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Evaluating Market Risk Factors in Positive and Negative World Markets

This study delves into the impact of market conditions on country market risks, highlighting the significance of skewness and beta variations in different market scenarios. By deviating from the standard CAPM and splitting betas into positive and negative components, the model offers a more nuanced approach to risk evaluation. Results underscore the need for separate betas for up and down markets, showcasing the model's potential for generating excess returns in tactical asset allocation decisions. The next steps involve testing the model's predictive capabilities, analyzing market risks across different economic phases, and refining the pricing model for enhanced accuracy.

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Evaluating Market Risk Factors in Positive and Negative World Markets

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  1. Evaluating Market Risk Factors in Positive and Negative World Markets Buhdy Bok Frank Liu Jeff Lu Brad Newcomer Ron Yee

  2. Agenda • Hypothesis • Overview • Analysis • Applications • Next Steps

  3. Hypothesis • Country market risk differ depending upon market conditions • Skewness is an important factor in evaluating country market risk

  4. Overview • CAPM assumes an average beta • Volatility varies in different market conditions • Betas vary depending upon market conditions • CAPM assumes returns are normally distributed • Returns are not generally symmetrical • Returns typically exhibit positive or negative skewness

  5. Data Source • Compared Monthly Returns - Equity Markets from 37 Countries vs. World Market (MSCI Indices) • 16 Developed Nations • 21 Emerging Markets

  6. Diversion from Standard CAPM • We want to split the CAPM Beta into 2 Betas • Beta+ when world market return is positive • Beta- when world market return is negative r   =   α   +   β( Rm - Rf ) + error to r   =   α   +   β+( Rm+ - Rf ) +   β-( Rm- - Rf ) + error

  7. Coskewness Regression • Coskewness: The amount of skewness that an asset adds to the diversified portfolio (systematic skewness) r   =   α   +   β1( RM ) + β2( RM )2 + error

  8. Application of the Model • Results demonstrate the significance of separate betas for up/down markets • A simple, intuitive refinement of the CAPM • Incorporating this concept into tactical allocation decisions will generate excess returns

  9. Application of the Model • Requires a predictive model to forecast up/down markets • New procedure: • Create a predictive model to forecast +/- market signals • Calculate the appropriate correlation matrix • Run optimization model (either up/down) • Use output to determine asset allocations

  10. Application of the Model

  11. Application of the Model

  12. Next Steps • Run an out-of-sample test of the model • Parse market risk over more buckets • Examine performance of market risk factor using different parsing criteria • e.g., recession vs. expansion • Goal: create a more accurate pricing model that allows the market risk factor to be more dynamic over a range of market conditions

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