Category Archives: measuring risk

Climate apples and oranges

Monday’s post discussed a proposal by Vikram Pandit, the CEO of Citigroup, calling for a comparison of the results produced by risk models across different banks when evaluating a standardized “hypothetical” portfolio of assets. Exercises like this are standard fare in many research fields where modeling encompasses a broad array of complicated issues, and there can be wide disparity in both structural and parameter choices.

Let’s look at the economics of controlling greenhouse gas emissions. A number of major research institutions have developed economic models to assess the costs of mitigation, including one of the MIT centers where I work. The results from these models play a useful role in public discussion about what should be done.

But these models are inherently very complicated. Greenhouse gases are produced in many different sectors of the economy, in particular in the energy sector which is, in turn, an input to many other sectors, so an economy wide model is required. The greenhouse gas problem operates at long time scales, so technological evolution over many decades is key. Finally, greenhouse gases are a global public goods problem, so a global economic model is required. Building a model to meet these demands is a heroic effort, demanding many judgment calls on major issues.

Understanding the different assumptions and structural choices and how they impact the results is useful, and can shape how the results are read. During the Bush administration, the U.S. government, which underwrites much of the research in this area, ran a comparison exercise on these economic models similar to what Vikram Pandit is proposing for bank risk models. They took 3 of the leading models, and had them generate a suite of diagnostic results when analyzing a common set of policies. They then published an analysis of the different results and the underlying modeling choices that generated the differences. This was done as a part of the Climate Change Science Program and the full results can be found here.

To illustrate the variation across models, here’s just one of the diagnostics, the forecasted change in the price of natural gas. The three models produced strikingly different results. One model produces a forecast that increases more than 800% by the end of the century under modest emissions constraints, while another forecasts increases less than 200%.

Seeing such widely variant results is an eye opener. Novices in any research area often take modeling results for granted. Seasoned researchers are more attuned to the weaknesses and uncertainties and range of different opinions across the scientific community. Comparison exercises, like the one done by the U.S. Climate Change Science Program, or like the one Citibank’s Vikram Pandit is proposing, shine light on differences that the public needs to be better attuned to.

Of course, knowing that there are differences isn’t the end of the process, and doesn’t solve all of the problems. But it’s a useful contribution.

Banking apples and oranges

Vikram Pandit, the CEO of Citigroup, used an opinion piece in last week’s Financial Times to make an interesting proposal on risk disclosures: banks and other financial institutions should be required to report how their internal modeling assesses the risk in a “benchmark” portfolio. Regulators would define the contents of this hypothetical portfolio, and banks would report “a hypothetical loan/loss reserve level, value at risk, stress-test results and risk-weighted assets.”

It’s a useful proposal that could give investors and other market participants additional useful information. But it also has its limitations, and does not resolve some inherent problems with risk-based capital requirements, and does not eliminate the need to control bank size and risk by other means.

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Is that a fat tail I see?

One side effect of the financial crisis is a much wider familiarity with the wonky lexicon of risk management, which is generally a good thing. But it has its drawbacks. Once a person has learned to spot a black swan, it seems there are black swans everywhere. Of course, the very ubiquity means one isn’t really talking about black swans. Overuse of the term threatens to rob it of its special meaning.

Javier Blass reports in the Financial Times that

One buzzword – “tail risk” – is dominating oil markets and could have big implications for prices for 2012. If this year was marked by relative stability in crude prices, with oil trading in a narrow band between $100 and $120 a barrel in spite of turmoil in the Middle East, next year may be very different. Oil traders and investors are bracing themselves for a rougher ride. In the trading rooms of London, New York and Geneva the talk is of tail risks, low probability events that have an outsize impact on prices. The problem, says Daniel Jaeggi, head of trading at Mercuria, the Geneva-based oil trading house, is that these tails are “currently inordinately fat”. On the one hand are intensifying fears over the eurozone crisis, a bearish factor. On the other, the continuing political turmoil in the Middle East could be bullish. “This means that the [price] outcomes could be substantially altered from the base case if anyone of a number of low probability events materialises,” he says.

So, the probability of prices far above and below the base case is higher than usual. That’s risk alright, but it’s not necessarily a fat tail. It could just be a plain vanilla increase in variance. If the percent change in price is a normally distributed random variable, and the variance goes up, that gives a higher probability of prices far above and below the base case. A fat tail is something more. The normal distribution does not have fat tails, no matter how high the variance.

Maybe the tails are fat. Or maybe it’s just a plain vanilla increase in risk. Not everyone is as punctilious as a pedant on such fine points.