Copula-GARCH模型下的两资产期权定价

Access Check

Our systems have detected unusual traffic activity from your network. Please complete this reCAPTCHA to demonstrate that it's you making the requests and not a robot. If you are having trouble seeing or completing this challenge, this page may help. If you continue to experience issues, you can contact JSTOR support.

Block Reference: #a5e4c1db-1fde-11ed-b3a5-686962565262
VID: #
IP: 188.18.253.215
Date and time: Fri, 19 Aug 2022 16:47:44 GMT

A copula–GARCH model for macro asset allocation of a portfolio with commodities

Many authors have Copula-GARCH模型下的两资产期权定价 suggested that the mean-variance criterion, conceived by Markowitz (The Journal of Finance 7(1):77–91, 1952), is not optimal for asset allocation, because the investor expected utility function is better proxied by a function that uses higher moments and because returns are distributed in a non-Normal way, being asymmetric and/or leptokurtic, so the mean-variance criterion cannot correctly proxy Copula-GARCH模型下的两资产期权定价 the expected utility with non-Normal returns. In Riccetti (The use of copulas in asset allocation: when and how a copula model can be useful? LAP Lambert, Saarbrücken 2010), a copula–GARCH model is applied and it is found that copulas are not useful for choosing among stock indices, but can be useful in a macro asset allocation model, that is, for choosing stock and bond composition of portfolios. In this paper I apply that copula–GARCH model for the macro asset allocation of portfolios containing a commodity component. I find that the copula model appears to be useful and better than the mean-variance one for the macro asset allocation Copula-GARCH模型下的两资产期权定价 also in presence of a commodity index, even if it is not better than GARCH models on independent univariate series, probably because of the low correlation of the commodity index returns to the stock, the bond and the exchange rate returns.

GARCH copula quantile regression model for risk spillover analysis

We propose a new GARCH copula quantile regression-based CoVaR model.Copula-GARCH模型下的两资产期权定价

We investigate the risk spillovers from four financial markets to the financial system of developed market.

We find that Germany exhibits the largest risk spillovers, followed by France, the US and the UK.

We also find that the risk spillovers are much larger during the COVID-19 pandemic than during the periods of the financial crisis and sovereign debt Copula-GARCH模型下的两资产期权定价 crisis.

Abstract

To assess risk spillovers, this paper proposes a new GARCH copula quantile regression-based CoVaR model in which the nonlinear tail dependence is allowed to change with risk levels. Based on MSCI index daily data, we investigate the risk spillovers from four financial markets to the financial system of developed market. We find that Germany displays the largest risk spillovers, followed by France, the US and the UK, and that the risk spillovers are much larger during the COVID-19 pandemic than during the periods of the financial crisis and sovereign debt crisis.

Copula-GARCH Analysis of Chinese Stock Market Dependence Structure

In recent five years, Chinese stock market experienced unprecedented prosperity and slump, which provides valuable data for research on market action in extreme situations. This paper analyzes the correlations between returns of five indices (industrial index, financials index, metals index, property index, Shenzhen Composite Index) and SSE Copula-GARCH模型下的两资产期权定价 Composite Index from 2006 to 2011. We adopt Copula method combined with GARCH-t process to construct a Copula-GARCH model and use this model to analyze static and time varying correlations. The static analysis shows that t-Copula functions fit the significant tail dependence best. The dynamic analysis shows that correlation parameters of each index have similar trends, but the levels of variations are different which indicates that the macro-environment exerts severer influence on financial and property sectors in last five years.

Keywords

GARCH model
Copula
Correlation analysis
Chinese Stock indices

This is a preview of subscription content, access via your institution.

Buying options

eBook EUR 234.33 Price includes VAT (Russian Federation)

Softcover Book EUR 279.99 Price excludes VAT (Russian Federation)

Preview

Unable to display preview. Download preview PDF.

References

Patton, A.J.: Estimation of multivariate models for time series of possibly different lengths. Journal of Applied Econometrics 21, 147–173 (2006)

Jondeau, E., Rockinger, M.: The copula-GARCH model of conditional Copula-GARCH模型下的两资产期权定价 dependencies: An International stock-market application. Journal of International Money and Finance 25(5), 827–853 (2006)

Sklar, A.: Fonctions de repartition àn dimensions et leursmarges. Publication de l′Institut de Statistique de l′Université de Paris 8, 229–231 (1959)

Joe, H.: Multivariate. Models and Dependence Concepts, Chapman&Hall (1997)

Nelsen, R.B.: An Introduction to Copulas, 2nd edn. Springer, New York (2006)

Frey, R., McNeil, A.: Modeling Dependent Defaults. Journal of Risk 6(1) (2003)

Patton, A.J.: Onthe Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation. Journal of Financial Econometrics 2(1), 130–168 (2004)

Kole, E., Koedijk, K., Verbeek, M.: Selecting Copulas for Risk Management. Journal of Banking & Finance 31(8), 2405–2423 (2007)

Author information

Authors and Affiliations

International Copula-GARCH模型下的两资产期权定价 School of Software, Wuhan University, 430079, Wuhan, China

Economics and Management School, Wuhan University, 430072, Wuhan, China

Sun Huang, Wang Chun & Wang Ying

H. J. Cai

You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

, School of Electronic Engineering, Wuhan Institute of Technology, Lvting yajing 10-3-102, Wuhan, 430079, China, People's Republic

Rights and permissions

Copyright information

About this paper

Cite this paper

Cai, H.J., Huang, S., Chun, W., Ying, W. (2012). Copula-GARCH Analysis of Chinese Stock Market Dependence Structure. In: Tan, H. (eds) Technology for Education and Learning. Advances in Intelligent Systems and Computing, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27711-5_78

Download citation

Publisher Name : Springer, Berlin, Heidelberg

Print ISBN : 978-3-642-27710-8

Online ISBN : 978-3-642-27711-5

Share this paper

Anyone you share the Copula-GARCH模型下的两资产期权定价 following link with will be able to read this content: