Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/34622
Appears in Collections:Accounting and Finance eTheses
Title: Essays on Financial Market Volatility
Author(s): Sahiner, Mehmet
Supervisor(s): McMillan, David
Kambouroudis, Dimos
Keywords: Volatility
Forecasting
Machine Learning
Contagion
Issue Date: 14-Apr-2022
Publisher: University of Stirling
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.
Type: Thesis or Dissertation
URI: http://hdl.handle.net/1893/34622

Files in This Item:
File Description SizeFormat 
Mehmet Sahiner Thesis.pdfMehmet Sahiner PhD Thesis7.44 MBAdobe PDFUnder Embargo until 2023-10-31    Request a copy

Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.



This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.