TY - BOOK

T1 - Modelling financial volatility using Bayesian and conventional methods

AU - Li, Xuguang

PY - 2016

Y1 - 2016

N2 - This thesis investigates different volatility measures and models, including parametric and non-parametric volatility measurement. Both conventional and Bayesian methods are used to estimate volatility models. Chapter 1: We model and forecast intraday return volatility based on an extended stochastic volatility (SV) specification. Compared with the standard SV, we incorporate the trading duration information which includes both actual and expected durations. We use the Autoregressive Conditional Duration (ACD) model to calculate the expected duration that can be used to measure the surprise in durations. We find that the effect of surprise in durations on intraday volatility is highly significant. If there is an unexpected increase for the lag actual duration, the current volatility tends to decrease, and vice versa. We also take into account the duration and volatility intraday patterns. Our empirical results is based on the SPDR S&P 500 (SPY) and Microsoft Corporation (MSFT) data. According to the in-sample and out-of-sample empirical results, the extend SV model outperforms the GARCH and GARCH augmented with duration information. Chapter 2: We examine contagion effects resulting from the US subprime crisis on a sample of EU countries (UK, Switzerland, Netherlands, Germany and France) using a Multivariate Stochastic Volatility (MSV) framework augmented with implied volatilities. The MSV framework is estimated using Bayesian techniques. We compare the the MSV framework with the Multivariate GARCH (M-GARCH) framework and find the contagion effect is more significant under MSV framework. Moreover, augmenting the MSV framework with implied volatilities further increases model fit. Compared with the original MSV framework, we find that the contagion effect becomes more significant when we incorporate implied volatilities. Therefore, implied volatility information is useful for detecting financial contagion, or double checking some cases of market interdependence (strong linkages but insignificant increase in correlations). Chapter 3: We extend the Heterogeneous AR (HAR) model to allow the autoregressive parameter of daily realized volatility (RV) to be time varying (TV-HAR). The daily lag weights are adjusted according to the fluctuations of RV around its longer time average level (monthly RV). We compare the TV-HAR model with the HAR model and the recently introduced HARQ model. We observe a regular pattern of RV which the HAR and HARQ models do not fully capture: if there is an increase in the lag daily RV compared with its longer-term average level (monthly RV), the current RV tends to decrease rapidly to its long term level; conversely, if there is a decrease in the lag daily RV compared with its longer-term average level (monthly RV), that reversion takes longer. The TV-HAR model can capture this RV pattern. We find that the TVHAR model performs better than the benchmark HAR model and the HARQ model for both simulated and empirical data. Our empirical analysis is based on the S&P 500 equity index, SPY index and ten series of stocks data from 2000 to 2010.

AB - This thesis investigates different volatility measures and models, including parametric and non-parametric volatility measurement. Both conventional and Bayesian methods are used to estimate volatility models. Chapter 1: We model and forecast intraday return volatility based on an extended stochastic volatility (SV) specification. Compared with the standard SV, we incorporate the trading duration information which includes both actual and expected durations. We use the Autoregressive Conditional Duration (ACD) model to calculate the expected duration that can be used to measure the surprise in durations. We find that the effect of surprise in durations on intraday volatility is highly significant. If there is an unexpected increase for the lag actual duration, the current volatility tends to decrease, and vice versa. We also take into account the duration and volatility intraday patterns. Our empirical results is based on the SPDR S&P 500 (SPY) and Microsoft Corporation (MSFT) data. According to the in-sample and out-of-sample empirical results, the extend SV model outperforms the GARCH and GARCH augmented with duration information. Chapter 2: We examine contagion effects resulting from the US subprime crisis on a sample of EU countries (UK, Switzerland, Netherlands, Germany and France) using a Multivariate Stochastic Volatility (MSV) framework augmented with implied volatilities. The MSV framework is estimated using Bayesian techniques. We compare the the MSV framework with the Multivariate GARCH (M-GARCH) framework and find the contagion effect is more significant under MSV framework. Moreover, augmenting the MSV framework with implied volatilities further increases model fit. Compared with the original MSV framework, we find that the contagion effect becomes more significant when we incorporate implied volatilities. Therefore, implied volatility information is useful for detecting financial contagion, or double checking some cases of market interdependence (strong linkages but insignificant increase in correlations). Chapter 3: We extend the Heterogeneous AR (HAR) model to allow the autoregressive parameter of daily realized volatility (RV) to be time varying (TV-HAR). The daily lag weights are adjusted according to the fluctuations of RV around its longer time average level (monthly RV). We compare the TV-HAR model with the HAR model and the recently introduced HARQ model. We observe a regular pattern of RV which the HAR and HARQ models do not fully capture: if there is an increase in the lag daily RV compared with its longer-term average level (monthly RV), the current RV tends to decrease rapidly to its long term level; conversely, if there is a decrease in the lag daily RV compared with its longer-term average level (monthly RV), that reversion takes longer. The TV-HAR model can capture this RV pattern. We find that the TVHAR model performs better than the benchmark HAR model and the HARQ model for both simulated and empirical data. Our empirical analysis is based on the S&P 500 equity index, SPY index and ten series of stocks data from 2000 to 2010.

KW - volatility

KW - financial markets

M3 - Doctoral Thesis

PB - Lancaster University

ER -