Value date

Abstract: Due to their forward-looking nature, derivative markets provide monetary authorities with a rich source of information for gauging market sentiment. For example, a futures price gives a widely used measure of the market's views about the future value of an asset, namely its mean or expected value at the maturity date of the futures contract. Moreover, the information available from futures prices can be extended by using option prices to estimate the market's entire probability distribution of the future value of an asset. This paper develops various techniques for estimating the market's probability distribution of the future value of an underlying asset from the prices of options on that asset. It discusses the relative merits and drawbacks of each approach, and shows how our preferred approach can be applied to estimate ex ante probability distributions using LIFFE equity and interest rate options, and Philadelphia Stock Exchange currency options. The paper then illustrates the potential value of this type of information to the policy-maker in assessing monetary conditions and conducting monetary operations. Finally, the paper looks at the limitations in data availability and highlights some areas for future research.

Abstract: In a financial market where traders are risk averse and short lived, and prices are noisy, asset prices today depend on the average expectation today of tomorrow’s price. Thus (iterating this relationship) the date 1 price equals the date 1 average expectation of the date 2 average expectation of the date 3 price. This will not in general equal the date 1 average expectation of the date 3 price. We show how this failure of the law of iterated expectations for average belief can help understand the role of higher order beliefs in a fully rational asset pricing model and explain overreaction to (noisy) public information.

Abstract: One of the most important daily decisions that a Genco has to make is to allocate generation assets between the forward and spot markets. That is, how much capacity should be contracted in the forward market and how much should be kept to bid in spot market? This paper focuses on generation asset allocation between monthly forward contracts, such as bilateral contracts, futures contracts, options contracts, and daily spot markets, considering operating costs and constraints of generating units, as well as spot price risk. The problem is to find the optimal hedging position based on the known forward price and the forecasted hourly spot prices and is formulated based on the model of PJM market. The double dynamic programming method is applied to solve this optimal asset allocation problem with all the short-term operating constraints of the generating unit satisfied. Three types of forward contracts that are commonly used in practice are considered and their impact on generation asset allocation are analyzed and compared. The analytic relationship between the optimal contract quantity of a particular type and spot generation is established. Based on this relationship, the applicability and characteristics of a particular type of contract are discussed. Furthermore, with the solution to the generation asset allocation problem as a basis, the pricing strategy in the forward market is analyzed. A Nash game model is established and an iterative process is developed to determine the equilibrium pricing of futures contracts. Numerical testing shows that the method for the generation asset allocation is effective. Various factors influencing the decision of generation asset allocation and the relationship between futures contract price and spot price are tested and analyzed.