Saturday 12 October 2013

Reciprocity as the Foundation of Financial Economics

This is the third of four posts developing my argument that at the heart of financial economics is the principle of balanced reciprocity.  In the previous post, Probability Theory: the synthesis of commerce and ethics I discussed the origins of probability. Here I describe the Fundamental Theorem of Asset Pricing in the context of these ethical origins.  This is wonkish but does give an account of the role of the Black-Scholes-Merton approach to pricing derivatives in the development of Financial Economics since the 1970s.

The Fundamental Theorem of Asset Pricing (FTAP) consists of two statements, (e.g. [36, Section 5.4])
1. A market admits no arbitrage, if and only if, the market has a martingale measure.
2. Every contingent claim can be hedged, if and only if, the martingale measure is unique.

The context of the FTAP

The FTAP emerged between 1979 and 1983 ([13], [14], [15]) as Michael Harrison sought to establish a mathematical theory underpinning the Black-Scholes-Merton (BSM) equation for pricing options, which was introduced in 1973.
In the late 1960s, Fischer Black and Myron Scholes worked as investment consultants and one of the problems the pair addressed was the valuation of ‘warrants’, options bundled with bonds. Black was an applied mathematician who had worked in consultancy for Jack Treynor around the time that Treynor developed his version of the Capital Asset Pricing Model (CAPM). Scholes had studied for a doctorate under Eugene Fama looking at risk-reward in the context of efficient markets [35]. Black tackled the problem of pricing warrants as an applied mathematician: the value of the warrant would be a function of the underlying asset’s price and amenable to the type of calculus that had been employed since Newton and Leibnitz. Scholes approached the problem from a financial perspective: the risk of holding a warrant could be removed by holding a complementary (short) position in the underlying asset, by hedging. What Scholes did not know was how to establish the size of the hedging portfolio, but when he discussed this with Black they realised the solution was in the slope of the function relating the warrant price and asset price, a result that had been anticipated by Thorp and Kassouf [22, pp 130—131].
Simultaneously, Robert C. Merton, who had studied advanced engineering mathematics before becoming a student of Paul Samuelson, was considering the problem of pricing warrants from a different perspective. Samuelson had never accepted Markowitz’s criterion of trading the expected returns of a portfolio against the variance of returns [33], which was a foundation of CAPM and Scholes’ work, so Merton tackled the problem of valuing warrants by maximising expected utility employing the stochastic calculus that had become important in aeronautical and electronic engineering. This work was published in 1969 ([32], [24]).
Despite the fact that Black never liked Merton’s highly mathematical technique, Scholes discussed their work with Merton in 1970. Merton saw how the Black-Scholes approach of hedging could be incorporated into his own continuous time models, removing the need to incorporate an arbitrary utility function in solving the pricing problem. Merton showed that a portfolio made up of: a single warrant, or an option; a hedging position in the risky underlying asset; and a funding position in the riskless bank account, would offer the same, certain, return as the initial cost of the portfolio deposited in the riskless bank account. It seemed that both subjectivity and risk had been removed from the pricing problem.
In October 1970 Black and Scholes submitted their work to the Journal of Political Economy and then the Review of Economics and Statistics, but it was rejected without review, on the basis that there was not enough economics in it. The paper was only published by the Journal of Political Economy [3] in 1973 after the intervention of influential academics and shortly after the opening of the Chicago Board Options Exchange ([2, p 314—315], [22, pp 133—136]). Merton published his approach almost simultaneously [25].
