Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443133
Title: Empirical risk management in emerging markets
Author: Maulana, Tubagus Nur Ahmad
ISNI:       0000 0001 3621 6573
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2007
Availability of Full Text:
Access through EThOS:
Access through Institution:
Abstract:
This research considers different aspect of modelling risk in the emerging markets. It places particular emphasis on modelling default probability in emerging bond markets, modelling country risk in emerging stock markets, modelling market risk in emerging stock markets and examining the appropriate asymmetric volatility model in emerging stock market as well as examining whether the long term memory in volatility exists in emerging stock market. More specifically, the aims of this study is to answer the following research questions: (1) what are the main factors determine and what is the best model to explain default probability in emerging bond market; (2) which model is the best to use to modelling country risk in emerging markets; (3) what is the best model to be used for explaining market risk in emerging stock markets; (4) what is the best asymmetric model to be used in emerging stock markets and is the SEMIFAR model successful at modelling long memory in the volatility of emerging stock markets. The study shows that the fluctuation in default probability in emerging bond markets can be explained by macroeconomic variable and financial variable. With regard to the second research question, it was found that the Kalman Filter model (in particular the Random Walk technique) was the best model to predict country risk in emerging stock markets. The result of the study shows that the most successful model to capture market risk (or extreme risk) in emerging stock market is the conditional t. The study also shows that emerging stock markets are more sensitive to bad news than to good news as indicated by their higher volatility during down-market as compared to up-market. It was found that the TGARCH model is the most appropriate model to be used for explaining asymmetry volatility in emerging stock markets. Finally, the result of this study reveals the existence of long term memory in emerging stock markets and the successfulness of SEMIFAR model to capture the phenomenon.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.443133  DOI: Not available
Share: