate perspective?
    Yusui TangFeng MaM. WahabYu Wei

    Economics

    Applied Economics

  • 2022

ABSTRACT This research investigates whether the US stock volatility index (S&P 500 index) has the forecasting ability to predict the volatility of CAC index (France), DAX index (Germany), and FTSE

  • 1
Oil price volatility forecasts: What do investors need to know?
    Stavros DegiannakisG. Filis

    Economics, Business

    Journal of International Money and Finance

  • 2021
  • 15
  • PDF
Forecasting and trading on the VIX futures market: A neural network approach based on open to close returns and coincident indicators
    L. BallestraA. GuizzardiFabio Palladini

    Economics, Business

    International Journal of Forecasting

  • 2019
  • 18
  • PDF
Volatility forecasting: Global economic policy uncertainty and regime switching
    Miao YuJinguo Song

    Economics

    Physica A: Statistical Mechanics and its…

  • 2018
  • 28
What Should be Taken into Consideration when Forecasting Oil Implied Volatility Index?
    Panagiotis DelisStavros DegiannakisK. Giannopoulos

    Economics

    The Energy Journal

  • 2023

This study forecasts the oil volatility index (OVX) incorporating information from other implied volatility (IV) indices. We provide evidence for the existence of long memory in the OVX in order to

  • 1
  • PDF
Forecasting Realized Volatility of Agricultural Commodities
    Stavros DegiannakisG. FilisTony KleinT. Walther

    Agricultural and Food Sciences, Economics

    International Journal of Forecasting

  • 2020

We forecast the realized and median realized volatility of agricultural commodities using variants of the Heterogeneous AutoRegressive (HAR) model. We obtain tick-by-tick data for five widely traded

  • 29
  • PDF
Forecasting implied volatility risk indexes: International evidence using Hammerstein-ARX approach
    Kais Tissaoui

    Economics

    International Review of Financial Analysis

  • 2019
  • 12
Forecasting Macroeconomic Indicators for Eurozone and Greece: How Useful are the Oil Price Assumptions?
    G. FilisStavros DegiannakisZ. Bragoudakis

    Economics

    SSRN Electronic Journal

  • 2022

This study evaluates oil price forecasts based on their economic significance for macroeconomic predictions. More specifically, we first use the current state-of-the-art frameworks to forecast

  • 1
  • PDF
Forecasting the Oil Volatility Index Using Factors of Uncertainty
    Panagiotis DelisStavros DegiannakisK. Giannopoulos

    Economics, Business

    Asian Journal of Economics and Empirical Research

  • 2022

The oil volatility index (OVX) has attracted the attention of investors, as oil prices have been subject to high degrees of variation in the last few decades, and investors would therefore benefit

  • PDF

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79 References

Forecasting Volatility
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The paper compares the forecasting ability of the most popular volatility forecasting models and develops an alternative. The comparison of existing models focuses on four issues: 1) the relative

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This discussion paper resulted in an article in the Journal of Empirical Finance (2005). Vol. 12, issue 3, pages 445-475. The increasing availability of financial market data at intraday frequencies

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Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model
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Predicting the one-step-ahead volatility is of great importance in measuring and managing investment risk more accurately. Taking into consideration the main characteristics of the conditional

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Modeling and Forecasting Realized Volatility
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A general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions is provided and the links between the conditional covariance matrix and the concept of realized volatility are formally developed.

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Modeling and predicting the CBOE market volatility index
    Marcelo FernandesM. C. MedeirosMarcel Scharth

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Modeling and Forecasting Exchange Rate Volatility in Time-Frequency Domain
    Jozef BaruníkTomas KrehlikLukáš Vácha

    Economics, Mathematics

    Eur. J. Oper. Res.

  • 2016

This paper proposes a realized Jump-GARCH models estimated in two versions using maximum likelihood as well as observation-driven estimation framework of generalized autoregressive score that outperform statistically the popular as well conventional models in both one-day and multi-period-ahead forecasting.

    E. Haji*zadehA. SeifiM. ZarandiI. Türksen

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Volatility forecasting: intra-day versus inter-day models
    Timotheos AngelidisStavros Degiannakis

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Model Uncertainty, Thick Modelling and the Predictability of Stock Returns
    Marco AiolfiCarlo A. Favero

    Business, Economics

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Recent financial research has provided evidence on the predictability of asset returns. In this Paper we consider the results contained in Pesaran-Timmerman (1995), which provided evidence on

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