Extracting information from option prices in the markets

Lin, Han (2019) Extracting information from option prices in the markets. Doctoral thesis, University of East Anglia.

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Abstract

By their nature, options markets are forward-looking. The riskneutral densities (RND) provide information on market’s view regarding the future movements of the underlying index and the perception of the risk. In Chapter 2, we use S&P 500 index option prices and the recently introduced China’s 50 Exchange-Traded Fund options to extract densities and find that all methods adopted fit both option data well. However, the non-parametric method outperforms the parametric approaches on the basis of RMSE, MAE, and also the MAPE. We also investigate the dynamic behavior of the densities from smoothing the implied volatility smile in both markets, especially the impacts of higher moments on the price levels and returns of underlying assets. Chapter 3 examines the impact of macroeconomic announcements on S&P 500 option prices and 50 ETF option prices. We aim to distil information with the RND from both options data by employing the stochastic volatility inspired (SVI) method. We investigate the densities and test market efficiency based on the impact of implied moments on current returns. Furthermore, we also distinguish between types of the macroeconomic indicators and examine the reactions of RNDs. In Chapter 4, we apply the Recovery Theorem of Ross (2015) to deduce both the physical distribution and pricing kernel from option prices. The time-homogeneity and irreducibility of the Markov Chain and the path-independence in pricing kernel are two main restrictions. This study aims to test the efficiency of the Recovery Theorem with the application to the options written on Adidas AG. The interpretation of risk aversion and real-world probability distribution is provided. Chapter 5 concludes.

Item Type: Thesis (Doctoral)
Faculty \ School: Faculty of Social Sciences > School of Economics
Depositing User: Users 11011 not found.
Date Deposited: 14 Nov 2019 11:26
Last Modified: 14 Nov 2019 11:26
URI: https://ueaeprints.uea.ac.uk/id/eprint/72963
DOI:

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