Research ArticleOpen AccessGoogle Scholar indexed
Study on the Systemic Risk of China’s Stock Markets under Risk-Neutral Conditions
School of Systems Science, Beijing Normal University, Beijing, China
School of Systems Science, Beijing Normal University, Beijing, China
- 1 School of Systems Science, Beijing Normal University, Beijing, China
- 2 School of Systems Science, Beijing Normal University, Beijing, China
Journal of Mathematical Finance·Volume 09 (2018)·Pages 54–79·Published 28 December 2018·DOI10.4236/jmf.2019.91005
Copy link · social · email
Abstract
Based on stochastic discount factor theory, this paper proposes a method to convert the traditional systemic risk measures of financial markets, such as VaR, ES, MES and SES, into risk-neutral measures. We proposed a novel way to neutralize the returns without relying on option price information. Then, we empirically analyzed and compared the systemic risks and changes between the A-shares in Shanghai and H-shares in Hong Kong before and after a stock market crash, and we found that systematic risk measures under risk neutrality could more accurately determine market system risks than traditional systemic risk measures. Moreover, these systemic risk measures have a certain market risk warning effect.
KeywordsStochastic Discount FactorRisk NeutralityTail RiskShanghai IndexHang Seng Index
- Bates, D.S. (1991) The Crash of 87: Was It Expected? The Evidence from Options Markets. Journal of Finance, 46, 1009-1044. https://doi.org/10.1111/j.1540-6261.1991.tb03775.x
- Bates, D.S. (2000) Post-87 Crash Fears in the S&P 500 Futures Option Market. Journal of Econometrics, 94, 181-238. https://doi.org/10.1016/S0304-4076(99)00021-4
- Pan, J. (2002) The Jump-Risk Premia Implicit in Options: Evidence from an Integrated Time-Series Study. Journal of Financial Economics, 63, 3-50. https://doi.org/10.1016/S0304-405X(01)00088-5
- Artzner, P., Delbaen, F., Eber, J.M. and Heath, D. (1999) Coherent Risk Measures. Mathematical Finance, 9, 203-228. https://doi.org/10.1111/1467-9965.00068
- Acharya, V.V., Engle, R. and Richardson, M. (2012) Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks. American Economic Review, 102, 59-64. https://doi.org/10.1257/aer.102.3.59
- Adrian, T. and Brunnermeier, M. (2016) CoVaR. American Economic Review, 106, 1705-1741. https://doi.org/10.1257/aer.20120555
- Acharya, V., Santos, J. and Yorulmazer, T. (2010) Systemic Risk and Deposit Insurance Premiums. Economic Policy Review, Federal Reserve Bank of New York. https://ideas.repec.org/a/fip/fednep/y2010iaugp89-99nv.16no.1.html https://www.newyorkfed.org/medialibrary/media/research/epr/10v16n1/1008yoru.pdf
- Brownlees, C.T. and Engle, R. (2010) Volatility, Correlation and Tails for Systemic Risk Measurement. Mimeo, New York University, New York.
- Jon, D. and de Vries, C.G. (1997) Tail Index and Quantile Estimation with Very High Frequency Data. https://doi.org/10.1016/S0927-5398(97)00008-X
- McNeil, F.R. (2002) A VaR and Expected Shortfall in Portfolios of Dependent Credit Risks. Journal of Banking and Finance, 26, 1317-1334. https://doi.org/10.1016/S0378-4266(02)00265-0
- Cai, Z.W. and Xian, W. (2008) Nonparametric Estimation of Conditional VAR and Expected Shortfall. Journal of Econometrics, 147, 120-130. https://doi.org/10.1016/j.jeconom.2008.09.005
- Taylor, J.W. (2008) Estimating Value at Risk and Expected Shortfalls Using expecteiles. Journal of Financial Econometrics, 6, 231-252. https://doi.org/10.1093/jjfinec/nbn001
- Cui, X.T., Zhu, S.S., Sun, X.L. and Duan, L. (2013) Nonlinear Portfolio Selection Approximate Parametric Value-at-Risk. Journal of Banking and Finance, 37, 2124-2139. https://doi.org/10.1016/j.jbankfin.2013.01.036