IDEAS home Printed from https://ideas.repec.org/r/eee/econom/v186y2015i1p258-275.html
   My bibliography  Save this item

Bad environments, good environments: A non-Gaussian asymmetric volatility model

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Segal, Gill & Shaliastovich, Ivan & Yaron, Amir, 2015. "Good and bad uncertainty: Macroeconomic and financial market implications," Journal of Financial Economics, Elsevier, vol. 117(2), pages 369-397.
  2. Wu, Xinyu & Xia, Michelle & Zhang, Huanming, 2020. "Forecasting VaR using realized EGARCH model with skewness and kurtosis," Finance Research Letters, Elsevier, vol. 32(C).
  3. Dunbar, Kwamie, 2021. "Pricing the hedging factor in the cross-section of stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
  4. Trong‐Nghia Nguyen & Minh‐Ngoc Tran & Robert Kohn, 2022. "Recurrent conditional heteroskedasticity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1031-1054, August.
  5. Delis, Manthos D. & Savva, Christos S. & Theodossiou, Panayiotis, 2021. "The impact of the coronavirus crisis on the market price of risk," Journal of Financial Stability, Elsevier, vol. 53(C).
  6. Herrera, R. & Clements, A.E., 2018. "Point process models for extreme returns: Harnessing implied volatility," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 161-175.
  7. Juan M. Londono & Nancy R. Xu, 2021. "The Global Determinants of International Equity Risk Premiums," International Finance Discussion Papers 1318, Board of Governors of the Federal Reserve System (U.S.).
  8. Xu, Nancy R., 2021. "Procyclicality of the comovement between dividend growth and consumption growth," Journal of Financial Economics, Elsevier, vol. 139(1), pages 288-312.
  9. Yusui Tang & Feng Ma & Yaojie Zhang & Yu Wei, 2022. "Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4770-4783, October.
  10. Zhang, Chuanhai & Zhang, Zhengjun & Xu, Mengyu & Peng, Zhe, 2023. "Good and bad self-excitation: Asymmetric self-exciting jumps in Bitcoin returns," Economic Modelling, Elsevier, vol. 119(C).
  11. Yao, Haixiang & Huang, Jinbo & Li, Yong & Humphrey, Jacquelyn E., 2021. "A general approach to smooth and convex portfolio optimization using lower partial moments," Journal of Banking & Finance, Elsevier, vol. 129(C).
  12. BenSaïda, Ahmed, 2019. "Good and bad volatility spillovers: An asymmetric connectedness," Journal of Financial Markets, Elsevier, vol. 43(C), pages 78-95.
  13. Yarovaya, Larisa & Brzeszczyński, Janusz & Lau, Chi Keung Marco, 2017. "Asymmetry in spillover effects: Evidence for international stock index futures markets," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 94-111.
  14. Sylvia J. Soltyk & Felix Chan, 2023. "Modeling time‐varying higher‐order conditional moments: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 33-57, February.
  15. Geert Bekaert & Eric Engstrom & Andrey Ermolov, 2023. "The Variance Risk Premium in Equilibrium Models," Review of Finance, European Finance Association, vol. 27(6), pages 1977-2014.
  16. Li, Dan & Clements, Adam & Drovandi, Christopher, 2021. "Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo," Econometrics and Statistics, Elsevier, vol. 19(C), pages 22-46.
  17. Segal, Gill, 2019. "A tale of two volatilities: Sectoral uncertainty, growth, and asset prices," Journal of Financial Economics, Elsevier, vol. 134(1), pages 110-140.
  18. Mete Kilic & Ivan Shaliastovich, 2019. "Good and Bad Variance Premia and Expected Returns," Management Science, INFORMS, vol. 67(6), pages 2522-2544, June.
  19. Tang, Yusui & Ma, Feng, 2023. "The volatility of natural resources implications for sustainable development: Crude oil volatility prediction based on the multivariate structural regime switching," Resources Policy, Elsevier, vol. 83(C).