When BSM was being developed option pricing was a relatively unimportant activity. Gambling legislation in the United States meant that options were only traded on ‘deliverable’ assets, principally agricultural commodities, and these markets were stagnant [22, pp 142-145]. However, following the ‘Nixon Shock’ of August 1971, the Bretton-Woods system of fixed exchange rates collapsed and in the aftermath, interest rates, exchange rates and commodity prices became much more volatile. Options, which have been a feature of financial practice since the seventeenth century, and were widely traded before the suspension of the European financial markets during the First World War [28], re-emerged as a tool to insure against volatile asset prices.
Despite the financial rational for options, their legitimacy with regard to gambling legislation was still ambiguous. The introduction of BSM delivered a mathematical equation that defined the price of an option in terms of known parameters, making their valuation deterministic. Trading in options could not be gambling, given that there was no speculation in their valuation. Donald MacKenzie reports the view of the legal counsel to the Chicago Board of Trade at the time, Burton Rissman
Black-Scholes was what really enabled the exchange to thrive ...we were faced in the late 60s — early 70s with the issue of gambling. That fell away, and I think Black-Scholes made it fall away. It wasn’t speculation or gambling it was efficient pricing. [22, p 158]
Essentially a mathematical formula transformed index options from being illegitimate gambles to deterministic investments.
Both the Black-Scholes and Merton approaches to pricing options involved heuristic arguments, they were‘engineering solutions’. Harrison sought to establish a rigorous option pricing‘theory’ to support the range of mathematical models developed on the back of the explosion in derivatives markets [22, pp 140—141]. Harrison, and his colleagues, were successful in their mission and opened finance to investigation by pure mathematicians, such as [34], [5], [6], and by 2000, any mathematician working on asset pricing would do so within the context of the FTAP.
The FTAP is not well known outside the academic field of financial mathematics. Practitioners focus on the models that are a consequence of the Theorem while social scientists focus on the original Black-Scholes-Merton approach as an exemplar. Even before the market crash of 1987 practitioners were sceptical as to the validity of the prices produced by their models ([26, pp 409-410 ], [22, p 248], [16]) and today the original Black-Scholes equation is used to measure market volatility, a proxy for uncertainty, rather than to ‘price’ options.
However, the status of the Black-Scholes model as an exemplar in financial economics has been enhanced following the development of the FTAP. Significantly, the theorem unifies different approaches in financial economics. The most immediate example of this synthesis was that in the course of the development of the FTAP it was observed that a mathematical object, the Radon-Nikodym derivative, which is related to the stochastic calculus Merton employed involved the market-price of risk (Sharpe ratio), a key object in CAPM that Black used. Without the FTAP the two approaches are incongruous [21, p 834]. Overall, as will be discussed in full in the next section, the FTAP brings together: Merton’s approach employing stochastic calculus advocated by Samuelson; CAPM, developed by Treynor and Sharpe; martingales, a mathematical concept employed by Fama in the development of the Efficient Markets Hypothesis; and the idea of incomplete markets, introduced by Arrow and Debreu.
The synthesis by the FTAP of a‘constellation of beliefs, values, techniques’ represented a Kuhnian paradigm for financial economics focused on the Black-Scholes-Merton approach to pricing options. The paradigm was further strengthen by the fact that the unification was presented as emerging out of pure mathematics and appeals to Realists who believe in the transcendence of mathematics and the existence of an Idealised economic universe.

