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2010 | nr 143 | 214--226
Tytuł artykułu

Porównanie modeli wyceny opcji na danych wysokiej częstotliwości

Warianty tytułu
Option Pricing Models whith HF Data
Języki publikacji
PL
Abstrakty
Artykuł ma charakter metodyczny, gdyż ocenia właściwości teoretyczne estymatora zmienności zrealizowanej (realized volatility)w modelu Blacka. Efektywność tego mdelu poddano ocenie przy wykorzystaniuuśrednionej zmienności zrealizowanej lub przy zastosowaniu interwału delta ( w przedziale testowym 10s - 15 m). (fragment tekstu)
EN
Through the analysis of Black option pricing model with realized Volatility (RV) on HF data (lOs data for WIG20 index options in the first half of 2008 year) we present the properties of RV estimator. The detailed attention is put to utilization of RV as a sigma parameter in option pricing model. The properties of realized Volatility analysed in details by Andersen et al. (2001 a, 2001 b, 2003), Martens and Dijk (2007) or Ślepaczuk and Zakrzewski (2009) enable to formulate the hypothesis, that the standard version of RV estimator (annualized sum of squared logarithmic rates of return calculated on the basis of HF data on interval delta) is characterized by high Volatility of Volatility. This feature significantly affects the efficiency of Black option pricing model with realized Volatility. The properties of realized Volatility can be significantly modified if RV estimator will be averaged or calculated on the basis of different time interval. In the first case, the properties of averaged RV estimator makes it similar to the classical standard deviation, reducing Volatility of Volatility, while in the second case, manipulating with the interval delta (at least within the tested interval: from l Os to 15m) we do not affect the properties of RV estimator. We analyse the relation-ship mentioned above and describe the influence of assumption taken in the process of RV calculation on the efficiency of Black option pricing model. At the end we try to define some characteristics of Volatility estimator, which will be the most appropriate in the option pricing model. (original abstract)
Rocznik
Numer
Strony
214--226
Opis fizyczny
Twórcy
  • Uniwersytet Warszawski
  • Uniwersytet Warszawski
  • Uniwersytet Warszawski
  • Uniwersytet Warszawski
  • Uniwersytet Warszawski
Bibliografia
  • Ait-Sahalia Y., Mykland P.A., Zhang L. (2005), How often to sample a continuous-time process in the presence of market microstructure noise, Review of Financial Studies, 18,351-416.
  • Ammann M., Skovmand D., Yerhofen M. (2009), Implied and realized volatility in the cross-section of equity options, International Journal of Theoretical and Applied Finance, 12, 745-765.
  • Andersen T.G., Bollerslev T. (1998), Answering the Skeptics: Yes, Standard Yolatility Models do Provide Accurate Forecasts, International Economic Review, 39, No.4, 885-905.
  • Andersen!., Andersen T.G., Bollerslev T., Diebold F.X., Labys P. (1999a), Realized volatility and correlation, Manuscript in progress.
  • Andersen T.G., Bollerslev T., Diebold F.X., Labys P. (1999b), Microstructure Bias and Volatility Signatures, Manuscript in progress.
  • Andersen, T.G. (2000), Some reflections on analysis of high-frequency data, Journal of Business & Economic Statistics, 18, 146-153.
  • Andersen T.G., Bollerslev T., Diebold F.X, Ebens H. (2001 a), The Distribution of Realized Stock Return Volatility, Journal of Financial Economics, 61, 43-76.
  • Andersen, T., Bollerslev T., Diebold F., Labys P. (2001 b), The Distribution of Realized Exchange Rate Volatility, Journal of American Statistical Association 96, 42-55.
  • Andersen T.G., Bollerslev T., Diebold F.X., Labys P. (2003), Modeling and Forecasting Realized Volatility, Econometrica, 71, 579-625.
  • Andersen T.G., Frederiksen P., Staal A.D. (2007), The Information content of realized volatility forecasts, mimeo.
  • Bakshi G., KapadiaN., Madan D. (2003), Stock return characterisics, skew laws and differential pricing of individual equity options, Review of Financial Studies, 16, 101-143.
  • Black F. (1976), The pricing of commodity contracts, Journal of Financial Economics, 3, 167-179.
  • Black F., Scholes M. (1973), The pricing of options and corporate liabilities, Journal of Political Economy, 81, 637-659.
  • Christensen B.J., PrabhalaN.R. (1998), The relation between implied and realized volatility, Journal of Financial Economics, 50, 125-150.
  • Garman M., Klass M. (1980), On the estimation of security price volatilities from historical data, The Journal of Business, 53, 67-78.
  • Hansen P.R., Lunde A. (2006b), Realized variance and market microstructure noise, Journal of Business and Economic Statistics 24, 127-218 (with comments and rejoinder).
  • Kokoszczyński R., NehrebeckaN., Sakowski P, Strawiński P, Ślepaczuk R. (2010), Option Pricing Models with HF Data. The Properties of Black Model with Realized Yolatility Measures, manuscript in progress.
  • Li S., Yang Q. (2009), The relationship between implied and realized volatility: evidence from the Australian stock market, Review of Quantitative Finance and Accounting, 32,405^419.
  • Martens M., Dijk van D. (2007), Measuring volatility with the realized rangę, Journal of Econometrics 138, 181-207.
  • Merton R. (1973), Theory ofr ational option pricing, Bell Journal of Economics and Management Science, 4, 141-183.
  • Mixon S. (2009), Option markets and implied volatility: Past versus present, Journal of Financial Economics, 94, 171-191.
  • Poon S., Granger C.W.J. (2003), Forecasting volatility in financial markets: A review, Journal of Economic Literature, 41, 478-539.
  • Raj M., Thurston D.C. (1998), Transactions data examination of the effectiveness of the Black model for pricing options on Nikkei index futures, Journal of Financial and Strategie Decisions, 11, 37^15.
  • Rogers L.C.G., Satchell S.E. (1991), Estimating variancefrom high, Iow andclosingprices, The Annals of Applied Probability l, 504-512.
  • Shu J., Zhang J. (2006), Testing rangę estimators of historical volatility, The Journal of Futures Markets 26, 297-313.
  • Ślepaczuk R., Zakrzewski G. (2009), High-frequency and model-free volatility estimators, University of Warsaw, Faculty of Economic Sciences,Working Papers 13/2009(23).
  • Yang D., Zhang Q. (2000), Drijt-independent volatihty estimation basedon high, Iow, open and close prices, The Journal of Business 73, 477-491.
  • Zhang L., Mykland P.A., Ait-Sahalia Y. (2005), A tale of two time scales: determining integrated volatility with noisy high-frequency data, Journal of the American Statistical Association 100, 1394-1411.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171200175

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