BazEkon - The Main Library of the Cracow University of Economics

BazEkon home page

Main menu

Author
Skoczylas Tomasz (University of Warsaw)
Title
Log-Volatility Enhanced GARCH Models for Single Asset Returns
Source
Bank i Kredyt, 2015, nr 5, s. 411-431, aneks, bibliogr. 20 poz.
Bank & Credit
Keyword
Zmienność, Analiza wartości zagrożonej, Model GARCH, Prognozowanie
Variability, Value at Risk Analysis, GARCH model, Forecasting
Note
summ.
Abstract
This paper presents an alternative approach to modelling and forecasting single asset return volatility. A new, flexible framework is proposed, one which may be considered a development of single-equation GARCH-type models. In this approach an additional equation is added, which binds logarithms of conditional volatility and observed volatility, as measured by the Garman-Klass variance estimator. It enables more information to be retrieved from data. Proposed models are compared with benchmark GARCH and range-based GARCH (RGARCH) models in terms of prediction accuracy. All models are estimated with the maximum likelihood method, using time series of EUR/PLN, EUR/USD, EUR/GBP spot rates quotations as well as WIG20, Dow Jones industrial and DAX indexes. Results are encouraging, especially for foreasting Value-at-Risk. Log-volatility enhanced models achieved lesser rates of VaR exception, as well as lower coverage test statistics, without being more conservative than their single-equation counterparts, as their forecast error measures are to some degree similar.(original abstract)
Accessibility
The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
The Main Library of Poznań University of Economics and Business
The Main Library of the Wroclaw University of Economics
Full text
Show
Bibliography
Show
  1. Alizadeh S., Brandt M., Diebold F. (2001), Range-based estimation of stochastic volatility models, Journal of Finance, 57, 1047-1091.
  2. Beckers S. (1983), Variances of security price returns based on high, low, and closing prices, Journal of Business, 56, 97-112.
  3. Brandt M., Jones C. (2006), Volatility forecasting with range-based EGARCH model, Journal of Business and Economic Statistics, 24, 470-486.
  4. Broto C., Ruiz E. (2004), Estimation methods for stochastic volatility model: a survey, Journal of Economic Surveys, 18(5), 613-649.
  5. Bollerslev T. (2008), Glossary to ARCH(GARCH), CREATES Research Paper, 49.
  6. Chou R. (2005), Forecasting financial volatilities with extreme values: the conditional autoregressive range (CARR) model, Journal of Money Credit and Banking, 37, 561-582.
  7. Fiszeder P., Perczak P. (2013), Model GARCH - wykorzystanie dodatkowych informacji o cenach minimalnych i maksymalnych, Bank i Kredyt, 45(2), 105-132.
  8. Garman M., Klass M. (1980), On the estimation of security price volatilities from historical data, Journal of Business, 53, 67-78.
  9. Harvey A., Shephard N. (1996), Estimation of an asymmetric stochastic volatility model for asset returns, Journal of Business & Economic Statistics, 14(4), 429-434.
  10. Li K., Weinbaum D. (2001), The empirical performance of alternative extreme value volatility estimators, New York University - Salomon Center - Leonard N. Stern School of Business.
  11. Lildholdt P. (2003), Estimation of GARCH models based on open, close, high, and low prices, mimeo.
  12. Molnar P. (2011), High-low range in GARCH models of stock return volatility, EFMA Annual Meetings, Barcelona.
  13. Parkinson M. (1980), The extreme value method for estimating the variance of the rate of return, Journal of Business, 53, 61-65.
  14. Patton A.J. (2011), Volatility forecast comparison using imperfect volatility proxies, Journal of Econometrics, 160(1), 246-256.
  15. Rogers L., Satchell S. (1991), Estimating variance from high, low and closing prices, Annals of Applied Probability, 1, 504-512.
  16. Shephard N., Andersen T.G. (2009), Stochastic volatility: origins and overview, in. T.G. Andersen, R.A. Davis, J.P. Kreiss, Th.V. Mikosch (eds.) Handbook of financial time series, Springer Berlin Heidelberg.
  17. Skoczylas T. (2013), Modelowanie i prognozowanie zmienności przy użyciu modeli opartych o zakres wahań, Ekonomia, 35, 65-80.
  18. Taylor S. (1986), Modeling financial time series, Wiley, Chichester.
  19. Terasvirta T. (2009), An introdution to univariate GARCH models, T.G. Andersen, R.A. Davis, J.P. Kreiss, Th.V. Mikosch (eds.) Handbook of financial time series, Springer Berlin Heidelberg.
  20. Wiggins J. (1991), Empirical tests of the bias and efficiency of the extreme-value variance estimator for common stocks, Journal of Business, 64, 417-432.
Cited by
Show
ISSN
0137-5520
Language
eng
Share on Facebook Share on Twitter Share on Google+ Share on Pinterest Share on LinkedIn Wyślij znajomemu