F. Douglas Foster

Strategic Trading with Asymmetrically Informed Traders and Long-Lived Information

A dynamic model of strategic trading with two asymmetrically informed traders is analyzed where one informed trader knows the information seen by both informed traders, and the other informed trader only knows his private information. While the first informed trader is better informed, the second informed trader can make inferences about this extra information; in fact, the second informed trader can make sharper inferences from the order flow than the market maker about the extra information. In this setting, competition among the informed traders has a very interesting form. The informed trader with the additional information trades less intensely on that information early on, and both informed traders trade very intensely on their common information. This makes it more difficult for the trader with less information to learn about the information he does not have. When there are only a few remaining trading periods and the information known to both traders has largely been revealed through their trading, then the trader with the additional information trades more intensely on the basis of his private information.

Can Speculative Trading Explain the Volume–Volatility Relation?

We derive a speculative trading model with endogenous informed trading that yields a conditionally heteroscedastic time series for trading volume and the squared price changes. We use half-hourly price-change and volume data for IBM during 1988 to test the model and estimate the structural parameters using the simulated method-of-moments estimation procedure. Although the model seems to do a reasonable job fitting the unconditional moments of the volume and the squared price change processes, it fares less well in fitting the relation between current trading volume and lags of trading volume and squared volumes (and its lags) relation to squared price changes.

Discussions on long-term financial choice

We analyse focus group discussions about long-run (retirement) financial decisions, and explore the extent to which participant responses are related to the oft-used behavioural explanations of financial choice. We find that persons of all ages understand the importance of long-term savings, but face many challenges in preparing for retirement. There is mixed support for a range of associated behavioural explanations. Complexity, relevance of decisions, and uncertainty come up repeatedly in all focus groups, irrespective of the age of the participants. The use of heuristics, confidence, costs of mistakes, mental accounting, and the importance of social interaction appeared of less immediate relevance to all groups. We discuss the implications of these findings for how the financial services and superannuation industries communicate with members. There appears to be a general view from the focus groups that breaking large, complex retirement decisions into more manageable pieces (based on personal circumstances) and providing more focused and relevant information to investors would result in more effort and care expended on retirement decisions.

An Application of Bayesian Option Pricing to the Soybean Market

Options pricing techniques have been an important part of finance for some time. Mostapproaches specify a particular stochastic process to represent the price dynamics of theunderlying asset and then derive an explicit pricing model. While this may be acceptablefor standard financial assets, it can be problematic for commodities. Many commoditieshave significant seasonalities and require a far more elaborate time-series specification ofthe price dynamics of the underlying asset. Hence, it becomes difficult at best to deriveexplicit pricing formulae. Further, with the additional complexity of a rich time-seriesspecification, estimation risk becomes a genuine concern.In this paper we suggest an alternative approach. We use numerical Bayestechniques to build a predictive density for the price of the underlying asset (for theexample in this paper we need to predict the soybean cash and futures prices at theoption’s expiration). Bayesian techniques allow for two very important additions. First, wecan integrate out any estimation risk. Second, it allows us to incorporate properly anynon-sample information that we may have. Once the predictive density has beencomputed, we use a procedure proposed by Stutzer (1996) to translate this density to itsrisk-neutral form. Once this is done, pricing European options is very straightforward.To illustrate this approach we consider recent prices of options on soybean futurestraded on The Chicago Board of Trade. We start with a simple vector autoregressivespecification of the spot price return and the basis (defined as the log difference betweenthe futures price and the spot price). We compare this procedure with traditionalapproaches as well as with a non-parametric procedure advocated by Stutzer (1996).

Bayesian Cross Hedging: An Example From the Soybean Market

Following Lence and Hayes (1994a), we study the problem faced by an Iowa farmer who wishes to hedge a soybean harvest using Chicago futures contracts. A time-series model for spot and futures prices is postulated, and numerical Bayesian procedures are used to calculate predictive densities and optimal hedges. The numerical procedures extend earlier analytical work, and easily accommodate alternative views about specification (levels vs. logarithms, trends vs. no trends, etc.), uncertainty about parameterisations (estimation risk), as well as other non-sample information (the likely size of the difference between spot prices in Iowa and Chicago, the tendency of the basis to be large in the spring, the shrinking of the basis as expiration of the future looms, etc.)

Bayesian Prediction, Entropy, and Option Pricingx

This paper studies the performance of the Foster-Whiteman (1999) procedure for using a Bayesian predictive distribution for the future price of an asset to compute the price of a European option on that asset. A technical contribution of the paper is the description of a sequential importance sampling procedure for implementing an informative prior that reflects and rewards past option-pricing success. The risk-neutralization of the predictive distribution is accomplished by Stutzers (1996) constrained KLIC-minimizing change of measure. The procedure is used in weekly pricing of July and November options on soybeans on the Chicago Board of Trade from 1993–1997, and produces option prices that mimic market prices much more closely than those of the Black model or those produced by risk-neutralizing a nonparametric predictive.

Design of MySuper default funds: influences and outcomes

We interview Australian fund executives about how their organisations responded to MySuper, a regulatory framework for default retirement savings funds that providers were required to have in place by the beginning of 2014. We provide an account of the influences on MySuper product design. Our analysis provides insight into how fund providers balanced their perceptions of the needs of default fund members against business considerations. Differences in member bases and organisational circumstances across funds are found to lead to considerable variation in default fund design.

Customer foreign exchange orders: When timing really does matter

This paper investigates customer foreign exchange (FX) transactions to learn if there are any groups of customers whose transactions are related to subsequent FX rate changes. We use a unique data set from an Australian commercial bank with every customer FX trade in the spot Australian Dollar/US Dollar market between 2005 and 2010. We use Monte Carlo simulation to generate benchmark expected cash flows for each individual customer. This enables us to determine whether that customer’s transaction history is well-timed and therefore potentially relevant for FX determination. We find very few customers whose transactions appear well-timed, given subsequent FX rate changes.

Measuring the Information Content of Customer Foreign Exchange Orders

This paper investigates whether customer order flow conveys information about future foreign exchange (FX) prices. We use a unique data set from a leading Australian commercial bank that records every FX trade made by the bank in the spot Australian dollar/US dollar market between 2005 and 2010. We find little evidence in support of a cointegrating relation or a statistically significant correlation between customer order flow and FX returns. However, consistent with the liquidity provision role of non-financial customers in Evans and Lyons ((2002) Order flow and exchange rate dynamics. Journal of Political Economy 110: 170–180), we find a statistically significant negative correlation between order flow from the diversified economic sector and FX returns. A dynamic analysis suggests that order flow has little or no price impact on FX returns. These results suggest that the non-financial customer order flow of a commercial bank does not carry information about FX prices.

Does Portfolio Emulation Outperform its Target Funds

An emulation fund is designed to reduce trading activity, thereby lowering costs, for a multi-manager fund. It does this by delaying, and potentially combining, trading decisions from each employed fund manager to eliminate offsetting trades (e.g. one manager may buy a stock for her fund while another manager sells the same stock at approximately the same time for his fund). While lowering transaction costs is a key benefit of an emulation strategy, there has been little research that compares the reduction in transaction costs with the opportunity costs of delaying trade. Using reported equity trades for a large Australian pension fund, we simulate the consequences of an emulation strategy. We find that simulated emulation trades underperform those trades made by the employed (or target) fund over our sample period. That is, the opportunity cost of delayed trading significantly outweighs transaction cost reductions. Overall, we do not find strong evidence to support emulation from a cost–benefit perspective before management fees and taxes.