The Golden Period in Financial Markets

To understand the recent failures in risk management, some history is instructive.  Prior to the current financial crisis, the previous two low tide marks for the financial system and risk management were the stock market crash of October 1987 and the failure of the hedge fund LTCM in September 1998.  Both prompted a sea-change in risk management practices and technologies.

The October 1987 crash in many respects marked the birth of Value at Risk (VaR) as a key risk management tool in financial firms. JP Morgan were an early-developer and early-adopter of VaR.  By 1996, they had published their methodology and the detail of the parameterisation of their risk models.

From 1997 onwards, VaR came to take a degree of prominence.

Stress-testing was given considerable impetus by the failure of LTCM more than a decade after the October 1987 crash.

This technological transformation contributed to what was, with hindsight, an extraordinary period of growth and success for the financial system and financial markets – a Golden Decade.  Between October 1998 and June 2007,  banks’ share prices increased almost 60% and their balance sheets rose more than threefold.  In some markets growth was little short of explosive, with the rise in volumes outstanding in the CDS market making Moore’s Law look positively sluggish.

However, after 2007 things have changed. Asset prices have collapsed – for example, world equity prices have lost more than three-quarters of their gains during the Golden Decade.  Prices of banks’ shares have fared even worse, losing almost 60% of their value and are now lower than at the start of the Golden Decade.  In the face of these falls, risk management systems across virtually all institutions have been found badly wanting.  A survey of 500 risk managers by KPMG in October last year found that 92% intended to review their risk management practices.

Extract from a speech given by Andrew G Haldane - "Why Banks Failed the Stress Test?"

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.