Analysis of Extreme Risk Transfer across Selected Financial Markets with Application of Granger Causality in Risk

Marcin Fałdziński

doi:https://doi.org/10.59139/ps.2014.04.1

Marcin Fałdziński Uniwersytet Mikołaja Kopernika w Toruniu Przegląd Statystyczny. Statistical Review, vol. 61, 2014, 4, pages: 433-448 Published online: 31 December 2014 DOI https://doi.org/10.59139/ps.2014.04.1

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ARTICLE

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ABSTRACT

The main aim of this paper is an analysis of integration among main financial markets which represent financial and economic processes occurring in the contemporary world. This research focuses on issue of extreme risk spillover effect on financial markets. Proper understanding of risk transfer mechanism has paramount importance for effective risk management, financial institutions and market supervision institutions. In particular, extreme risk is the most important due the fact that the extreme prices movements are the main cause of threats and opportunities for market participants. This paper is the extension of previous researches on that issue. This extension takes into account the newest methodology framework which is Granger-causality test presented in work of Candelon et al. (2013). Innovation in this approach boils down to allowing for multiple different risk levels across distribution tails which takes into consideration different dynamics of risk transfer mechanism across markets. In order to estimate Value-at-Risk a Peaks over Threshold approach is applied with volatility models (McNeil, Frey, 2000).

KEYWORDS

Granger-causality in risk, Value-at-Risk, extreme risk, spillover effect, Peaks over Threshold (POT) method

REFERENCES

Bekiros S. D., Georgoutsos D. A., (2005), Estimation of Value-at-Risk by Extreme Value and Conventional Methods: A Comparative Evaluation of their Predictive Performance, Journal of International Financial Markets, Institutions and Money, 15 (3), 2009–2028.

Bystrom H., (2004), Managing Extreme Risks in Tranquil and Volatile Markets Using Conditional Extreme Value Theory, International Review of Financial Analysis, 13 (2), 133–152.

Candelon B., Joëts M., Tokpavi S., (2013), Testing for Granger Causality in Distribution Tails: An Application to Oil Markets Integration, Economic Modelling, 31, 276–285.

Coles S., (2001), An Introduction to Statistical Modeling of Extreme Values, Springer, London.

Dufour J.-M., (2006), Monte Carlo Tests with Nuisance Parameters: a General Approach to Finite Sample Inference and Nonstandard Asymptotics, Journal of Econometrics, 27 (2), 443–477.

Engle R. F., Manganelli S., (2004), CAViaR: Conditional Autoregressive Value-at-Risk by Regression Quantile, Journal of Business and Economic Statistics, 22, 367–381.

Fałdziński M., Osińska M., Zdanowicz T., (2012), Detecting Risk Transfer in Financial Markets using Different Risk Measures, Central European Journal of Economic Modelling and Econometrics, 4 (1), Polish Academy of Sciences – Oddział Łódź, 45–64.

Fałdziński M., (2014), Teoria Wartości Ekstremalnych w ekonometrii finansowej, Wydawnictwo UMK, Toruń.

Ghorbel A., Trabelsi A., (2008), Predictive Performance of Conditional Extreme Value Theory in Value-at-Risk Survey, International Journal of Monetary Economics and Finance, 1 (2), 121–147.

Harmantzis F. C., Miao L., Chien Y., (2006), Empirical Study of Value-at-Risk and Expected Shortfall Models with Heavy Tails, Journal of Risk Finance, 7 (2), 117–126.

Hong Y., (2003), Extreme Risk Spillover Between Chinese Stock Markets and International Stock Markets, Working Paper, http://www.wise.xmu.edu.cn/oldversion/downloadfile.asp?id=93, (05.09.2014).

Hong Y., Liu Y., Wang S., (2009), Granger Causality in Risk and Detection of Extreme Risk Spillover between Financial Markets, Journal of Econometrics 150 (2), 271–287.

Kuester K., Mittik S., Paolella M. S., (2006), Value-at-Risk Prediction: a Comparison of Alternative Strategies, Journal of Financial Econometrics, 4 (1), 53–89.

Lee J., Lee H., (2009), Testing for Risk Spillover between Stock Market and Foreign Exchange Market in Korea, Journal of Economic Research, 14, 329–340.

McNeil J. A., Frey F., (2000), Estimation of Tail-Related Risk Measures for Heteroscedastic Financial Time Series: an Extreme Value Approach, Journal of Empirical Finance, 7, 271–300.

Lardy N., (1998), China and the Asian Contagion, Foreign Affairs, 77, 78–88.

Osińska M., (2006), Ekonometria finansowa, Polskie Wydawnictwo Ekonomiczne, Warszawa.

Osińska M., Fałdziński M., Zdanowicz T., (2012), Econometric Analysis of the Risk Transfer in Capital Markets. The Case of China, Argumenta Oeconomica, 2 (29)–2012, Uniwersytet Ekonomiczny we Wrocławiu, 139–164.

Osińska M., Fałdziński M., (2013), Are Currencies in Central Asian States Related? An Econometric Study of Granger Causality in Risk, Statistica Učet Audit, 4 (51), 91–97.

Osińska M., Fałdziński M., (2007), Modele GARCH i SV z zastosowaniem teorii wartości ekstremalnych, Dynamiczne Modele Ekonometryczne, Wydawnictwo Uniwersytetu Mikołaja Kopernika, 10, 27–34.

Ozun A., Cifter A., Yilmazer S., (2010), Filtered Extreme Value Theory for Value-at-Risk Estimation: Evidence from Turkey, Journal of Risk Finance Incorporating Balance Sheet, 11 (2), 164–179.

Peek J., Rosengre E. S., (1997), The International Transmission of Financial Shocks: The Case of Japan, The American Economic Review, 87, 495–505.

Wang L, (2014), Study on the Extreme Risk Spillover between China and World Stock Market after China’s Share Structure Reform, Journal of Financial Risk Management, 3, 50–56.

Zakoian J.-M., (1994), Threshold Heteroscedastic Models, Journal of Economic Dynamics and Control, 18 (5), 931–955.