Midquotes or Transactional Data? The Comparison of Black Model on HF Data
AbstractThe main idea of this research is to check the efficiency of the Black option pricing model on the basis of HF emerging market data. However, liquidity constraints - a typical feature of an emerging derivatives market - put severe limits for conducting such a study. That is the reason why Kokoszczynski et al., 2010, have conducted their earlier research on midquotes data treating them as potential transactional data. They have got some intriguing conclusions about implementing different volatility processes into the Black option model. Nevertheless, taking into account that midquotes do not have to be the proper representation of market prices as probably transactional data do, we decide to compare in this paper the results of the research conducted on HF transactional and midquotes data. This comparison shows that the results do not differ significantly between these two approaches and that BIV model significantly outperforms other models, especially BRV model with the latter producing the worst results. Additionally, we provide the discussion of liquidity issue in the context of emerging derivatives market. Finally, after exclusion of spurious outliers we observe significant patterns in option pricing that are not visible on the raw data.
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Bibliographic InfoPaper provided by Faculty of Economic Sciences, University of Warsaw in its series Working Papers with number 2010-15.
Length: 30 pages
Date of creation: 2010
Date of revision:
option pricing models; financial market volatility; high-frequency financial data; midquotes data; transactional data; realized volatility; implied volatility; microstructure bias; emerging markets;
Find related papers by JEL classification:
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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Monash Econometrics and Business Statistics Working Papers
1/02, Monash University, Department of Econometrics and Business Statistics.
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