|Appears in Collections:
|Accounting and Finance eTheses
|Essays in Financial Econometrics: Conditional Volatility, Realized Volatility and Volatility Spillovers
|University of Stirling
|Korkusuz, B., Kambouroudis, D., & McMillan, D.G. (2023). “Do extreme range estimators improve realized volatility forecasts? Evidence from G7 Stock Markets”. Finance Research Letters, 1544-6123
Korkusuz, B., McMillan, D.G. & Kambouroudis, D., (2022). “Complex network analysis of volatility spillovers between global financial indicators and G20 stock markets”. Empirical Economics, pp. 1-21.
|The accurate forecast of stock market volatility is of particular importance for policy makers, investors, and market participants who have certain levels of risk which they can bear. This thesis centres around the conditional volatility, realized volatility, and volatility spillovers in the context of their model extensions. In particular, we examine the behaviour of stock market volatility in a selection of international markets, the ability of different models to provide accurate volatility forecasts, and the nature of the interrelations between markets from the perspective of complex network theory. Focussing on the modelling and forecasting of volatility we compare some well-established conditional volatility models with realized volatility models and further investigate the use of a number of additional parameters in improving the forecast accuracy of the future realized volatility. In this regard, a wide range of additional parameters, from assets to commodities, extreme range estimators to overnight volatility, oil price to gold price, VIX to EPU, bond price to interest rate, are included. Moreover, those are classified as different information channels, namely local, regional, and global. In terms of volatility spillovers, a volatility spillover model is combined with complex network theory in order to construct a volatility network of international financial markets, consisting of nodes and edges. The main contributions of this thesis are four. First, using the thirty different stock market indices and more up-to-date data the realized volatility (HAR-RV) models outperform the conditional volatility (GARCHs) models and, moreover, decomposition of realized volatility into positive and negative realized semi-variances (HAR-PS) improve the forecast accuracy of HAR-RV model. Second, extreme range estimators such as Parkinson and Garman-Klass could contain additional information for forecasting the future realized volatility. Third, the role of global information at improving the forecasts of future realized volatility is more important than that of local and regional information. Lastly, the spillover networks of international financial markets are much denser in crisis periods compared to non- crisis periods and volatility spillovers in COVID-19 Crisis (2020) period are more transitive and intense than Global Financial Crisis (2008) period.
|Thesis or Dissertation
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