Looking at the financial markets and the world at large, it is hard to escape the feeling that we are living in conditions that would have been considered extreme outliers 10 years ago. It used to be thought that European countries, which follow more socialist politics than the United States and have stronger safety nets, would never tolerate unemployment as high as Americans have been willing to accept. But while the United States is now at 4.4% unemployment, the unemployment rate across the Eurozone remains in double digits, where it has stood for years. In some countries, it has been more than 20% for adults and 50% for young people, and the situation does not appear to be resolving quickly. Meanwhile, in an effort to stimulate their economies, central banks in the United States and Europe expanded their balance sheets to enormous levels and drove interest rates so low that they have gone negative in a number of countries. Despite economic conditions that are bad in Europe and still weak in the United States (even with very low measured unemployment), the stock market is at record highs. And again, in the face of what feels like extraordinary uncertainty about the near future, stock market volatility is close to its all-time low. Then, of course, we have had Brexit, the election of a U.S. president with zero experience in government, and a man who had never been elected to public office as president of France. The Republicans lost landslide elections in 2006 and 2008, but in 2016 they achieved total domination of government at all levels. Millions of refugees are attempting to enter Europe by sea and the United States by land, and there is little clear idea of how to handle the humanitarian crisis.
Ten years ago, if one had guessed the probability that any one of these things would come to pass, it would have been considered a complete outlier. But today, outliers are everywhere. What this means, unfortunately, is that we are falling outside the “parameter space” in which our models are designed to function, where we can feel that we have some idea what we are doing based on past experience. Now, as one of the articles in this issue discusses, because our valuation models don’t allow for negative interest rates, traders don’t know how they should make quotes on swaptions. In many ways, we are “flying blind.”
So let us turn, then, to this issue of The Journal of Derivatives. We begin with an article by Tim Xiao, which offers a systematic approach to formally incorporating counterparty credit risk, and the mitigation of that risk by the use of collateral, into valuation of derivative contracts. This is a timely subject given the recent requirement to impose credit value adjustments in accounting for derivatives positions, and heightened interest in measuring and managing counterparty risk generally.
Next, Riccardo Rebonato explores the market price of volatility risk in the swaptions market. The concept is relatively simple: extract the risk-neutral volatility from swaptions prices and subtract the market’s forecast of empirical volatility (“actuarial volatility,” in Rebonato’s terminology) to get the volatility risk premium. But estimating actuarial swaption volatility is not so easy, because there are two maturities and a volatility smile across strikes for each combination, making what is typically called a volatility cube. Rebonato develops a nice modeling approach to limiting the dimensionality that allows efficient use of information from all of the swaptions in the estimation set. The resulting implied risk premia are found to exhibit a number of interesting and highly plausible features.
In the following article Jr-Yan Wang and Tian-Shyr Dai consider another important aspect of credit risk—the need to specify an assumed recovery rate in case of default. For want of more precise estimates, expected recoveries are frequently simply set to a fixed value of 40%. But empirical evidence shows that actual recovery rates are negatively related to default probabilities and they vary over time. The authors’ suggestion is to estimate the statistical relationship between such factors and realized recoveries and to use the model recovery rate predictions in pricing credit-sensitive contracts. They illustrate the use of the model in pricing convertible bonds.
As mentioned earlier, since our valuation models don’t allow for negative interest rates, traders don’t know how they should make quotes on swaptions. Vincenzo Russo and Frank Fabozzi document the problem of quoting swaptions when rates are negative and compare three alternatives for calibrating standard interest rate models empirically.
The last two articles describe a couple of contracts with “exotic” option features. Ling Xin, Philip Yu, and Kin Lam describe and analyze the “accumulator” contract. This is one of a variety of relatively new financial products in which the payoff is tied to the behavior of a stock index or other asset price, with barrier and guarantee features that make the payoff highly path-dependent and hard to evaluate for a nonexpert. They are designed to appeal to small (unsophisticated?) investors, while providing an attractive return to the issuer. Last is an article by Carlos Ortiz, Charles Stone, and Anne Zissu that describes a kind of bond issued by a beverage firm and paid off in the form of a lifetime supply of beer. Sound attractive? Maybe, but it’s important to live long enough to get a good return on the investment.
Summer is here! Even though, according to the calendar, the actual first day of summer is still a long way off, as soon as spring semester classes are done, it’s summer as far as I’m concerned. And as a typical finance professor, what I am saying today is: “Summer is here! Now I’ll finally be able to get some work done!”
I hope your summer is as enjoyable as I expect mine to be.
Stephen Figlewski
Editor
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