An Ethical Analysis of the FTAP

The FTAP is a theorem of mathematics, and the use of the term ‘measure’ in its statement places the FTAP within the theory of probability formulated by Andrei Kolmogorov in 1933 [20]. Kolmogorov’s work took place in a context captured by Bertrand Russell, who in 1927 observed that
It is important to realise the fundamental position of probability in science. ... As to what is meant by probability, opinions differ. [31, p 301]
In the 1920s the idea of randomness, as distinct from a lack of information, was becoming substantive in the physical sciences [41, pp 147—157] because of the emergence of the Copenhagen Interpretation of quantum mechanics. In the social sciences, Frank Knight argued that uncertainty was the only source of profit [19, III.VII.1—4] and the concept was pervading John Maynard Keynes’ economics ([27], [38, pp 84—88]).
Two mathematical theories of probability had become ascendant by the late 1920s. Richard von Mises (brother of the Austrian economist Ludwig) [40] attempted to lay down the axioms of classical probability within a framework of Empiricism, the‘frequentist’ or ‘objective’ approach. To counter—balance von Mises, the Italian actuary Bruno de Finetti presented a more Pragmatic approach, characterised by his claim that “Probability does not exist” because it was only an expression of the observer’s view of the world. This ‘subjectivist’ approach was closely related to the less well-known position taken by the Pragmatist Frank Ramsey who developed an argument against Keynes’ Realist interpretation of probability presented in the Treatise on Probability ([29], [30], [4], [7]).
Kolmogorov addressed the trichotomy of mathematical probability by generalising so that Realist, Empiricist and Pragmatist probabilities were all examples of ‘measures’ satisfying certain axioms. In doing this, a random variable became a function while an expectation was an integral: probability became a branch of Analysis, not Statistics.
Von Mises criticised Kolmogorov’s generalised framework as un-necessarily complex [40, p 99] while the statistician Maurice Kendall argued that abstract measure theory failed “to found a theory of probability as a branch of scientific method” [18, p 102]. More recently the physicist Edwin Jaynes champions Leonard Savage’s subjectivist Bayesianism as having a “deeper conceptual foundation which allows it to be extended to a wider class of applications, required by current problems of science” [17, p 655].
The objections to measure theoretic probability for empirical scientists can be accounted for as a lack of physicality. Frequentist probability is based on the act of counting; subjectivist probability is based on a flow of information, which, following Claude Shannon, is now an observable entity in Empirical science. Measure theoretic probability is based on abstract mathematical objects unrelated to sensible phenomena. However, the generality of Kolmogorov’s approach made it flexible enough to handle problems that emerged in physics and engineering during the Second World War and his approach became widely accepted after 1950 because it was practically more useful.
In the context of the first statement of the FTAP, a ‘martingale measure’ is a probability measure, usually labelled , such that the (real, rather than nominal) price of an asset today, X0, is the expectation, using the martingale measure, of its (real) price in the future, XT . Formally,
X0 = E[XT ].
The abstract probability distribution is defined so that this equality exists, not on any empirical information of historical prices or subjective judgement of future prices. The only condition placed on the relationship that the martingale measure has with the ‘natural’, or ‘physical’,probability measures usually assigned the label ,is that they agree on what is possible.
