Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models

被引:66
|
作者
Ben Cheikh, Nidhaleddine [1 ]
Ben Zaied, Younes [2 ]
Chevallier, Julien [3 ,4 ]
机构
[1] ESSCA Sch Management, 1 Rue Lakanal, F-49000 Angers, France
[2] EDC Paris Business Sch, 70 Galerie Damiers, F-92415 Courbevoie, France
[3] IPAG Business Sch IPAG Lab, 184 Bd St Germain, F-75006 Paris, France
[4] Univ Paris 8 LED, 2 Rue Liberte, F-93526 St Denis, France
关键词
Cryptocurrencies; Asymmetric volatility; Smooth transition GARCH; SAFE HAVEN; BITCOIN; GOLD; DOLLAR; RETURN; HEDGE;
D O I
10.1016/j.frl.2019.09.008
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
This paper investigates the presence of asymmetric volatility dynamics in Bitcoin, Ethereum, Ripple, and Litecoin. Asymmetric effects between good and bad news are traditionally modeled using threshold GARCH models that allow only for two possible variance regimes. We experiment a slightly flexible specification for the conditional variance by using a Smooth Transition GARCH (ST-GARCH) model, where a continuum of intermediate states is allowed between the two extreme volatility regimes. We feature an inverted asymmetric reaction for the majority of cryptocurrencies. The presence of positive return-volatility relationship, which is different from other traditional assets, supports the safe-haven hypothesis in cryptocurrencies.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Asymmetric volatility in the cryptocurrency market: New evidence from models with structural breaks
    Aharon, David Y.
    Butt, Hassan Anjum
    Jaffri, Ali
    Nichols, Brian
    [J]. INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS, 2023, 87
  • [2] Impact of COVID-19 Crisis on Volatility Spillovers across Global Financial Markets: Evidence from Asymmetric GARCH Models
    Khan, Muhammad Niaz
    [J]. JOURNAL OF ECONOMIC INTEGRATION, 2024, 39 (02) : 373 - 393
  • [3] Performance of ARCH and GARCH Models in Forecasting Cryptocurrency Market Volatility
    Almansour, Bashar Yaser
    Alshater, Muneer M.
    Almansour, Ammar Yaser
    [J]. INDUSTRIAL ENGINEERING AND MANAGEMENT SYSTEMS, 2021, 20 (02): : 130 - 139
  • [4] Geopolitical Risks, Returns, and Volatility in the MENA Financial Markets: Evidence from GARCH and EGARCH Models
    Gharaibeh, Omar
    Kharabsheh, Buthiena
    [J]. MONTENEGRIN JOURNAL OF ECONOMICS, 2023, 19 (03) : 21 - 36
  • [5] Mean reversion in international markets: evidence from GARCH and half-life volatility models
    Ahmed, Rizwan Raheem
    Vveinhardt, Jolita
    Streimikiene, Dalia
    Channar, Zahid Ali
    [J]. ECONOMIC RESEARCH-EKONOMSKA ISTRAZIVANJA, 2018, 31 (01): : 1198 - 1217
  • [6] Forecasting volatility with asymmetric smooth transition dynamic range models
    Lin, Edward M. H.
    Chen, Cathy W. S.
    Gerlach, Richard
    [J]. INTERNATIONAL JOURNAL OF FORECASTING, 2012, 28 (02) : 384 - 399
  • [7] Modelling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India
    Lithin, B. M.
    Chakraborty, Suman
    Iyer, Vishwanathan
    Nikhil, N. M.
    Ledwani, Sanket
    [J]. COGENT ECONOMICS & FINANCE, 2023, 11 (01):
  • [9] A note on financial vulnerability and volatility in emerging stock markets: evidence from GARCH-MIDAS models
    Demirer, Riza
    Gupta, Rangan
    Li, He
    You, Yu
    [J]. APPLIED ECONOMICS LETTERS, 2023, 30 (01) : 37 - 42
  • [10] Asymmetric Volatility Risk: Evidence from Option Markets
    Jackwerth, Jens
    Vilkov, Grigory
    [J]. REVIEW OF FINANCE, 2019, 23 (04) : 777 - 799