STORRE Collection: Electronic theses of Accounting and Finance Students.Electronic theses of Accounting and Finance Students.http://hdl.handle.net/1893/2302024-03-18T08:03:48Z2024-03-18T08:03:48ZEssays in Financial Econometrics: Conditional Volatility, Realized Volatility and Volatility SpilloversKorkusuz, Burakhttp://hdl.handle.net/1893/353952023-09-21T11:37:16Z2023-01-01T00:00:00ZTitle: Essays in Financial Econometrics: Conditional Volatility, Realized Volatility and Volatility Spillovers
Author(s): Korkusuz, Burak
Abstract: 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.2023-01-01T00:00:00ZEssays on Financial Market VolatilitySahiner, Mehmethttp://hdl.handle.net/1893/346222022-10-26T14:49:03Z2022-04-14T00:00:00ZTitle: Essays on Financial Market Volatility
Author(s): Sahiner, Mehmet
Abstract: Volatility is an important component of market risk analysis and it plays a key role in many financial activities, such as risk management, asset pricing, hedging, and diversification strategies. This thesis consists of four empirical essays that evaluate the utility of a wide range of econometric models as well as explore and propose the use of further novel methods to enhance the understanding of volatility mechanisms across emerging and developed financial markets of Asia. Specifically, the first empirical essay provides an in-depth analysis on the characteristics of volatility phenomenon by comparing various GARCH models using three different frequencies with 24 years of data. The findings reveal robust empirical evidence that asymmetric GARCH models outperform in daily and weekly return series, while symmetric GARCH models outperform in monthly return series, indicating that different frequencies have their own structure and characteristics. The second empirical chapter investigates the forecast ability of a number of representative econometric models belonging to two main model groups based on recursive and rolling window methods. The obtained results report that frequency of the data and choice of forecast method have strong effects on performance of the models. Furthermore, existence of strong volatility asymmetry has been found in the higher frequencies of data which is also systematically confirmed by the superiority of the asymmetric models in daily and weekly series. On the other hand, it is found that the monthly series of Asian stock markets are less sensitive to the leverage effects, thus the predictive capability of symmetric GARCH genre of models are more superior in lower frequencies. The third empirical chapter extended the volatility forecasting exercise by evaluating the utility of advanced Machine Learning models in comparison to traditional forecasting models. The findings indicate that the neural network prediction models exhibit improved forecasting accuracy across both statistical and economic based metrics, offering new insights for market participants, academics, and policymakers. The obtained results are further evaluated by the risk management settings of Value at Risk (VaR) and Expected Shortfall (ES). The final empirical essay introduced an Early Warning System (EWS) by integrating DCC correlations with state-of-the-art Deep Learning (DL) model. The novel results demonstrate that the bursts in volatility spillovers are successfully verified by the proposed model and EWS signals are generated with high accuracy before the 12-month period of crises, providing supplementary information that contributes to the decision-making process of practitioners, as well as offering indicative evidence that facilitate the assessment of market vulnerability to policymakers.2022-04-14T00:00:00ZThree Essays in Corporate Finance: The Case of Chinese Firms and Foreign ListingLi, Lianglianghttp://hdl.handle.net/1893/343902022-06-02T07:05:39Z2021-07-01T00:00:00ZTitle: Three Essays in Corporate Finance: The Case of Chinese Firms and Foreign Listing
Author(s): Li, Liangliang
Abstract: The thesis contains three empirical studies that investigate the effect of a Hong Kong listing on Chinese firms. The first study (Chapter 3) investigates whether a Hong Kong listing improves Chinese firms’ investment efficiency. Using a large sample of Chinese listed firms from 2001 to 2015, the study finds that investments by Hong Kong-listed Chinese firms have a higher sensitivity to investment opportunities (Tobin’s Q) compared with their domestically listed peers. Also, Hong Kong-listed Chinese firms are not associated with underinvestment. Overall, the findings suggest that a Hong Kong listing improves Chinese firms’ investment efficiency. The second study (Chapter 4) uses a sample of attempted and completed acquisitions to examine whether a Hong Kong listing affects Chinese firms’ acquisition behaviour. Using the propensity score matching method, Hong Kong-listed Chinese firms are matched with domestically listed Chinese firms over the period 2001 to 2015. The study finds that Hong Kong-listed Chinese firms are less likely to be bidders compared with their domestically listed peers. Furthermore, the study finds that Hong Kong-listed Chinese firms are more likely to make completed acquisitions compared to their domestically listed peers. The third study (Chapter 5) investigates the payment method used in over 2,000 completed acquisitions over the period 2001 to 2015 by Hong Kong-listed Chinese firms compared to their domestically listed peers. The study finds that Hong Kong-listed Chinese firms are more likely to use all-cash payments in acquisitions. However, in cross-border deals, all-cash payments are less used by Hong Kong-listed Chinese firms. Also, the study finds that Hong Kong-listed Chinese firms using all-cash payments have a high level of excess cash. Furthermore, these Hong Kong-listed Chinese firms are more likely to experience positive abnormal returns when using all-cash payments.2021-07-01T00:00:00ZEssays on Realised Volatility Forecasting for International Stock MarketsDing, Yihttp://hdl.handle.net/1893/339802022-02-28T14:11:20Z2021-09-01T00:00:00ZTitle: Essays on Realised Volatility Forecasting for International Stock Markets
Author(s): Ding, Yi
Abstract: Modelling and forecasting market volatility is an important topic within finance research, with the aim of producing accurate forecasts, as confirmed by the plethora of academic papers written over the past few decades. Understanding volatility is crucial for market participants such as investors, policymakers, and academics. The linear Heterogeneous Autoregressive (HAR) model currently dominates the volatility models for forecasting Realised Volatility (RV). This thesis enters the ongoing volatility forecasting debate by developing further the HAR model. First, within the HAR setting volatility jumps, realised semi-variance and the leverage effect are added. With the use of a selection of loss functions and forecasting comparisons it is found that adding the leverage effect into the HAR model can produce the most accurate forecasts over daily, weekly, and monthly horizons. Second, this thesis compares the foresting ability of the Autoregressive (AR) model with flexible lags, generated by the Least Absolute Shrinkage & Selection Operator (Lasso) approach (es), to the HAR model with a fixed lag structure. In-sample results show the Lasso approach to improve the model fitness, and the out-of-sample results indicate a more flexible lag structure is preferred, especially the ordered Lasso performs the best. Third, this thesis incorporates the Smooth Transition and Markov-switching approaches with the linear HAR model in a further forecasting exercise. In-sample results show that the regime-switching models provide better estimation accuracy than the linear HAR model. For the out-of-sample results, although the regime-switching models have limited forecasting ability over the daily horizon, these do outperform the linear HAR model over weekly and monthly horizons. The Markov-switching model is found to be the best, by consistently exhibiting the most accurate forecasts over time. All the above findings have been evaluated within a risk management setting (Value at Risk & Expected Shortfall).2021-09-01T00:00:00Z