GR Semicolon EN

Show simple item record

dc.contributor.author
Kontaratou, Chrysi Christina
en
dc.date.accessioned
2016-12-19T14:04:40Z
dc.date.available
2016-12-20T01:00:15Z
dc.date.issued
2016-12-19
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/14569
dc.rights
Default License
dc.subject
Value at Risk
en
dc.subject
Backtesting
en
dc.subject
ARCH
en
dc.subject
AIC
en
dc.subject
SBIC
en
dc.title
Value at Risk: An analysis for the European Stock Exchanges
en
heal.type
masterThesis
en
heal.keywordURI.LCSH
Risk management
heal.keywordURI.LCSH
Bank investments
heal.keywordURI.LCSH
Stock exchanges
heal.keywordURI.LCSH
Stock exchanges--Europe
heal.keywordURI.LCSH
Financial risk management
heal.keywordURI.LCSH
Stocks--Europe
heal.keywordURI.LCSH
Financial futures
heal.keywordURI.LCSH
Finance--Mathematical models
heal.language
en
en
heal.access
free
el
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en
heal.references
Akaike, H. (1974), A new look at the statistical model identification , IEEE Transactions on Automatic Control, 19 (6), 716–723 Angelidis, T., and Skiadopoulos, G. (2008). Measuri ng the Market Risk of Freight Rates: A Value-at-Risk Approach. International Journal of Theoretical and Applied Finance, 11 (5), 447-469. Angelidis, T., and Degiannakis, S. (2008). Volatili ty Forecasting: Intra-day vs. Inter-day Models. Journal of International Financial Markets, Institutions an d Money, 18, 449- 465 . Angelidis, T., and Degiannakis, S. (2007). Backtest ingVaRModels: A Two-Stage Procedure. The Journal of Risk Model Validation, 1 (2), 1-22. Angelidis, T., and Degiannakis, S. (2007). Volatili ty Forecasting: Intraday versus inter- day models. The Journal of International Financial Markets, Ins titutions and Money. 18 (2008) 449-465 . Angelidis, T., Benos, A., and Degiannakis, S. (2006 ). A Robust VaRModel Under Different Time Periods and Weighting Schemes. Review of Quantitative Finance and Accounting, 28, 187-201. Angelidis, T., and Benos, A.(2006). Liquidity Adjus ted Value-at-Risk based on the components of the bid-ask spread. Applied Financial Economics, 16 (11), 835-851 . Angelidis, T., Benos, A., and Degiannakis, S. (2004 ). The Use of GARCH Models in VaREstimation. Statistical Methodology 1, 105-128. Artzner, P., Delbaen, F., Eber, J-M., and Heath D. (1997 ). Thinking Coherently . Risk 10, 68-71 Artzner, P., Delbaen, F., Eber, J-M., and Heath D. (1999). Coherent Measures of Risk. Mathematical Finance, Vol. 9, No. 3 (July 1999), 20 3–228. Bangia, A., Diebold, F. X., Schuermann, T. and Stro ughair, J. (1999). Modeling liquidity risk, with implications for traditional m arket risk measurement and management. The Wharton Financial Institutions Center WP 99–06. 46 Bali, TG., and Theodossiou, P., (2006. A conditiona l-SGT-VaR approach with alternative GARCH models . Ann Oper Res, forthcoming. Barone Adesi, and G., Giannopoulos, K. (1999). The Case for Non Parametric Market Risk Measures. Risk Professional Journal Barone-Adesi, G., Giannopoulos, K., and L. Vosper, (1999). VaR without Correlations for Nonlinear Portfolio . Journal of Futures Markets, 19 (April), 583 - 602. Basak, S. and A. Shapiro (2001). Value-at-risk-base d risk management: Optimal policies and asset prices. The Review of Financial Studies 14 (2), 371–405. Bekiros, DS., and Georgoutsos, AD. (2005) J Int Fin Mark, Inst Mone 15 (3), 209–228 Billio, M. and Pelizzon, L. (2000). Value-at-risk: a multivariate switching regime approach. J Empir Fin 7, 531–554 Bollerslev, T. (1986). Generalized autoregressive c onditional heteroskedasticity. J Econometrics 31, 307–327 Boudoukh, J., Richardson, M., and Whitelaw, R. (19 98). The Best of Both Worlds . Risk 64-67. Boucher, C., M., Daníelsson, J., Kouonchou, P. S., and Maillet, B. B. (2014). Risk models-at-risk . Journal of Banking and Finance, 44, 72–92. Bozdigan. H., (1987), Model selection and Akaike’s Information Criterion: The general theory and it’s analytical extensions phychometrica , Phycometrica Vol 52 No 3, 345- 370. Brooks, C.