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Measuring systemic risk in time-frequency domain
Název práce v češtině: Měření systémového rizika v časově-frekvenční doméně
Název v anglickém jazyce: Measuring systemic risk in time-frequency domain
Klíčová slova: systémové riziko, podmíněná hodnota v riziku, závislost na chvostech, DCC GARCH, waveletová analýza
Klíčová slova anglicky: systemic risk, conditional value at risk, tail dependence, DCC GARCH, wavelet analysis
Akademický rok vypsání: 2013/2014
Typ práce: diplomová práce
Jazyk práce: angličtina
Ústav: Institut ekonomických studií (23-IES)
Vedoucí / školitel: doc. PhDr. Jozef Baruník, Ph.D.
Řešitel: skrytý - zadáno vedoucím/školitelem
Datum přihlášení: 09.02.2014
Datum zadání: 09.02.2014
Datum a čas obhajoby: 23.09.2015 00:00
Místo konání obhajoby: IES
Datum odevzdání elektronické podoby:31.07.2015
Datum proběhlé obhajoby: 23.09.2015
Oponenti: prof. PhDr. Michal Bauer, Ph.D.
 
 
 
Seznam odborné literatury
Acharya, V.V.,Pedersen, L.H., Philippon, T. and Richardson, M.P. (2010), “Measuring Systemic Risk”, Denver Meetings Paper.

Adrian, T. and Brunnermeier M. K. (2010), “CoVar”, Report no. 348, Federal Reserve Bank of New York.

Billio, M., Getmansky, M., Lo, A. W. and Pelizzon, Y. (2010), “Econometric measures of systemic risk in the finance and insurance sectors”, NBER Working Paper 16223.

Bisias, D., Flood, M., Lo, Andrew W., Valavnis, S. (2012), “A Survey of Systemic Risk Analytics”. Office of Financial Research.

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

Han, H.and Linton, O. B. and Oka, T and Whang, Y. (2014), “The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series”.

Koenker, R., Bassett, G. (1978). “Regression quantiles”, Econometrica 46: 33-50.

Mittnik, S. (2013), “VaR-implied tail-correlation matrices”, CFS Working Paper Series 2013/05, Center for Financial Studies (CFS)“.

Zikes, F., & Barunik, J. (2013), “Semiparametric conditional quantile models for financial returns and realized volatility”.
Předběžná náplň práce
Out of all the approaches towards measuring systemic risk, I chose the analysis of returns of financial institutions in the tail of their distribution. First of all, I would like to analyze the tail dependence measures of systemic risk that have already been proposed, particularly Adrian’s CoVar (2010), Mittnik’s tail correlation matrices (2013) and Han’s cross quantilogram (2013). I would like to apply these approaches to datasets with data of particular financial institutions and perform different robustness checks and out-of-sample forecasts to determine strengths and weaknesses of the models at hand. I consider this to be the preparatory part, where I will become familiar with both the data and the estimation techniques developed already. Should I identify any weakness of the measure which could be corrected for, I would like to design a new riskometer, either by merging different features of proposed models into a new one or by a direct adjustment of one of the existing measures.

One of the ideas is to follow the reasoning of Barunik & Zikes (2013) and include realized volatility into the quantile regression used to estimate Adrian’s CoVar. The quantile regression theory introduced by Koenker and Bassett (1978) provides a convenient tool for robust inference over observations drawn from heavy-tailed or skewed distributions. The appeal of using realized volatility to measure variation in asset returns lies in its non-parametrical nature and ease of estimation, once high-frequency data are at hand.

In the first part of my work, I would like to use a dataset consisting of daily returns of financial institutions to analyze comparative performance of proposed measures. In addition, studied models use 3-month T-rate, repo rate, VIX index and CRSP index in their estimation. Later, for the purpose of realized volatility estimation, I need high frequency data of returns of the institutions.

1. Motivation: there is no consensus on usage of a particular systemic risk measure, different approaches proposed.
2. Literature review of studies on tail risk measures
3. Application and comparison of proposed measures, out-of-sample forecast
4. Designing an alternative measure – theoretical approach
5. Application, robustness checks
6. Comparison against proposed benchmarks
7. Concluding remarks
Předběžná náplň práce v anglickém jazyce
Out of all the approaches towards measuring systemic risk, I chose the analysis of returns of financial institutions in the tail of their distribution. First of all, I would like to analyze the tail dependence measures of systemic risk that have already been proposed, particularly Adrian’s CoVar (2010), Mittnik’s tail correlation matrices (2013) and Han’s cross quantilogram (2013). I would like to apply these approaches to datasets with data of particular financial institutions and perform different robustness checks and out-of-sample forecasts to determine strengths and weaknesses of the models at hand. I consider this to be the preparatory part, where I will become familiar with both the data and the estimation techniques developed already. Should I identify any weakness of the measure which could be corrected for, I would like to design a new riskometer, either by merging different features of proposed models into a new one or by a direct adjustment of one of the existing measures.

One of the ideas is to follow the reasoning of Barunik & Zikes (2013) and include realized volatility into the quantile regression used to estimate Adrian’s CoVar. The quantile regression theory introduced by Koenker and Bassett (1978) provides a convenient tool for robust inference over observations drawn from heavy-tailed or skewed distributions. The appeal of using realized volatility to measure variation in asset returns lies in its non-parametrical nature and ease of estimation, once high-frequency data are at hand.

In the first part of my work, I would like to use a dataset consisting of daily returns of financial institutions to analyze comparative performance of proposed measures. In addition, studied models use 3-month T-rate, repo rate, VIX index and CRSP index in their estimation. Later, for the purpose of realized volatility estimation, I need high frequency data of returns of the institutions.

1. Motivation: there is no consensus on usage of a particular systemic risk measure, different approaches proposed.
2. Literature review of studies on tail risk measures
3. Application and comparison of proposed measures, out-of-sample forecast
4. Designing an alternative measure – theoretical approach
5. Application, robustness checks
6. Comparison against proposed benchmarks
7. Concluding remarks
 
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