Abstract

Following the recent financial crises, there has been a proliferation of new risk management and portfolio construction approaches. These approaches all endeavour to better quantify and manage risk by accounting for the stylised facts of financial time series mainly heavy and skewed tails, volatility clustering and converging correlations. Capturing all these stylised facts in a coherent framework has proved to be an elusive and knotty task. We here propose a pure econometric framework that captures all the stylised facts satisfactorily. We use three data sets to show how the approach is implemented in VaR forecasting and correlation analysis. We show how an investment portfolio can be constructed in order to optimise reserve capital holding. The approach employed is linear programming (LP) computable, satisfies second degree stochastic dominance and outperforms the general mean/VaR quadratic optimisation to arrive at efficient asset allocation.

Highlights

  • The past decade has seen a number of financial institutions fail worldwide

  • We extend the univariate Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models to incorporate the assymetric response of returns to market shocks

  • Contrary to what literature suggests, VaR is a function of the returns distribution for a given asset and not of time

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Summary

Introduction

The past decade has seen a number of financial institutions fail worldwide. In Zimbabwe alone, from a peak of 40 banks in 2002, 20 banks remain operational as of January 2014 [1]. In the USA, since 2008, 465 banks have failed, amounting to USD 687 bn in total assets [2] Failures at these institutions are more often than not caused by deep rooted risk management deficiencies, excessive risk appetite resulting in over-trading and poor corporate governance practices. In developing countries, these institutions are key in the economy as they provide basic financial services to the public, financing to commercial enterprises, and access to the payment systems; there is a need to safeguard their continued existence and ensure sustainable economic growth. These measures all have the effect of greatly increasing the market risk capital that large banks are required to hold.

Problem Statement and Objectives
Methodology
Value at Risk for the Gaussian Distribution
Value at Risk for the Skewed t Distribution
Data Analysis and Presentation of Findings
Evidence of Non-Normality in Returns
Incorporating Non-Normality into Asset Allocation Framework
Incorporating Heteroscedasticity
Conclusions
Full Text
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