We conduct a laboratory experiment to study whether people intuitively use real-option strategies in a dynamic investment setting. The participants were asked to play as an oil manager and make production decisions in response to a simulated mean-reverting oil price. Using cluster analysis, participants can be classified into four groups, which we label ‘mean-reverting’, ‘Brownian motion real-option’, ‘Brownian motion myopic real-option’, and ‘ambiguous’. We find two behavioral biases in the strategies of our participants: ignoring the mean-reverting process, and myopic behavior. Both lead to too frequent switches when compared with the theoretical benchmark. We also find that the last group behaved as if they have learned to incorporate the true underlying process into their decisions, and improved their decisions during the later stage.
This paper determines the value of asset tradeability in an option pricing framework. In our model, tradeability is valuable since it allows investors to exploit temporary mispricings of stocks. The model delivers several novel insights on the value of tradeability: The value of tradeability is the larger, the higher the pricing efficiency of the market is. Uncertainty increases the value of tradeability, no matter whether the uncertainty results from noise trading or from new information about the fundamental value of the stock. The value of tradeability is the larger, the longer the illiquid stock cannot be traded and the more trading dates the liquid stock offers.
This appendix extends the empirical results in Chesney, Crameri, and Mancini (2011). Informed trading activities on put and call options are analyzed for 19 companies in the banking and insurance sectors from January 1996 to September 2009. Our empirical findings suggest that certain events such as the takeovers of AIG and Fannie Mae/Freddie Mac, the collapse of Bear Stearns Corporation and public announcements of large losses/writedowns are preceded by informed trading activities in put and call options. The realized gains amount to several hundreds of millions of dollars. Several cases are discussed in detail.
Marcet and Marimon (1994, revised 1998) developed a recursive saddle point method which can be used to solve dynamic contracting problems that include participation, enforcement and incentive constraints. Their method uses a recursive multiplier to capture implicit prior promises to the agent(s) that were made in order to satisfy earlier instances of these constraints. As a result, their method relies on the invertibility of the derivative of the Pareto frontier and cannot be applied to problems for which this frontier is not strictly concave. In this paper we show how one can extend their method to a weakly concave Pareto frontier by expanding the state space to include the realizations of an end of period lottery over the extreme points of a flat region of the Pareto frontier. With this expansion the basic insight of Marcet and Marimon goes through - one can make the problem recursive in the Lagrangian multiplier which yields significant computational advantages over the conventional approach of using utility as the state variable. The case of a weakly concave Pareto frontier arises naturally in applications where the principal's choice set is not convex but where randomization is possible.
I evaluate whether the so-called long-run risk framework can jointly explain key features of both equity and bond markets as well as the interaction between asset prices and the macroeconomy. I find that shocks to expected consump-
tion growth and time-varying macroeconomic volatility can account for the level of risk premia and its variation over time in both markets. The results suggest a common set of macroeconomic risk factors operating in equity and bond
markets. I estimate the model using a simulation estimator which accounts for time-aggregation of consumption growth and utilizes a rich set of moment conditions.
In this paper we examine the effects of default and scarcity of collateralizable durable goods on risk-sharing. We assume that there is a large set of assets which all
promise a risk-less payo but which distinguish themselves by the collateral requirement. In equilibrium agents default and the assets have dierent payoffs. Assets with very low collateral requirements can be interpreted as sub-prime loans and these assets are often traded actively in the competitive equilibrium. If there is an abundance of commodities that can be used as collateral and if each agent
owns a large fraction of these commodities, markets are complete and competitive equilibrium allocations Pareto optimal. If, on the other hand, the collateralizable durable good is scarce or if some agents do not own enough of the collateralizable durable good in the first
period, markets can be endogenously incomplete, not all of the available assets are traded in the competitive equilibrium and allocations are not Pareto optimal. We give examples that show that welfare losses can be quantitatively large.
We also examine the scope for government intervention. In particular we ask who in the economy gains and who loses if collateral requirements are regulated exogenously. In our
examples, regulation never leads to a Pareto-improvement. Often, the rich and the poor agents gain if trade is restricted to subprime contracts and lose if trade in these contracts is not allowed.
Prospect Theory is widely regarded as the most promising descriptive model for decision mak-ing under uncertainty. Various tests have corroborated the validity of the characteristic fourfold pattern of risk attitudes implied by the combination of probability weighting and value transformation. But is it also safe to assume stable Prospect Theory preferences at the individual level? This is not only an empirical but also a con-ceptual question. Measuring the stability of preferences in a multi-parameter decision model such as Prospect Theory is far more complex than evaluating single-parameter models such as Expected Utility Theory under the assumption of constant relative risk aversion. There exist considerable interdependencies among parameters such that allegedly diverging parameter combinations could in fact produce very similar preference structures. In this paper, we provide a theoretic framework for measuring the (temporal) stability of Prospect Theory parame-ters. To illustrate our methodology, we further apply our approach to 86 subjects for whom we elicit Prospect Theory parameters twice, with a time lag of one month. While documenting remarkable stability of parameter estimates at the aggregate level, we find that a third of the subjects show significant instability across sessions.
For loss averse investors, a sequence of risky investments looks less attractive if it is evaluated myopically—an effect called myopic loss aversion (MLA). The consequences of this effect have been confirmed in several experiments and its robustness is largely undisputed. The effect’s causes, however, have not been thoroughly examined with regard to one important aspect. Due to the construction of the lotteries that were used in the experiments, none of the studies is able to distinguish between MLA and an explanation based on (myopic) loss probability aversion (MLPA). This distinction is important, however, in discussion of the practical relevance and the generalizability of the phenomenon. We designed an experiment that is able to disentangle lottery attractiveness and loss probabilities. Our analysis reveals that mere loss probabilities are not as important in this dynamic context as previous findings in other domains suggest. The results favor the MLA over the MLPA explanation.
A new model class for univariate asset returns is proposed which involves the use of mixtures of stable Paretian distributions, and readily lends itself to use in a multivariate context for portfolio selection. The model nests numerous ones currently in use, and is shown to outperform all its special cases. In particular, an extensive out-of-sample risk forecasting exercise for seven major FX and equity indices confirms the superiority of the general model compared to its special cases and other competitors. Estimation issues related to problems associated with mixture models are discussed, and a new, general, method is proposed to successfully circumvent these. The model is straightforwardly extended to the multivariate setting by using an independent component analysis framework. The tractability of the relevant characteristic function then facilitates portfolio optimization using expected shortfall as the downside risk measure.
