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12/20/2011

Reflections on the FMA Annual Meeting


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MyronScholes
Myron Scholes
Credit: Wikimedia Commons

In October, I attended the annual meeting of the Financial Management Association International in Denver. After listening to the keynote address by Nobel Laureate Myron Scholes (of Black-Scholes fame), I left the conference hall along with the crowd to attend the reception. While waiting for the elevator, I noticed a situation that was pertinent to Scholes’ presentation. Because there was no stairway leading from the conference level to the concourse, people had no choice but to take the elevators, and a small jam formed. I was sure that there would be a stairway, if only for fire-safety reasons. I searched the floor and found only a door leading to the basement. I considered how dangerous this would be in a fire emergency. Fires in the modern hotels are relatively infrequent occurrences and a hotel can stay past its useful life without any. In that case, the cost of adding an extra stairway would be entirely sunk.

Because hotel managers regard the cost of extra stairway as deadweight, this scenario is a vivid demonstration of why regulations are necessary and why our economic science is woefully inadequate. In his presentation, Scholes used the less dramatic example of children in a playground, who reform their groups after some of them have spotted an ice cream truck. While the situation I describe above is commonplace, modern financial theory has no clue how to describe it quantitatively.

Let’s replace children or conference attendees with homeowners who have a rational expectation to sell their homes in, say, three months, and assume that this is priced into MBS (mortgage-backed securities). The relevance of my example above to the state of the MBS market is evident, and the observation in the hotel reinforced the impression. For instance, if all of the homeowners decide to sell their houses at the same time because of an external adverse event, nobody in a given neighborhood will be able to sell. Pricing such a catastrophic event in a single neighborhood—for instance, bankruptcy of a leading company in a factory town—can be statistically complicated but is relatively straightforward philosophically. In the past it was assumed that catastrophic risks can be successfully diversified away in MBS portfolios but the recent crisis demonstrated that single obligors in these portfolios were not as independent as it has been assumed in modeling. There is probably a hierarchy of correlated adverse events in real estate (a neighborhood, a state, the country) but nobody currently knows how to treat such a hierarchical chain of adverse events.

Below I describe other observations I made during the annual meeting.

ASSET PRICING

Quantitative asset pricing has a diminishing importance in finance conferences. Academic research has moved into niche areas such as asset pricing of ETFs (exchange-traded funds), where liquidity—the ability to sell an asset quickly—is intimately related to valuation. Liquidity and hedges should not be understood entirely in terms of financial assets.

For instance, drilling and rig infrastructure are by far the largest costs in oil and gas production. Fan Chen of the University of Oklahoma pointed out that in the US, there is a significant market for the leasing of rigs and drilling equipment, and turnover of equipment can be achieved in about three months. In the Middle East, where most production companies are state owned, the time is much longer and the correlation of oil prices and equipment utilization much lower. The UK is somewhere between Texas and the Middle East. In the same “Commodity Derivatives" session, Kazuhiko Ohashi of Hitotsubashi University, Tokyo, discussed the pricing of emission allowances. While cap-and-trade schemes for energy commodities are anathema to many US politicians, pollution-allowance markets are alive and well in the European Union and Nordic countries. There are several approaches to pricing pollution allowances (see Çetin and Verschuere 2009; (Daskalakis, Psychoyios, and Markellos 2009; (Lerner 2010; (Paollela and Taschini 2008; (Uhrig-Homburg and Wagner 2009). Ohashi and his colleagues propose a structural model— i.e., a model that relies on economic fundamentals—rather than a reduced-form approach favored in most other papers. They price environmental permits as options on spreads between alternative (supposedly clean and unclean) energy sources. Their approach is purely analytical and uses Black-Scholes-type formulas, which are relatively easy to program despite their visible complexity.

Equity modeling, a field that is almost 60 years old, still shows some activity. Despite notoriously poor performance, capital asset pricing model (CAPM) is still explained in textbooks, and its variations are used in many academic research papers. Required return on a stock or portfolio is used as a regressor in numerous contexts such as valuation, events studies, and identification of bubbles. Zijun Wang of Texas A&M University made the most extensive empirical evaluation of conditional CAPM (Jagannathan and Wang 1996) so far, and discovered that it works no better than the old, unconditional version.