The term ‘martingale’ in this context derives from doubling strategies in gambling and it was introduced into mathematics by Jean Ville in a development of von Mises work of 1939. The idea that asset prices have the martingale property was first proposed by Benoit Mandelbrot [23] in response to an early formulation of Eugene Fama’s Efficient Market Hypothesis (EMH) [8], the two concepts being combined by Fama in 1970 [9]. For Mandelbrot and Fama the key consequence of prices being martingales was that the current price was independent of the future price and technical analysis would not prove profitable in the long run. In developing the EMH there was no discussion on the nature of the probability under which assets are martingales, and it is often assumed that the expectation is calculated under the natural measure. While the FTAP employs modern terminology in the context of value-neutrality, the idea of equating a current price with a future, uncertain, payoff would have been understood by Olivi and obvious to Huygens, both working in an explicitly ethical framework.
The other technical term in the first statement of the FTAP, arbitrage, has long been used in financial mathematics. In Chapter 9 of the Liber Abaci Fibonacci discusses ‘Barter of Merchandise and Similar Things’,
20 arms of cloth are worth 3 Pisan pounds and 42 rolls of cotton are similarly worth 5 Pisan pounds; it is sought how many rolls of cotton will be had for 50 arms of cloth. [37, p 180]
In this case there are three commodities, arms of cloth, rolls of cotton and Pisan pounds, and Fibonacci solves the problem by having Pisan pounds ‘arbitrate’, or ‘mediate’ as Aristotle might say, between the other two commodities. Over the centuries this technique of pricing through arbitration evolved into the Law of One Price: if two assets offer identical cash flows then they must have the same price. This was employed by Jan de Witt in 1671 when he solved the problem of pricing life annuities in terms of redeemable annuities, based on the presumption that
the real value of certain expectations or chances of objects, of different value, should be estimated by that which we can obtain from as many expectations or chances dependent on one or several equitable contracts. [39, p 313, quoting De Witt]
In 1908 Vincent Bronzin published a text which discusses pricing derivatives by ‘covering’, or hedging, them with portfolios of options and forward contracts employing the principle of‘equivalence’ [43]. In 1965 the mathematicians, Edward Thorp and Sheen Kassouf, combined the Law of One Price with basic techniques of calculus to identify market mis-pricing of warrant prices and in 1967 they published their methodology in a best-selling book, Beat the Market.
Within neo-classical economics, the Law of One Price was developed in a series of papers between 1954 and 1964 by Kenneth Arrow, Gérard Debreu and Lionel MacKenzie in the context of general equilibrium, in particular the introduction of the Arrow Security, which, employing the Law of One Price, could be used to price any asset [1]. It was on this principle that Black and Scholes believed the value of the warrants could be deduced by employing a hedging portfolio, in introducing their work with the statement that “it should not be possible to make sure profits” [3] they were invoking the arbitrage argument, which had an eight hundred year history.
In the context of the FTAP, ‘an arbitrage’ has developed into the ability to formulate a trading strategy such that the probability, under a natural or martingale measure, of a loss is zero, but the probability of a positive profit is not. This definition is important following Hardie’s criticism of the way the term is applied loosely in economic sociology, and elsewhere [12]. The important point of this definition is that, unlike Hardie’s definition [12, p 243], there is no guaranteed (strictly positive) profit.
To understand the connection between the financial concept of arbitrage and the mathematical idea of a martingale measure, consider the most basic case of a single asset whose current price, X0, can take on one of two (present) values, XT D < XTU, at time T > 0, in the future. In this case an arbitrage would exist if X0 < XT D < XTU: buying the asset now, at a price that is less than or equal to the future pay-offs, would lead to a possible profit at the end of the period, with the guarantee of no loss. Similarly, if XT D < XT  < X0, short selling the asset now, and buying it back would also lead to an arbitrage. So, for there to be no arbitrage opportunities we require that

