Overview of Financial Risks and Regulatory Frameworks › Interactions Among Risk Types

Michael BrenndoerferDecember 19, 20256 min read
Quantitative FinanceAll chapters
Part 10 of 15Risk Management and Financial Risk Controls
Chapter 52Overview of Financial Risks and Regulatory Frameworks › Interactions Among Risk Types
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While we have categorized risks into distinct types, in practice these risks interact and compound each other. Risk managers must account for these interactions because one risk can amplify another.

Risk Cascades

The 2008 financial crisis illustrated how risks can cascade:

  1. Credit losses on subprime mortgages impaired the value of mortgage-backed securities
  2. Market risk materialized as MBS prices fell, causing mark-to-market losses
  3. Liquidity risk emerged as investors fled the MBS market, eliminating secondary market liquidity
  4. Funding liquidity risk intensified as banks holding MBS faced margin calls and deposit flight
  5. Counterparty credit risk spiked as questions emerged about institution solvency
  6. Operational challenges arose from the complexity of structured products whose documentation and servicing hadn't anticipated mass defaults

Each risk type amplified the others, creating a systemic crisis far worse than any single risk in isolation would have caused.

Out[24]:
Visualization
Flow diagram of five boxes connected from left to right: Credit Risk, Market Risk, Market Liquidity, Funding Liquidity, and Counterparty Risk. A curved arrow beneath returns from the final stage to the first, indicating a self-reinforcing feedback mechanism.
Systemic risk propagation during the 2008 financial crisis. Initial credit defaults in subprime mortgages trigger a self-reinforcing sequence of market price declines, liquidity drying up, and counterparty concerns, showing how interconnected risk types amplify shocks.

Correlation Breakdown

A particularly dangerous interaction involves the breakdown of diversification during stress events. Correlations between asset classes, which appear low during normal times, spike during crises:

  • Emerging market equities and developed market equities become highly correlated during global selloffs
  • Credit spreads and equity volatility move together during risk-off episodes
  • Commodity prices, usually driven by specific supply-demand factors, become correlated with financial assets during liquidity crises

This means that diversification, the primary tool for managing market risk, becomes less effective precisely when it is most needed. Risk management must account for this through stress testing that assumes higher correlations.

We can simulate the breakdown of diversification by comparing correlation matrices under normal and crisis conditions. The intuition is straightforward: during normal times, different assets respond to different factors, so their correlations are moderate. During crises, a single dominant factor (often related to liquidity or risk appetite) drives all risky assets simultaneously, causing correlations to converge toward extremes.

In[25]:
Code
# Demonstrate correlation breakdown: normal vs. crisis correlations
# Simulated data showing how correlations change during stress

normal_corr = np.array(
    [
        [1.0, 0.3, 0.2, -0.2],
        [0.3, 1.0, 0.4, -0.1],
        [0.2, 0.4, 1.0, 0.0],
        [-0.2, -0.1, 0.0, 1.0],
    ]
)

crisis_corr = np.array(
    [
        [1.0, 0.8, 0.7, -0.5],
        [0.8, 1.0, 0.85, -0.4],
        [0.7, 0.85, 1.0, -0.3],
        [-0.5, -0.4, -0.3, 1.0],
    ]
)

asset_labels = ["US Equities", "EM Equities", "High Yield", "Treasuries"]
Out[26]:
Visualization
Annotated correlation matrix for US Equities, EM Equities, High Yield, and Treasuries under normal conditions. Most risky-asset off-diagonal values range from 0.2 to 0.4, while Treasury correlations range from minus 0.2 to zero.
Correlation matrix under normal market conditions. Cross-asset correlations are moderate (mostly 0.2 to 0.4), allowing for effective diversification between equities, bonds, and high-yield credit.
Annotated correlation matrix for the same four asset classes under crisis conditions. Risky-asset correlations rise to 0.7 through 0.85, while Treasury correlations fall to minus 0.3 through minus 0.5.
Correlation matrix under crisis conditions. Correlations between risky assets rise toward 1.0, while Treasuries show stronger negative correlation, illustrating how diversification changes during stress events.

The shift from normal to crisis correlations increases portfolio risk. A portfolio that appeared well-diversified under normal correlations may have much higher risk under crisis correlations. Risk management should evaluate the portfolio under both scenarios. Notice in particular how the correlation between US Equities and EM Equities jumps from 0.3 to 0.8, and the correlation between EM Equities and High Yield jumps from 0.4 to 0.85. These asset classes, which provide meaningful diversification during calm periods, move nearly in lockstep during crises.