Liquidity Forecasting is the Next Challenge

From the Financial Times

By Christopher Finger

Published: August 3 2008 10:51 Last updated: August 3 2008 10:51

As chief executive of JPMorgan in the late 1980s and early 1990s, Dennis Weatherstone, who died recently, insisted on a daily report of the entire bank’s risk-taking activities.

In the light of recent events, it is tempting to cast Sir Dennis and his report as creatures of the past century, no longer appropriate for today’s complex markets.

Statistical risk models are designed to forecast how much a portfolio’s value may fluctuate over some trading horizon. To be useful, the forecast should apply to the time over which trading decisions are made; it should be the forecasts on today’s positions that are relevant, not those on hypothetical future trades. For actively managed portfolios, the risk horizon is typically one day or one week.

Importantly, financial crises do not transpire over these horizons. The models do not pretend to predict crises, then, but rather to indicate the potential for large short-term market moves. And forecasts over these short horizons can be validated: we can objectively differentiate a good from a bad model.

So do the models succeed at their goal? Recent anecdotes suggest not.

Large banks disclose both their risk forecasts and their real profit and loss. The risk forecasts typically take the form of value-at-risk, or VaR – that is, the worst-case loss that is expected, for example, with 99 per cent confidence. About one day in 100, subject to statistical fluctuations, the bank should experience a VaR excession – a day where the loss is greater than the VaR forecast.

The third quarter of 2007 (62 trading days) was ripe with disclosures of embarrassingly many excessions: as many as 16 for some banks. For good reason, there has been plenty of criticism of Sir Dennis’s reports.

But a keen observer of VaR disclosures would have questioned the models well before the current crisis. It was not unusual for banks to disclose years with zero excessions (on average we would expect two or three) and one bank went nine years without one. While the flurry of excessions in 2007 raised plenty of doubts, the lack of excessions in previous years did not, even though under a good model, nine years without an excession is 100 times less likely than a fiscal quarter with nine or more. Statistical evidence to challenge the forecasts existed but, in rising markets, it was easy to mumble about conservatism and accept demonstrably bad forecasts as good risk disclosure.

So the supposed beneficiaries of risk disclosure have been guilty of apathy, but how could the banks themselves produce such poor forecasts? One explanation is that some favoured simplicity and stability over performance. Twenty-five years of financial research have established two facts: first, risk changes, with market volatility greater in some periods than others; and second, these changes can be forecast. Methods that ignore these facts produce inferior forecasts.

Models that recognise that risk changes – even those applied generically to all asset classes – continue to produce useful forecasts. For the ABX contracts (the actively traded indices based on subprime mortgage-backed securities), forecast volatility picked up in the early spring of 2007, dropped as the crisis appeared to have slowed and then rose in the late summer. VaR forecasts showed an appropriate level of excessions over 2007 and 2008.

It is, however, simplistic to say some banks ignored better modelling choices. The real trouble was not how they modelled based on historical data, but how they modelled when there were no data at all.

At the heart of the subprime crisis were complex securities with poor data on their underlying collateral. Not surprisingly, few of these traded actively. Unfortunately, one approach was to pretend there is no risk in securities that are difficult to model, do not trade and yet appear safe enough.

There are no perfect answers, but using the few mortgage-backed products that do trade to proxy risk is at least better than the alternative.

Enhancements to today’s risk-forecasting models must address these securities with little pricing information.

Certainly, a flight to simplicity – investors demanding products they can better analyse and value – will lessen the burden. But that still leaves the vagaries of market liquidity. It is here that we must focus our efforts in the future.


Christopher Finger is head of global risk research at RiskMetrics Group