The dynamic connectedness and hedging opportunities of implied and realized volatility: Evidence from clean energy ETFs

被引:16
|
作者
Celik, Ismail [1 ]
Sak, Ahmet Furkan [2 ]
Hol, Arife Ozdemir [1 ]
Vergili, Gizem [3 ]
机构
[1] Burdur Mehmet Akif Ersoy Univ, Dept Finance & Banking, Burdur, Turkey
[2] Burdur Mehmet Akif Ersoy Univ, Dept Business, Burdur, Turkey
[3] Burdur Mehmet Akif Ersoy Univ, Dept Econ & Finance, Burdur, Turkey
关键词
Clean energy ETF; Implied volatility; Dynamic connectedness; Hedging effectiveness; IMPULSE-RESPONSE ANALYSIS; STOCK-PRICES; CRUDE-OIL; UNIT-ROOT; CONDITIONAL CORRELATION; FINANCIAL CONTAGION; EXCHANGE-RATES; CO-MOVEMENT; TIME-SERIES; SHORT-RUN;
D O I
10.1016/j.najef.2022.101670
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
This paper aims to examine dynamic connectedness and hedging opportunities between the realized volatilities of clean energy ETFs and energy implied volatilities through Time-Varying Parameter Vector Autoregression Model (TVP-VAR) and Asymmetric Dynamic Conditional Cor -relation (ADCC) GARCH models. TVP-VAR analysis results show that dynamic connectedness increases during turbulence periods. We also determine that clean energy ETFs such as PBW, QCLN, SMOG, and TAN are net volatility transmitters. Surprisingly, OVX is a net volatility receiver, especially with the developments after the Paris Agreement in 2016. As a result of the ADCC GARCH analysis, we determine that the conditional correlation be-tween clean energy ETFs and implied volatility ETFs is asymmetric, and negative information shocks increase the conditional correlation. Although OVX is a cheap alternative for hedging long position risks in clean energy ETFs, VXXLE is more effective than OVX in terms of hedging effectiveness. These findings provide insight for individual and institutional investors, and portfolio managers on how negative and positive shocks change the conditional correlation be-tween assets at different levels.
引用
收藏
页数:21
相关论文
共 50 条
  • [41] Dynamic volatility connectedness between thermal coal futures and major cryptocurrencies: Evidence from China
    Pham, Son Duy
    Nguyen, Thao Thac Thanh
    Do, Hung Xuan
    Energy Economics, 2022, 112
  • [42] Intraday return predictability: Evidence from commodity ETFs and their related volatility indices
    Xu, Yahua
    Bouri, Elie
    Saeed, Tareq
    Wen, Zhuzhu
    RESOURCES POLICY, 2020, 69
  • [43] The pricing of idiosyncratic risk: evidence from the implied volatility distribution
    Suss, Stephan
    FINANCIAL MARKETS AND PORTFOLIO MANAGEMENT, 2012, 26 (02) : 247 - 267
  • [44] The pricing of idiosyncratic risk: evidence from the implied volatility distribution
    Stephan Süss
    Financial Markets and Portfolio Management, 2012, 26 (2): : 247 - 267
  • [45] Dispersion Trading and Determinants of Implied Volatility: Evidence from Australia
    Li Jialong
    Li Bowei
    Liu Min
    PROCEEDINGS OF THE 5TH (2013) INTERNATIONAL CONFERENCE ON FINANCIAL RISK AND CORPORATE FINANCE MANAGEMENT, VOLS I AND II, 2013, : 200 - 207
  • [46] Impacts of oil implied volatility shocks on stock implied volatility in China: Empirical evidence from a quantile regression approach
    Xiao, Jihong
    Hu, Chunyan
    Ouyang, Guangda
    Wen, Fenghua
    ENERGY ECONOMICS, 2019, 80 : 297 - 309
  • [47] Forecasting ethanol market volatility: new evidence from the corn implied volatility index
    Dutta, Anupam
    BIOFUELS BIOPRODUCTS & BIOREFINING-BIOFPR, 2019, 13 (01): : 48 - 54
  • [48] When passive funds affect prices: evidence from volatility and commodity ETFs
    Todorov, Karamfil
    REVIEW OF FINANCE, 2024, 28 (03) : 831 - 863
  • [49] Dynamic connectedness in the higher moments between clean energy and oil prices
    Hao, Wei
    Pham, Linh
    ENERGY ECONOMICS, 2024, 140
  • [50] The dynamic correlations between the G7 economies and China: Evidence from both realized and implied volatilities
    Luo, Xingguo
    Qi, Xuyuanda
    JOURNAL OF FUTURES MARKETS, 2017, 37 (10) : 989 - 1002