Price discovery and volatility spillovers in index futures markets: Some evidence from Mexico

This paper investigates the hypotheses that the recently established Mexican stock index futures market effectively serves the price discovery function, and that the introduction of futures trading has provoked volatility in the underlying spot market. We test both hypotheses simultaneously with daily data from Mexico in the context of a modified EGARCH model that also incorporates possible cointegration between the futures and spot markets. The evidence supports both hypotheses, suggesting that the futures market in Mexico is a useful price discovery vehicle, although futures trading has also been a source of instability for the spot market. Several managerial implications are derived and discussed. (C) 2004 Elsevier B.V. All rights reserved.

Relative accuracy and predictive ability of direct valuation methods, price to aggregate earnings method and a hybrid approach

In this paper, we assess the relative performance of the direct valuation method and industry multiplier models using 41 435 firm-quarter Value Line observations over an 11 year (1990–2000) period. Results from both pricingerror and return-prediction analyses indicate that direct valuation yields lower percentage pricing errors and greater return prediction ability than the forward price to aggregated forecasted earnings multiplier model. However, a simple hybrid combination of these two methods leads to more accurate intrinsic value estimates, compared to either method used in isolation. It would appear that fundamental analysis could benefit from using one approach as a check on the other.

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes. which may be (ens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far. most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be, predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply-demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour. electricity), demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.

The Function and Consequences of Alternative Fresh Produce Supply Chain Models

Around the world, consumers and retailers of fresh produce are becoming more and more discerning about factors such as food safety and traceability, health, convenience and the sustainability of production systems, and in doing so they are changing the way in which fresh produce supply chains are configured and managed. When consumers demand fresh, safe, convenient, value-for-money produce, retailers in an increasingly competitive environment are attracted to those business models most capable of meeting these demands profitably. Traditional models are proving less and less able to deliver competitive advantage in such an environment. As a result, opportunistic, adversarial, price-based approaches to doing business between chain members are being replaced by approaches that are more strategic, collaborative and value-based. The shaping force behind this change is the need for producers, wholesalers, category managers, retailers and consumers to have more certainty about the performance of the supply chains upon which they rely. Certainty is generated through the supply chain's ability to create, deliver and share value. How to build supply chains that create, deliver and share value is arguably the single biggest challenge to the competitiveness of fresh produce firms, and therefore to the industries to which they belong.