(2008). Introductory Econometrics for Finance ,Cambridge University Press Brooks, C., and Persand, G., (2003a), The effect of asymmetries on stock index return Value-at-Risk estimates . J Risk Fin, 29–42 Chen, S. X. (2008). Nonparametric estimation of exp ected shortfall. Journal of Financial Econometrics, 6, 87–107 Chan, KF., and Gray, P. (2006) Using extreme value theory to measure value-at-risk for daily electricity spot prices. Int J Forecasting 22(2), 283–300 Chavez-Demoulin, V., and Davisony, A. C. and McNei l, A. J.(2004) Estimating Value-at-Risk: A point process approach. Quantitative Finance Vol 5, p 227-234. 47 Christoffersen, P. (1998). Evaluating the internal forecasts, International Economic Review, 39, 841-862 Christoffersen, P. (2011). Elements of Financial Ri sk Management. Academic Press. Degiannakis, S. (2004). Volatility forecasting: evi dence from a fractional integrated asymmetric power ARCH Skewed-t model . App Fin Econ 14, 1333–1342 de la Pena, V. H., Rivera, R., and Ruiz-Mata, J. ( 2007). Quality control of risk measures: backtesting VaR models. Journal of Risk, 9(2), 39–54. Ding, Z. Granger, CWJ., and Engle, RF. (1993). A lo ng memory property of stock market returns and a new model. J Empiri Fin 1, 83–106 Dowd, Kevin., (2002), Measuring Market Risk , Wiley Finance, John Wiley & Sons Ltd Engle, RF. (1982). Autoregressive conditional heter oskedasticity with estimates of the variance of U.K. Inflation . Econometrica 50, 987–1008 Escanciano, J. C., and Olmo, J. (2010). Backtestin g parametric value-at-risk with estimation risk. Journal of Business and Economic Statistics, 28(1), 36–51. Escanciano, J. C., and Pei, P., (2012). Pitfalls in Backtesting Historical Simulation VaR models. CAEPR Working paper European Banking Authority, Available online at: https://www.eba.europa.eu/regulation-and-policy/mar ket-risk [Accessed 21 August 2016] Frey, R. and Michaud, P., (1997). The effect of GAR CH-type volatilities on prices and payoff-distributions of derivative assets–a simulat ion study . Preprint, ETH Zürich. Gencay, R., and Selcuk, R., (2004). Extreme value t heory and Value at Risk: Relative performance in emerging markets. International Journal of Forecasting 20, 287–303 Gencay, R., Selcuk, F., and Ulugulyagcı, A. (2003). High volatility, thick tails and extreme value theory in Value at Risk estimations. Insurance: Mathematics and Economics. Giot, P., (2005). Market risk models for intraday d ata. European Journal of Finance 11, 309–324 48 Giot, P. and Laurent, S. (2003b). Market risk in co mmodity markets: a VaR approach. Energy. Econ 25, 435–457 Guermat, C. and Harris, RDF. (2002) Forecasting val ue-at-risk allowing for time variation in the variance and kurtosis of portfolio returns . Int J Forecasting 18, 409–419 Gurrola, P., and Murphy, D. (2015). Filtered histor ical simulation Value at Risk models and their competitors. Working Paper No 525, Bank of England. Hendricks, D.,(1996), Evaluation of value-at-risk m odels using historical data . Econ Police Rev 2,39–70 Herrera, R. and Schipp, B., (2013) Value at risk fo recasts by extreme value models in a conditional duration framework. Journal of Empirical Finance 23 (2013) 33–47 Huang, YC., and Lin, B-. (2004). Value-at-risk anal ysis for Taiwan stock index futures: fat tails and conditional