This implies that there is a number, 0 < q < 1, such that
X0 =
XT D + q(X T U - X T D)
qXT U + (1 - q)X T D.
The price now, X0, lies between the future prices, XT U and XT D, in the ratio q : (1 - q) and represents some sort of ‘average’. The first statement of the FTAP can be interpreted simply as “the price of an asset must lie between its maximum and minimum possible (real) future price”.
If X0 < XT  < XT U we have that q < 0 where as if XT  < XT U < X0 then q> 1, and in both cases q does not represent a probability measure which by Kolmogorov’s axioms, must lie between 0 and 1. In either of these cases an arbitrage exists and a trader can make a riskless profit, the market involves ‘turpe lucrum’. This account gives an insight as to why James Bernoulli, in his moral approach to probability, considered situations where probabilities did not sum to 1, he was considering problems that were pathological not because they failed the rules of arithmetic but because they were unfair.
It follows that if there are no arbitrage opportunities then quantity q can be seen as representing the ‘probability’ that the XT U price will materialise in the future. Formally
X0 =
qXT U + (1 - q)X T D
E[XT ].
The connection between the financial concept of arbitrage and the mathematical object of a martingale is essentially a tautology: both statements mean that the price today of an asset must lie between its future minimum and maximum possible value.
This first statement of the FTAP was anticipated by Ramsey in 1926 when he defined ‘probability’ in the Pragmatic sense of ‘a degree of belief’ and argues that measuring ‘degrees of belief’ is through betting odds [29, p 171]. On this basis he formulates some axioms of probability, including that a probability must lie between 0 and 1 [29, p 181]. He then goes on to say that
These are the laws of probability, ... If anyone’s mental condition violated these laws, his choice would depend on the precise form in which the options were offered him, which would be absurd. He could have a book made against him by a cunning better and would then stand to lose in any event. [29, p 182]
This is a Pragmatic argument that identifies the absence of the martingale measure with the existence of arbitrage and today this forms the basis of the standard argument as to why arbitrages do not exist: if they did the, other market participants would bankrupt the agent who was mis-pricing the asset. This has become known in philosophy as the ‘Dutch Book’ argument and as a consequence of the fact/value dichotomy this is often presented as a ‘matter of fact’. However, ignoring the fact/value dichotomy, the Dutch book argument is an alternative of the ‘Golden Rule’— “Do to others as you would have them do to you.”— it is infused with the moral concepts of fairness and reciprocity ([42], [11]).
The essential result of this paper is that embedded at the heart of the first statement of the FTAP is the ethical concept Justice, capturing the social norms of reciprocity and fairness. This is significant in the context of Granovetter’s discussion of embeddedness in economics [10]. It is conventional to assume that mainstream economic theory is ‘undersocialised’: agents are rational calculators seeking to maximise an objective function. The argument presented here is that a central theorem in contemporary economics, the FTAP, is deeply embedded in social norms, despite being presented as an undersocialised mathematical object. This embeddedness is a consequence of the origins of mathematical probability being in the ethical analysis of commercial contracts: the feudal shackles are still binding this most modern of economic theories.
Ramsey goes on to make an important point
Having any definite degree of belief implies a certain measure of consistency, namely willingness to bet on a given proposition at the same odds for any stake, the stakes being measured in terms of ultimate values. Having degrees of belief obeying the laws of probability implies a further measure of consistency, namely such a consistency between the odds acceptable on different propositions as shall prevent a book being made against you. [29, p 182—183]
Ramsey is arguing that an agent needs to employ the same measure in pricing all assets in a market, and this is the key result in contemporary derivative pricing. Having identified the martingale measure on the basis of a ‘primal’ asset, it is then applied across the market, in particular to derivatives on the primal asset but the well-known result that if two assets offer different ‘market prices of risk’, an arbitrage exists. This explains why the market-price of risk appears in the Radon-Nikodym derivative and the Capital Market Line, it enforces Ramsey’s consistency in pricing.
The second statement of the FTAP is concerned with incomplete markets, which appear in relation to Arrow-Debreu prices. In mathematics, in the special case that there are as many, or more, assets in a market as there are possible future, uncertain, states, a unique pricing vector can be deduced for the market because of Cramer’s Rule. If the elements of the pricing vector satisfy the axioms of probability, specifically each element is positive and they all sum to one, then the market precludes arbitrage opportunities. This is the case covered by the first statement of the FTAP.
In the more realistic situation that there are more possible future states than assets, the market can still be arbitrage free but the pricing vector, the martingale measure, might not be unique. The agent can still be consistent in selecting which particular martingale measure they choose to use, but another agent might choose a different measure, such that the two do not agree on a price. In the context of the Law of One Price, this means that we cannot hedge, replicate or cover, a position in the market, such that the portfolio is riskless. The significance of the second statement of the FTAP is that it tells us that in the sensible world of imperfect knowledge and transaction costs, a model within the framework of the FTAP cannot give a precise price. When faced with incompleteness in markets, agents need alternative ways to price assets and behavioural techniques have come to dominate financial theory. This feature was realised in The Port Royal Logic when it recognised the role of transaction costs in lotteries.


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  1. Hi Tim

    could you please put the four links in the sidebar

    1. Not that expert with Blogger, but the pieces are the preceding 2, e.g.

  2. Dr. Tim Johnson: Interesting piece. Out of curiosity, would you be open to corresponding over e-mail?


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