Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.794996
Title: Extracting information from option prices in the markets
Author: Lin, Han
ISNI:       0000 0004 8501 7492
Awarding Body: University of East Anglia
Current Institution: University of East Anglia
Date of Award: 2019
Availability of Full Text:
Access from EThOS:
Access from Institution:
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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.794996  DOI: Not available
Share: