Forecasting Extreme Risk of Equity Portfolios with Fundamental Factors

Vladislav Dubikovsky, Michael Y Hayes, Lisa R Goldberg and Ming Liu

Contents

Introduction to 'Lessons from the Financial Crisis'

1.

The Credit Crunch of 2007: What Went Wrong? Why? What Lessons Can be Learned?

2.

Underwriting versus Economy: A New Approach to Decomposing Mortgage Losses

3.

The Shadow Banking System and Hyman Minsky’s Economic Journey

4.

The Collapse of the Icelandic Banking System

5.

The Quant Crunch Experience and the Future of Quantitative Investing

6.

No Margin for Error: The Impact of the Credit Crisis on Derivatives Markets

7.

The Re-Emergence of Distressed Exchanges in Corporate Restructurings

8.

Modelling Systemic and Sovereign Risks

9.

Measuring and Managing Risk in Innovative Financial Instruments

10.

Forecasting Extreme Risk of Equity Portfolios with Fundamental Factors

11.

Limits of Implied Credit Correlation Metrics Before and During the Crisis

12.

Another view on the pricing of MBSs, CMOs and CDOs of ABS

13.

Pricing of Credit Derivatives with and without Counterparty and Collateral Adjustments

14.

A Practical Guide to Monte Carlo CVA

15.

The Endogenous Dynamics of Markets: Price Impact, Feedback Loops and Instabilities

16.

Market Panics: Correlation Dynamics, Dispersion and Tails

17.

Financial Complexity and Systemic Stability in Trading Markets

18.

The Martingale Theory of Bubbles: Implications for the Valuation of Derivatives and Detecting Bubbles

19.

Managing through a Crisis: Practical Insights and Lessons Learned for Quantitatively Managed Equity Portfolios

20.

Active Risk Management: A Credit Investor’s Perspective

21.

Investment Strategy Returns: Volatility, Asymmetry, Fat Tails and the Nature of Alpha

Extreme events are an important source of financial risk, but they present special challenges in quantitative forecasting. This chapter describes an empirical approach to forecasting extreme risk and evaluates its accuracy out-of-sample on a range of factor-based strategies and pair trades. The results show that for a large majority of strategies the empirical model is more consistent with market behaviour than a conditional normal model.

Like volatility, shortfall comes equipped with a standard portfolio management toolkit that includes risk budgets, betas and correlations (Goldberg et al 2010). Shortfall optimisation can be formulated as a linear programming problem, and it is therefore a suitable risk measure for portfolio construction, as described in Rockafellar and Uryasev (2000, 2002) and Bertsimas et al (2004). This optimisation framework has been applied in Bender et al (2010) as a constraint in the active management context, and for asset allocation in Sheikh and Qiao (2009). On the other hand, important questions remain about the practical application of shortfall in the investment process. One question is the role of estimation error, which has been studied

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