asymmetries in return inn ovations . Rev Quant Fin Account 22, 79–95 Hull, J., and A. White, 1998, Incorporating volatil ity updating for value-at-risk, Journal of Risk, no. 1, Fall, 5 - 19. Hwang, S. Y, and Woo. M.J., (2001), Threshold ARCH( 1) processes: asymptotic inference , Statistics and Probability Letters 53, (2001), 11-2 0. Insee, National Institute of Statistics and Economi c Studies, Available at: http://www.insee.fr/en/themes/info-rapide.asp?id=12 3 [ Accessed 22 August 2016] Jesus, R., Ortiz, E., and Cabello, A. (2013). Long run peso/dollar exchange rates and extreme value behavior: Value at Riskmodeling. The North American Journal of Economics and Finance, 24, 139–152. Jorion, C. (2007). Value at Risk , McGraw-Hill. JPMorgan, Reuters, (1996) ‘ Risk Metrics Technical Manual” 4 th Edition. Koopman, S.J., Jungbacker, B., and Hol, E., (2005). Forecasting daily variability of the S&P100 stock index using historical, realised and i mplied volatility measurements. Journal of Empirical Finance 12 (3), 445–475 . Kupiec, P.H, (1995). Techniques for verifying the a ccuracy of risk measurement models. Journal of Derivatives, 3, 73-84 49 Lopez, A. J. (1999). Regulatory evaluation of value -at-risk models. Journal of Risk, 1, 37–64. Nelson, D., (1991) Conditional heteroskedasticity i n asset returns: a new approach. Econometrica 59, 347–370 Nieto, Maria Rosa. and Ruiz, E. (2015). Frontiers i n VaR forecasting and backtesting. International Journal of Forecasting , 32 (2016) 475-501 Nieto, María Rosa,. Ruiz, Esther., (2010). Bootstra p prediction intervals for VaR and ES in the context of GARCH models. DES - Working Papers, Statistics and Econometrics WS 102814, Universidad Carlos III de M adrid O’Brien, J., and Szerszen P, J. An Evaluation of Ba nk VaR Measures for Market Risk During and Before the Financial Crisis . Federal Reserve Board. Pérignon, C., and Smith, D. R. (2008). A new approa ch to comparing VaR estimation method. The Journal of Derivatives, 16(2), 54–66. Pérignon, C., and Smith, D. R. (2010b). The level a nd quality of Value-at-risk disclosure by commercial banks. Journal of Banking and Finance, 34, 362–377. Pritsker, M., (2001), The Hidden danger of historic al Simulation . Federal Reserve Working Paper Sajjad, R., Coakley, J., and Nankervis, T. (2008). Markov Switching GARCH modelling of Value at Risk . Studies in Nonlinear Dynamics and Econometrics, 12 (3). Schwarz, G., (1978), Estimating the dimension of a model , Annals of Statistics, 6 (2): 461–464, Sheedy, E., Alexander, C., (2008). Developing a str ess testing framework based on market risk models. Journal of Banking & Finance 32 (2008) 2220–2236 Sener, E., Baronyan, S., and Mengütürk, L. A. (2012 ). Ranking the predictive performances of value-at-risk estimation methods . International Journal of Forecasting, 28(4), 849–873. Silva, A., and Mendes, B. (2003). Value-at-risk and extreme returns in Asian stock markets .International Journal of Business, 8, 17−40. Schwert, ., (1989. Why does stock market volatility change over time ?. J Fin 44, 1115– 1153 50 Statistisches Bundesamt, Available at: https://www. destatis.de/EN/Homepage.html [Accessed 22 August 2016] Taylor, S. (1986). Modeling financial time series. Wiley, New York Vlaar, P.,(2000), Value at risk models for Dutch bo nd portfolios. J Bank Fin 24, 131– 154 Yamai, Y. and T. Yoshiba (2005). Value-at-risk vers us expected shortfall: A practical perspective. Journal of Banking & Finance 29 (4), 997–1015. Zikovic, S., and Aktan, B. (2011). Decay factor opt imization in time weighted simulation — evaluating VaR performance. International Journal of Forecasting, 27 (4), 1147–1159.
