Modelling Conditional Quantiles of CEE Stock Market Returns

• Thesis title in Czech: Modelling Conditional Quantiles of CEE Stock Market Returns
• Thesis title in English: Modelling Conditional Quantiles of CEE Stock Market Returns
• Key words: VaR, vysokofrekvenčné dáta, ekonomická predpoveď, podmienené kvantily, kvantilová regresia
• English key words: VaR, high-frequency data, economic forecast, conditional quantiles, quantile regression
• Academic year of topic announcement: 2013/2014
• Thesis type: diploma thesis
• Thesis language: angličtina
• Department: Institute of Economic Studies (23-IES)
• Supervisor: doc. PhDr. Jozef Baruník, Ph.D.
• Author: hidden - assigned by the advisor
• Date of registration: 11.06.2014
• Date of assignment: 11.06.2014
• Date and time of defence: 23.06.2015 00:00
• Venue of defence: IES
• Date of electronic submission: 15.05.2015
• Date of proceeded defence: 23.06.2015
• Opponents: PhDr. Jiří Kukačka, Ph.D.

Preliminary scope of work

Správne definované modely na predpovedanie výnosov indexov sú dôležité pre investorov, kvôli minimalizovaniu rizika na finančných trhoch. Táto práca sa zameriava na podmiené modelovanie Value at Risk, ktorá využíva rámec flexibilnej kvantilovej regresie, a tým sa môže vyhnúť predpokladu o normálne rozdelených výnosoch. Aplikujeme semiparametrickú lineárnu regresiu kvantilov (LQR) s realizovaným rozptylom a tiež model s pozitívnou a negatívnou semivarianciou, ktorá umožňuje priame modelovanie kvantilov. Do úvahy berieme ceny štyroch európskych akciových indexov: českého PX, maďarského BUX, nemeckého DAX a londýnskeho FTSE 100. Naším cieľom je zistiť, ako použitie realizovaných rozptylov ovplyvňuje presnosť VaR a koreláciu medzi strednou a východnou Európou so západoeuróskymi indexmi. Hlavným prínosom práce je aplikácia modelov LQR pre modelovanie podmienených kvantilov
a porovnanie korelácie medzi európskymi indexmi s využitím realizovaných mier.

Hlavná bibliografie
ANDERSEN, T. G., T. BOLLERSLEV, F. X. DIEBOLD, & P. LABYS (2003): “Modeling and forecasting realized volatility.” Technical Report issue 2.
CHRISTOFFERSEN, P., V. R. ERRUNZA, K. JACOBS, & L. HUGUES (2010): “Is the potential for international diversification disappearing?” SSRN Electronic Journal.
HUA, J. & S. MANZAN (2013): “Forecasting the return distribution using high-frequency volatility measures.” Journal of Banking & Finance Vol. 37(No.11).
KOENKER, R. & J. GILBERT BASSETT (1978): “Regression quantiles.” Econometrica vol. 46(no. 1): pp. pp. 33–50.
PATTON, A. J. & K. SHEPPARD (2011): “Good volatility, bad volatility: Signed jumps and the persistence of volatility.” Economic Research Initiatives at Duke (ERID) Working Paper (no. 168).
ZIKES, F. & J. BARUNIK (2014): “Semiparametric conditional quantile models for financial returns and realized volatility.” Journal of Financial Econometrics.

Preliminary scope of work in English

Correctly specified models to forecast returns of indices are important for investors to minimize risk on financial markets. This thesis focuses on conditional Value at Risk modeling, employing flexible quantile regression framework and hence avoiding the assumption on the return distribution. We apply semiparametric linear quantile regression (LQR) models with realized variance and also models with positive and negative semivariance which allows for direct modelling of the quantiles. Four European stock price indices are taken into account: Czech PX, Hungarian BUX, German DAX and London FTSE 100. The objective is to investigate how the use of realized variance influence the VaR accuracy and the correlation between the Central & Eastern and Western European indices. The main contribution is application of the LQR models for modelling of conditional quantiles and comparison of the correlation between European indices with use of the realized measures.

Core bibliography
ANDERSEN, T. G., T. BOLLERSLEV, F. X. DIEBOLD, & P. LABYS (2003): “Modeling and forecasting realized volatility.” Technical Report issue 2.
CHRISTOFFERSEN, P., V. R. ERRUNZA, K. JACOBS, & L. HUGUES (2010): “Is the potential for international diversification disappearing?” SSRN Electronic Journal.
HUA, J. & S. MANZAN (2013): “Forecasting the return distribution using high-frequency volatility measures.” Journal of Banking & Finance Vol. 37(No.11).
KOENKER, R. & J. GILBERT BASSETT (1978): “Regression quantiles.” Econometrica vol. 46(no. 1): pp. pp. 33–50.
PATTON, A. J. & K. SHEPPARD (2011): “Good volatility, bad volatility: Signed jumps and the persistence of volatility.” Economic Research Initiatives at Duke (ERID) Working Paper (no. 168).
ZIKES, F. & J. BARUNIK (2014): “Semiparametric conditional quantile models for financial returns and realized volatility.” Journal of Financial Econometrics.