American Gas Association


One quickly developing academic field is nontrading. There exist assets (for instance, nontransferable shares) that have definite value and sometimes even recorded performance, yet there is no market. Another variation on this theme is information provided by periods of no trading, or no price change, in high-frequency trading.

In the next-to-last session of the annual meeting, I learned of some interesting developments taking place at Cornell University. In many trading pools, there is no reporting distinction between buys and sells. However, many analyses are based on using buying or selling pressure to infer other characteristics of the market. In 1991, Charles Lee and Mark Ready proposed an algorithm that predated electronic trading, based on an artificial classification between seller-initiated quotes (as performed near the ask price) and buyer-initiated quotes (as performed near the bid price). When the execution price is near the midpoint, the implied direction of trading is assumed to coincide with the previous quote change. Now, with massive amounts of data available in dark pools, we can calibrate the Lee-Ready Trade Classification Algorithm. Cornell’s Pamela Moulton observed that the algorithm performs almost perfectly on a daily basis, but functions rather poorly with high-frequency trading. However, there is a way to improve its performance significantly: discarding the trades within approximately one second of the given trade. The reason for this is obscure, as is the connection of the deadbeat period to other characteristics of the market such as depth and computer infrastructure.

Here we come to a problem in high-frequency trading imposed by relativity. As James Upson of the University of Texas at El Paso noted to me, the price signal from an assumed trading floor in New York would reach Los Angeles about 10 milliseconds later than it would reach midtown Manhattan. Even if we assume no inertia of the computer and display systems, the information available to a trader in Los Angeles would lag the information available in New York. The value of 10 milliseconds seems very low. However, if there are 100 orders for a security per second, a whopping 37% of orders will be separated by less time than that, assuming the Poisson distribution (i.e., no clustering), which only makes the situation worse. During this second, if there is a fair play, no one could make any sense of the information provided by the changing quotas. No extension of bandwidth could ameliorate this situation.

We must come up with a new notion of the trading book, which now exists not only in a platonic space of economic state variables (mainly price and volume) but also in real time and space. Electronic trading networks are starting to acquire physical characteristics akin to an electric power grid. The consequences for trading are unclear, but with the world becoming more and more dependent on the flawless performance of computers, we must figure this out soon.

REFERENCES

  1. Çetin, Umut, and Michel Verschuere. 2009.
    “Pricing and Hedging in Carbon Emissions Markets." International Journal of Theoretical and Applied Finance. 12: 7.
  2. Daskalakis, George, Dimitris Psychoyios, and Raphael N. Markellos. July 2009. >"Modeling CO2 Emission Allowance Prices and Derivatives: Evidence from the European Trading Scheme." Journal of Banking and Finance. 33: 7.
  3. Jagannathan, Ravi, and Zhenyu Wang. March 1996. "The Conditional CAPM and the Cross-Section of Expected Returns." Journal of Finance 51: 1.
  4. Lee, Charles M.C., and Mark J. Ready. June 1991. “Inferring Trade Direction from Intraday Data." Journal of Finance 46: 2.
  5. Lerner, Peter B. 2010. "Attempts at Pricing of the Regulatory Commodity: EU Emission Credits." Environmental Economics 1: 1.
  6. Paolella, Marc S., and Luca Taschini. 2008. "An Econometric Analysis of Emission Allowance Prices." Journal of Banking and Finance 32: 10.
  7. Uhrig-Homburg, Marliese, and Michael Wagner. Winter 2009. “Futures Price Dynamics of CO2 Emission Allowances: An Empirical Analysis of the Trial Period." Journal of Derivatives 17: 2.

–Peter Lerner, PhD, MBA is a semi-retired financial researcher who lives in Ithaca, NY. Last semester he taught International Financial Management in Rollins College, FL.


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