Systemic Risk

Systemic risks, as opposed to unique or firm-specific risks, are the most troublesome because they have the potential to affect anyone. Systemic risks may arise from common factors (for example, market and economic factors, weather, natural disasters, computer viruses, war) and can influence the whole market's well-being.

Unlike firm-specific risk, systemic risk cannot be diversified away--in fact, systemic risk is the residual risk when we have diversified away all specific risk.

Common factors

There are common factors in systemic risks.

  • Virtually unrestrained availability of credit

  • Sizable concentrations of risk that were not perceived as such

  • A relaxed acceptance of financial asset inflation--often fueled by cumulative public policy excesses

Examples of Systemic Risk

Examples of systemic risk include:

1. The fall of pegged Southeast Asian currencies, resulting in drastic economic slowdown throughout Asia

2. Japan's 80's lending excesses, resulting in mountains of bad debt still crippling its financial system

3. U.S. stock market crashes (for example, '04, '29, '87)

4. 80's S&L crisis

5. What is the next one?

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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.