The New Risk Management Framework after the 2008 Financial Crisis


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  • Choquet Expected Utility and Prospect Theory models also give higher weight to small probabilities. Decisions from experience – underweight rare events; from description (Kahnemann & Tversky) overweight rare events.
  • The New Risk Management Framework after the 2008 Financial Crisis

    1. 1. Prepared for Presentation 13 July 2010<br />GARP, London Chapter<br />by Barry Schachter<br />The New Risk Management Framework After the 2008 Financial Crisis<br />
    2. 2. “Those who cannot remember the past are condemned to repeat it.” Santayana, The Life of Reason (vol. 1)<br />“What experience and history teaches us is that nations and governments have never learned anything from history, or acted on principles deduced from it.” Hegel, Lectures on the Philosophy of History<br />Learning Lessons<br />
    3. 3. Received Wisdom on What Went Wrong<br />
    4. 4. These aren’t bugs, they’re features<br />e.g., mis-measurement of known risks – correlation<br />failure to take risks into account – liquidity<br />A case of 20-20 hindsight bias<br />e.g., over-reliance on rating agencies<br />failure to analyze crisis scenario risks<br />The questions asked are too superficial – failure to question the appropriateness of the paradigm<br />What’s Wrong with Received Wisdom<br />
    5. 5. I agree with Hoyle, “…human behavior is controlled by an interlocking system of nonlinear feedback loops... It is far beyond present-day resources to take full and accurate account of all the feedback effects that occur in human society. (Man and Materialism (1956))<br />Rational economic behavior is not what is assumed in neo-classical economics.<br />Stable and static equilibria do not describe the world in which we live.<br />Risk Management can move forward only by adopting a network view of markets with fallible, but strategically behaving risk takers.<br />A Personal View of Lessons Learned<br />
    6. 6. Attribution of effects to causes is intrinsic to us; it is how we build our model of the environment, and how we devise rules-of-thumb behavioral responses<br />Assigning causes is also how we maintain a sense of control of what is really a very complex and confusing world<br />There is a dark side of this in which we grasp at causes, even if they are fictions, and proceed to “address” them<br />We have to guard against simple responses to complex problems, because they may be ineffective and have unintended negative consequences<br />This applies to how we reinvent risk management in light of the crisis<br />A Caution on Causation<br />
    7. 7. Weare (daily) data junkies – Can’t get enough <br />Financial data isn’t IID, requires transformation or estimation of conditional moments of the generating process<br />We make up data for regulatory historical stress scenarios – we construct shocks for instruments that did not exist (ERM, Oct 1987)<br />We extrapolate even though we know extrapolation is Evil<br />The Data “Problem” Has No Fix<br />
    8. 8. “scenarios most likely to cause their current business model to become unviable” <br />define all “ruin scenarios”, pick plausible ones; or define plausible scenarios, find the “ruin scenarios” as a subset<br />Maximum Loss first developed by Studer (1997)<br />Problem may be ill-posed and ill-conditioned, depending on the actual distributions of risk factor and portfolio returns<br />Estimation may be computationally daunting<br />Innovation: Reverse Stress Testing<br />
    9. 9. Finding portfolio vulnerabilities through stress testing is an ill-conditioned problem (small changes in the shocks may result in large changes in the stress loss)<br />It is also an ill-posed problem (small changes in the stress loss may be associated with very different shock scenarios)<br />Whereas standard stress tests are arbitrary, in reverse stress tests results dependent on data (period) and fitted distribution. <br />But Reverse Stress Tests No Panacea<br />
    10. 10. Stressed VaR: 10-day, 99th percentile...calibrated to...a period of significant financial stress relevant to the firm’s portfolio. For most portfolios, the [Basel] Committee would consider a 12-month 2007/2008 to be a period of such stress.”<br />Chichilnisky (2010) proposes a subjective probability measure to increase the probability weight of rare events. (Also, Algorithmics approach)<br />What to do When Events are too Rare<br />
    11. 11. George Buffon corresponded (1760) with Daniel Bernoulli on event probabilities small enough that could be disregarded in evaluating decisions. Buffon called events with probability less than 1/10,000 a “moral certainty”. Bernoulli suggested 1/100,000. The History of Statistics in the 17th and 18th Centuries, E. S. Pearson, ed. (New York: MacMillan Publishing Co.), 1978, p. 193. <br />So, 1 time in 27.4 years or perhaps 1 time in 274 years. <br />How to Decide the Smallest Probability Worth Measuring for Risk? <br />
    12. 12. In the US, something called the “Kanjorski Amendment” gives Systemic Risk Council power to break up banks that pose “a grave threat to the financial stability or economy” (<br />Create need to monitor own firm systemic risk contribution (as perceived by the regulator), e.g., using CoVaR model (<br />Possibly alter business decisions which could result in adverse impact on systemic risk contribution. <br />New Need for Systemically Risky Risk Measuranagement<br />
    13. 13. Persaud (2002) and Morris and Shin (2003) argue that market-sensitive risk regulation might reduce stability by reducing diversity. <br />In an evolutionary context, diversity is key to species survival (I’ll return to this)<br />Some new regulatory changes might affect diversity, e.g., the widespread use of Stressed VaR, based on identical 12-month 2007/2008.<br />Aside: Convergence as a Systemic Risk<br />
