Investigation on Implied Volatilities with Time Series and Outlier Detection: Evidence from Post-Financial-Tsunami Taiwan
Date Issued
2015
Date
2015
Author(s)
Kuo, Tzu-Ming
Abstract
We use data of Taiwan securities markets to work out a panel data (volatility surface) of implied volatilities of near-month Taiwan Stock Exchange Capitalisation Weighted Stock Index Options from the year of 2008 to the year of 2013, and discover that there have been a structural difference in both the time series and the volatility smile of implied volatilities between pre-financial-tsunami Taiwan and post-financial-tsunami Taiwan. To begin with, we have considered utilising a two-step dynamic model by Kuo et al. (2009) to investigate the panel data, but we advert that there are in fact theoretical blemishes rooted in the longitudinal section of the two-step dynamic model. As a result, we adopt the Box-Jenkins autoregressive integrated moving average model in this work to eliminate the theoretical blemishes and to improve the capability of in-sample fitting and out-of-sample forecasting. Moreover, for the purpose of better capturing the effects of extreme events on the longitudinal data, the tools of outlier detection and adjustment are also been introduced into this work. Our result shows that (1) the two-step dynamic model is really improved so that we can merely rely on implied volatilities themselves to describe the time series in sample without contradict any fundamental assumption of models and to provide much more precise forecast when we want to do price discovery, and that (2) we can quantitatively dig out from transaction records the traces left by and the degree of impact resulted from extreme events.
Subjects
implied volatility
volatility smile
time series
outlier detection
financial tsunami
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-104-R02723029-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):ccf0c74adf74b66c4ccc90e498115a03