Risk factors and the financial crisis

Quantitative Risk Management in Python

Jamsheed Shorish

Computational Economist

Risk factors

  • Volatility: measure of dispersion of returns around expected value
  • Time series: expected value = sample average
  • What drives expectation and dispersion?
  • Risk factors: variables or events driving portfolio return and volatility

Dispersion plot, deviation of returns from time series mean

Quantitative Risk Management in Python

Risk exposure

  • Risk exposure: measure of possible portfolio loss

    • Risk factors determine risk exposure
  • Example: Flood Insurance

    • Deductible: out-of-pocket payment regardless of loss
    • 100% coverage still leaves deductible to be paid
    • So deductible is risk exposure
    • Frequent flooding => more volatile flood outcome
    • Frequent flooding => higher risk exposure
Quantitative Risk Management in Python

Systematic risk

  • Systematic risk: risk factor(s) affecting volatility of all portfolio assets

    • Market risk: systematic risk from general financial market movements
  • Airplane engine failure: systematic risk!

  • Examples of financial systematic risk factors:

    • Price level changes, i.e. inflation
    • Interest rate changes
    • Economic climate changes

Propeller plane engine

Quantitative Risk Management in Python

Idiosyncratic risk

  • Idiosyncratic risk: risk specific to a particular asset/asset class.

  • Turbulence and the unfastened seatbelt: idiosyncratic risk!

  • Examples of idiosyncratic risk:

    • Bond portfolio: issuer risk of default
    • Firm/sector characteristics
      • Firm size (market capitalization)
      • Book-to-market ratio
      • Sector shocks

Airline seat belt

Quantitative Risk Management in Python

Factor models

  • Factor model: assessment of risk factors affecting portfolio return
  • Statistical regression, e.g. Ordinary Least Squares (OLS):
    • dependent variable: returns (or volatility)
    • independent variable(s): systemic and/or idiosyncratic risk factors
  • Fama-French factor model: combination of
    • market risk and
    • idiosyncratic risk (firm size, firm value)
Quantitative Risk Management in Python

Crisis risk factor: mortgage-backed securities

  • Investment banks: borrowed heavily just before the crisis
  • Collateral: mortgage-backed securities (MBS)
  • MBS: supposed to diversify risk by holding many mortgages of different characteristics
    • Flaw: mortgage default risk in fact was highly correlated
    • Avalanche of delinquencies/default destroyed collateral value
  • 90-day mortgage delinquency: risk factor for investment bank portfolio during the crisis

90-day mortgage delinquencies 2005-2010

Quantitative Risk Management in Python

Crisis factor model

  • Factor model regression: portfolio returns vs. mortgage delinquency
  • Import statsmodels.api library for regression tools
  • Fit regression using .OLS() object and its .fit() method
  • Display results using regression's .summary() method

 

import statsmodels.api as sm

regression = sm.OLS(returns, delinquencies).fit()
print(regression.summary())
Quantitative Risk Management in Python

Regression .summary() results

regression summary example

Quantitative Risk Management in Python

Let's practice!

Quantitative Risk Management in Python

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