Financial Risk Modelling and Portfolio Optimization with R

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Introduction

The period since the late 1990s has been marked by financial crises – the Asian crisis of 1997, the Russian debt crisis of 1998, the bursting of the dot-com bubble in 2000, the crises following the attack on the World Trade Center in 2001 and the invasion of Iraq in 2003, the sub-prime mortgage crisis of 2007 and European sovereign debt crisis since 2009 being the most prominent. All of these crises had a tremendous impact on the financial markets, in particular an upsurge in observed volatility and a massive destruction of financial wealth. During most of these episodes the stability of the financial system was in jeopardy and the major central banks were more or less obliged to take counter-measures, as were the governments of the relevant countries. Of course, this is not to say that the time prior to the late 1990s was tranquil – in this context we may mention the European Currency Unit crisis in 1992–1993 and the crash on Wall Street in 1987, known as Black Monday. However, it is fair to say that the frequency of occurrence of crises has increased during the last 15 years.

Given this rise in the frequency of crises, the modelling and measurement of financial market risk have gained tremendously in importance and the focus of portfolio allocation has shifted from the μ side of the (μ, σ) medal to its σ side. Hence, it has become necessary to devise and employ methods and techniques that are better able to cope with the empirically observed extreme fluctuations ...