    14. 14. Financial Risk Management is atomistic<br />Possible Exceptions to this statement<br />Liquidity risk<br />Stress testing counterparty default<br />Positive Feedback and Contagion are aspects of a networked system<br />Positive Feedback is outside the Risk Management Paradigm<br />
    15. 15. In an evolutionary biology view, risk management as an adaptation for enhancing survival of an “agent”. <br />Agents are risk taking entities, competing for success in financial markets by adopting different trading and risk management strategies<br />Market shocks threaten survival of agents and stability of the system (via contagion)<br />Risk Management adaptations that increase survivability are selected and propagate through subsequent “generations”<br />The Networked Economy and Risk Management<br />
    16. 16. Consider N Hedge Funds, with capital allocated in each fund equally between 2 Traders, each taking long or short positions in 2 of 4 possible markets.<br />Traders submit demand schedules, markets must clear<br />Prices are then shocked<br />Traders cheer/cry, then re-compute their wealth<br />Assume that a trader “dies” if wealth falls from peak by more than 20% or if combined Fund resources fall by 15%<br />In each case there is a liquidation of positions<br />A Financial Market Network Model<br />
    17. 17. In liquidation the demand schedule submitted has 0 elasticity. <br />Liquidation affects prices in every market in which a position is held.<br />Depending on the demand schedules and wealth, the liquidation may have spillover effects.<br />Note that the liquidation is driven not by bankruptcy, but by a risk control mechanism - funding. <br />Further connections may arise via other mechanisms – e.g., VaR limits, the key is feedback and contagion.<br />Fragility in the Network<br />
    18. 18. Illiquidity<br />Crowded Trades<br />Hidden Correlations<br />New Approaches to Risk Control<br />New Risk Measurement and Management Directions<br />
    19. 19. Concepts<br />“Small” positions, when traded, induce “large” market impact costs (total market impact affected by aid/ask spread, return volatility, volume) <br />Liquidity black hole – where price movements trigger additional trading in a positive feedback loop resulting in large price changes<br />Liquidity black holes are not specifically about calculating liquidity-adjusted VaR (see L-VaR survey by Ernst, Stange and Kaserer (2009))<br />Illiquidity<br />
    20. 20. Qualitative detection<br />Quantitative detection – Normal illiquidity<br />Exchange-traded<br />Position as % of ADV<br />Total cost to exit (e.g., Almgren and Chriss (2000))<br />OTC<br />Quantitative detection – Black Holes<br />Brunnermeier and Pedersen (2008) generate black holes from the feedback between funding and market liquidity through margin requirements<br />Illiquidity<br />
    21. 21. Definition by CRMPG-II (2005):<br />Multiple parties entering into correlated trading strategies...where the aggregate volume of trades is sufficient to constrain the ability of traders to exit from the position on a simultaneous basis;<br />A crowded trade is characterized by dampening volatilities, increased measured liquidity, and decreased “spreads”. (<br />Also seen in RV type crowded trades (e.g., DAX v. STOXX)<br />higher than normal correlations between instruments/markets<br />Crowded Trades<br />
    22. 22. Qualitative detection<br />Quantitative detection<br />Pojarliev and Levich (2009) use returns-based style analysis for a (22-60) hedge fund sample and 4 currency trading styles (trend, carry, volatility, and value), and compute the difference between the number of funds with positive and negative statistically significant style exposures. <br />Pericoli and Sbracia (2010) examine the pairwise median correlation among hedge fund returns after extracting common macro factors. <br />Crowded Trades<br />
    23. 23. The network model suggests that a “phase transition” reveals hidden correlations<br />Risk management should seek out signs of hidden correlations (e.g., crowded trades, or risk control mechanisms)<br />No current methods for systematically investigating this at the individual firm (risk manager) level<br />Hidden Correlations<br />
    24. 24. It seems likely that the new risk management framework will be defined by the old risk management paradigm<br />Improvements within the old paradigm are possible, but represent a missed opportunity to re-think risk management<br />Within a view of markets as a dynamic network and risk takers as fallible but strategic agents the focus of risk management shifts and the design of risk control mechanisms may take a new direction.<br />Summary<br />
    25. 25. Golub, B. and Crum, C., Risk Management Lessons Worth Remembering from the Credit Crisis of 2007–2009, The Journal of Portfolio Management, Spring 2010, Vol. 36, No. 3: pp. 21-44<br />Stulz, R., Risk Management Failures: What Are They and When Do They Happen? (October 2008)<br />Senior Supervisors Group, Observations on Risk Management Practices during the Recent Market Turbulence (March 6, 2008)<br />Rosengren, E., Risk-Management Lessons from Recent Financial Turmoil (May 14, 2008)<br />References<br />
    26. 26. Jorion, P., Risk Management Lessons from the Crisis (June 2008)<br />President's Working Group on Financial Markets, Policy Statement on Financial Market Developments (March 2008)<br />Alexander, C., The Present and Future of Risk Management (February 2004)<br />McNeil, A. and Smith, A., Multivariate Stress Testing for Solvency II (March 2010)<br />Studer, G. Maximum Loss for Measurement of Market Risk (1997)<br />(More) References<br />
    27. 27. Pojarliev, M. and Levich, R., Detecting Crowded Trades in Currency Funds (December 2009).<br />Pericoli, M. and Sbracia, M., Crowded Trades among Hedge Funds (May 2010).<br />(Still More) References<br />