  20. Aretz, Kevin & Eser Arisoy, Y., 2023. "The Pricing of Skewness Over Different Return Horizons," Journal of Banking & Finance, Elsevier, vol. 148(C).
  21. Bruno Feunou & Ricardo Lopez Aliouchkin & Roméo Tedongap & Lai Xi, 2017. "Variance Premium, Downside Risk and Expected Stock Returns," Staff Working Papers 17-58, Bank of Canada.
  22. Ma, Feng & Li, Yu & Liu, Li & Zhang, Yaojie, 2018. "Are low-frequency data really uninformative? A forecasting combination perspective," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 92-108.
  23. T. -N. Nguyen & M. -N. Tran & R. Kohn, 2020. "Recurrent Conditional Heteroskedasticity," Papers 2010.13061, arXiv.org, revised Jan 2022.
  24. Lyu, Yongjian & Wei, Yu & Hu, Yingyi & Yang, Mo, 2021. "Good volatility, bad volatility and economic uncertainty: Evidence from the crude oil futures market," Energy, Elsevier, vol. 222(C).
  25. Bali, Turan G. & Brown, Stephen J. & Tang, Yi, 2017. "Is economic uncertainty priced in the cross-section of stock returns?," Journal of Financial Economics, Elsevier, vol. 126(3), pages 471-489.
  26. Chikashi Tsuji, 2016. "Does the fear gauge predict downside risk more accurately than econometric models? Evidence from the US stock market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1220711-122, December.
  27. Riccardo Colacito & Eric Ghysels & Jinghan Meng & Wasin Siwasarit, 2016. "Skewness in Expected Macro Fundamentals and the Predictability of Equity Returns: Evidence and Theory," The Review of Financial Studies, Society for Financial Studies, vol. 29(8), pages 2069-2109.
  28. Dew-Becker, Ian & Giglio, Stefano & Kelly, Bryan, 2021. "Hedging macroeconomic and financial uncertainty and volatility," Journal of Financial Economics, Elsevier, vol. 142(1), pages 23-45.
  29. Wang, Tianyi & Liang, Fang & Huang, Zhuo & Yan, Hong, 2022. "Do realized higher moments have information content? - VaR forecasting based on the realized GARCH-RSRK model," Economic Modelling, Elsevier, vol. 109(C).
  30. Siddique, Md Abubakar & Nobanee, Haitham & Karim, Sitara & Naz, Farah, 2023. "Do green financial markets offset the risk of cryptocurrencies and carbon markets?," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 822-833.
  31. Liu, Mengxi (Maggie) & Chan, Kam Fong & Faff, Robert, 2022. "What can we learn from firm-level jump-induced tail risk around earnings announcements?," Journal of Banking & Finance, Elsevier, vol. 138(C).
  32. Baur Dirk G. & Dimpfl Thomas, 2019. "Think again: volatility asymmetry and volatility persistence," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(1), pages 1-19, February.
  33. Bonaccolto, Giovanni & Caporin, Massimiliano & Paterlini, Sandra, 2019. "Decomposing and backtesting a flexible specification for CoVaR," Journal of Banking & Finance, Elsevier, vol. 108(C).
  34. Yu‐Fan Huang & Sui Luo, 2020. "Can Stock Volatility Be Benign? New Measurements and Macroeconomic Implications," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(4), pages 933-950, June.
  35. Fritzsch, Simon & Timphus, Maike & Weiß, Gregor, 2024. "Marginals versus copulas: Which account for more model risk in multivariate risk forecasting?," Journal of Banking & Finance, Elsevier, vol. 158(C).
  36. Stanislav Anatolyev & Stanislav Khrapov, 2015. "Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting," Econometrics, MDPI, vol. 3(3), pages 1-23, August.
  37. Schüler, Yves S., 2020. "The impact of uncertainty and certainty shocks," Discussion Papers 14/2020, Deutsche Bundesbank.
  38. Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
  39. Held, Matthias & Kapraun, Julia & Omachel, Marcel & Thimme, Julian, 2020. "Up- and downside variance risk premia in global equity markets," Journal of Banking & Finance, Elsevier, vol. 118(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.