Forecasting risk and failure in the Hedge Fund industry

Aldhahi, Huda Ibrahim Saad (2023) Forecasting risk and failure in the Hedge Fund industry. Doctoral thesis, University of East Anglia.

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Abstract

This dissertation aims to provide a comprehensive understanding of hedge fund performance, volatility forecasting, and survival analysis based on three extensive studies. It aims to extensively evaluate the performance of various hedge fund indices and examine the factors influencing hedge fund survival using an extensive dataset between 1994-2020. This thesis is constructed in three parts. In the first study, conduct a comprehensive analysis of prominent volatility forecasting models applied to different hedge fund indices and time horizons. The results indicate asymmetric EGARCH and TGARCH models as optimal choices for forecasting daily and weekly hedge fund volatility. Moreover, the study identifies IGARCH and LRE models as inferior alternatives across all indices and horizons examined.

The second study deeply investigates the survival of hedge funds by exploring critical factors behind their failures using survival analysis techniques such as non-parametric survival analysis, Semiparametric Cox proportional hazard, and Weibull AFT methods. This research reveals age, size, and performance as critical determinants for hedge funds' longevity. Conversely, volatility, advanced notice period, and efficiency values negatively affect hedge fund survival. The relationship between management fees, leverage employed, lockup periods, and fund survival rates exhibit mixed results based on measurements, fund styles, and evaluation periods studied.

The third study evaluates hedge fund performance through data envelopment analysis (DEA) to provide an accurate ranking of different performances. The findings offer insights into the instability of various hedge fund strategies in diverse time horizons. Additionally, it examines the impact of major economic crises on the performance of hedge funds. Ultimately, this research contributes significantly to investors' and fund managers' understanding by identifying high-performing funds to optimize portfolio diversification effectively.

The overarching objective of this dissertation is to provide investors and fund managers with a comprehensive and detailed understanding of hedge funds by investigating various aspects such as volatility forecasting techniques, key drivers of longevity, and precise performance measurement using data envelopment analysis. By examining these critical elements with extensive datasets and innovative methodologies, this dissertation aims to contribute significantly to the existing literature in the field while providing valuable guidance for investment decisions and portfolio diversification strategies.

Item Type:	Thesis (Doctoral)
Faculty \ School:	Faculty of Social Sciences > Norwich Business School
Depositing User:	Nicola Veasy
Date Deposited:	11 Jul 2024 11:38
Last Modified:	11 Jul 2024 11:38
URI:	https://ueaeprints.uea.ac.uk/id/eprint/95879
DOI:

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