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Robust Forecasting and Backtesting of Value at Risk (VaR) and Expected Shortfall (ES) risk measures

Argyropoulos, Christos (2017) Robust Forecasting and Backtesting of Value at Risk (VaR) and Expected Shortfall (ES) risk measures. Doctor of Philosophy (PhD) thesis, University of Kent,. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:66153)

Language: English

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Recent financial turmoil has set in motion changes that include the switch from the Value at Risk (VaR) risk measure to the Expected Shortfall (ES). Although ES seems superior to VaR, both measures are statistical quantities estimated using historical data. This basis in historical data raises numerous concerns regarding their implementation. This thesis focuses on the estimation, evaluation and applications of the Value at Risk and Expected Shortfall risk measures. It consists of four main chapters corresponding to four research papers.

In the second chapter we evaluate the performance of the backtesting methods and their reliability. Specifically, through a simulation exercise and a financial data application, we evaluate the size and power properties of the major VaR and ES backtesting methods. In addition, we examine the impacts of the regulatory specifications and estimation risk on the tests performance. The simulation results suggest that the size of the tests is distorted and the power of the tests is low, especially for the regulatory specifications. Finally, the empirical application results suggest that misspecified methods are not rejected.

Finally, in the fourth chapter we utilize the VaR and ES risk measures in order to evaluate the risk-return relationship of the hedge funds and the fund of funds. In addition, we propose an optimal portfolio strategy that aims to outperform the hedge funds and their portfolio benchmarks. Our findings suggest that there are small gains from investing in fund of funds when compared to low risk hedge funds. More importantly, the optimal portfolios outperform all the fund of funds benchmarks.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Panopoulou, Ekaterini
Thesis advisor: Tunaru, Radu
Uncontrolled keywords: Forecasting Evaluation Risk-Measures Value-at-Risk Expected-Shortfall
Divisions: Faculties > Social Sciences > Kent Business School
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 27 Feb 2018 14:10 UTC
Last Modified: 01 Aug 2019 10:43 UTC
Resource URI: (The current URI for this page, for reference purposes)
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