Browsing by Author "Hanani Farhah binti Harun"

Volatility implied by an option pricing model is seen as the market participants’ assessment of volatility. With the implied volatility as a significant aspect particularly in option valuation, this study examines the implied volatility smiles and term structures in the Australian Standard and Poor/Australian Stock Exchange (S&P/ASX) 200 index options over the course of 2001-2010, including the global financial crisis in the mid-2007 until the end of 2008. This study utilised the models of Leland (1985) and Leland (2007).The results show that the implied volatility rises significantly during the crisis period, which is more than the rate found before the crisis. Given the fact that the pricing biases of Leland option pricing models and the implied volatility structure of the option are related, secondly, this research adapts and modifies the models in Peña, Rubio and Serna (1999) and Engström (2002), in order to analyse whether the use of the implied adjusted volatility functions delivers an improvement in the option valuation accuracy of the index options. Both in- and out-of-sample resulted in approximately similar pricing error along the different Leland’s models. This study employs 2-step process. Results indicate that different implied adjusted volatility functions best explain the index options in different period of intervals (pre-, during and post-crisis). This shows that it is tremendously important to identify the intervals beforehand in investigating the implied adjusted volatility function.

Deciding an optimum asset allocation strategy is crucial, especially in view of market participants. However, to effectively decide an accurate strategy requires stable and unbiased portfolio, which can be achieved by reduced potential estimation error, an improved governing option pricing model and an effective portfolio strategy. This study provides an empirical analysis of option-implied volatility after correcting for possible estimation error using wavelet transform. So far, little attention has been paid in utilising wavelet transform in denoising the option-implied moments, especially within the model-guided nonparametric framework. Thus, this study primarily seeks to examine the effect of a continuous wavelet transform on option-implied information retrieved from Dow Jones Industrial Average (DJIA) index options throughout 2009 until the end of 2015. This study then extends the existing option pricing models by developing Extended Generalised Leland models based on the implied adjusted volatility introduced in Leland models. The proposed semiparametric models are developed to incorporate the transaction costs rate factor in the intermediated model-free framework to assure realistic pricing of options. We employ a nonparametric mechanism within the conventional option-pricing framework based on the Leland models in order to tackle both model misspecification problem introduced in most parametric models and the infeasible pricing problem in nonparametric models. Given the fact that selecting a portfolio with optimal asset allocation is a typical issue faced by many investors, this study extends the improved option-implied information in answering the asset allocation problems. This study finds that wavelet improves the error approximation of the signal. On top, this study reveals that the option-implied adjusted volatility, which is priced using the Extended Generalised Leland models, delivers a significant improvement to the option valuation accuracy. Superior option pricing accuracy was observed in the Extended Generalised Leland models. Results indicate that the proposed model has shown to improve asset allocation strategy significantly.