en
heal.recordProvider
School of Economics, Business Administration and Legal Studies, MSc in Banking and Finance
en
heal.publicationDate
2016-12-18
heal.abstract
This dissertation focuses on the estimation of Value at Risk in six European Stock Exchanges from the beginning of the millennium. It presents the theoretical framework regarding the VaR techniques as well as the ARCH models which are commonly used in the estimation of market risk. On the empirical part, the dissertation provides an insight into parametric models like Risk Metrics and non parametric like Historical Simulation and in order to evaluate their predictive ability during the recent global financial crisis they are backtested. In addition, models of the ARCH family are being presented extensively since they are commonly used in the VaR forecasting procedure. The Akaike’s Information as well as the Schwarz’s Bayesian Information Criterion are examined so as to be concluded if the aforementioned models are trustworthy and could predict VaR accurately.
en
heal.tableOfContents
Abstract .......................................... ................................................... ............................................. 3 Introduction ...................................... ................................................... ........................................... 6 1. Value at Risk .................................. ................................................... ......................................... 7 1.1 VaR Methodologies ............................. ................................................... ................................. 8 2. Parametric models .............................. ................................................... ..................................... 8 2.1 Autoregressive Conditional Heteroskedastic Mod els ............................................... .............. 9 2.2 Generalized Autoregressive Conditional Heterosk edastic Models .................................... .... 11 3. Non parametric models .......................... ................................................... ............................... 12 3.1 Historical Simulation ......................... ................................................... ................................. 12 4. Semi parametric models ......................... ................................................... ............................... 14 4.1 Filtered Historical Simulation ................ ................................................... ............................. 15 4.2 Extreme Value Theory .......................... ................................................... .............................. 16 5. Backtesting .................................... ................................................... ........................................ 18 5.1 Unconditional coverage ........................ ................................................... .............................. 18 5.2 Independence coverage ......................... ................................................... .............................. 19 5.3 Conditional coverage .......................... ................................................... ................................ 19 6. Expected Shortfall ............................. ................................................... .................................... 21 7. Empirical Investigation and Methodology ........ ................................................... .................... 22 8. Data Analysis .................................. ................................................... ...................................... 24 8.1 Descriptive statistics ........................ ................................................... ................................... 24 8.2 Historical Simulation ......................... ................................................... ................................. 29 8.3 Filtered Historical Simulation ................ ................................................... ............................. 30 8.4 Expected Shortfall ............................ ................................................... ................................... 31 8.5 EVIEWS results ................................ ................................................... .................................. 32 8.5.1 ARCH ........................................ ................................................... ...................................... 32 8.5.2 GARCH........................................ ................................................... .................................... 34 8.5.3 EGARCH ...................................... ................................................... ................................... 36 8.5.4 APARCH ...................................... ................................................... ................................... 37 8.5.5 TARCH ....................................... ................................................... ..................................... 39 5 9. Comparison of the models and model selection ... ................................................... ................ 40 9.1 Akaike’s Information Criterion (AIC) .......... ................................................... ...................... 41 9.2 Schwarz’s Bayesian Information Criterion (SBIC) .................................................. ............. 42 Conclusions ....................................... ................................................... ........................................ 44 References ........................................ ................................................... ......................................... 45
en
heal.sponsor
State Scholarship Foundation; within the framework of the scholarship program for funding master studies in Greece with entry into the labour market for the academic year 2014-2015.
en
heal.advisorName
Angelidis, Timotheos
en
heal.committeeMemberName
Archontakis, Fragiskos
en
heal.committeeMemberName
Grose, Christos
en
heal.committeeMemberName
Angelidis, Timotheos
en
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en
heal.numberOfPages
50
en
heal.spatialCoverage
Europe
en


This item appears in the following Collection(s)

Show simple item record

Related Items