Structural Breaks,Biased Estimations,and Forecast Errors in a GDP Series of Canada versus the United States

International Advances in Economic Research - A structural break was suspected for the Canadian gross domestic product (GDP) time series when the reporting system switched from the Standard...     

Noise, equity prices, and hedging: A new approach

The existence of noise trading in equity markets has possible economic implications for arbitrage, and asset pricing. In terms of pricing, noise trading can lead to excess volatility which has been shown to influence the value of options and futures. Furthermore, option research shows that modeling volatility leads to improved hedging performance. To this end, we derive a general hedging model for equity index futures in the presence of noise trading. Our analysis shows how the level and dynamics of noise trading should influence a hedger's behavior. Finally, we empirically test our model using the NASDAQ-100 index futures and FTSE 100 index futures over the period of January 1998 to May 2003.     

Algebra of Integrated Time Series: Evidence from Unit Root Analysis

It is argued if x_t ~ I(1) and y_t ~ I(1), then running a regression x_t on y_t would produce spurious results because e _t would generally be I(1). However, there may exist a ‘b’ such that e _t  = x _t - by _t is I(0), then running a regression x _t on y _t would not produce spurious results. This special case of two integrated time series is known in the literature as cointegration. In this particular case, x _t and y _t are said to be cointegrated. In our review of the development of the concept of cointegration, we identified that the underlying reason for this special case to arise is the proposition that if x _t  ~ I(d _x ), y _t  ~ I(d _y ), then z _t  = bx _t  + cy _t  ~ I(max(d _x ,d _y )). In this research, we offer evidence against this proposition.     

Optimizing marketer costs and consumer benefits across “clicks” and “bricks”

The Internet has revolutionized the retailing landscape and how goods and services are sold and distributed to consumers. One avenue of significant growth in online selling comes from multichannel retailers who offer products in stores as well as over the Web. These hybrids may leverage their “brick” locations by allowing customers to pick up or return orders purchased online at retail stores. This option lets Web-based buyers avoid added shipping costs and long package carrier lead times, albeit at a cost to retailers. To examine the viability of this strategy, we develop a mathematical model that examines the cost and value of providing in-store pickup and return. The model is used to determine the best subset of brick-and-mortar stores to handle in-store pickup and return demand. One of the principal takeaways is that not all retail stores should be offering in-store pickups and/or returns. Our computational results show optimizing the set of pickup and return locations may reduce system cost over baseline marketing policies where these services are set up at all or none of a retailer’s stores. In addition, we show that retailers can significantly improve some consumer benefits at little extra cost.     

Driven by distraction: investigating the effects of anxiety on driving performance using the Attentional Control Theory

This study investigates the effects of trait anxiety on self-reported driving behaviours through its negative impacts on Central Executive functions. Following a self-report study that found trait anxiety to be significantly related to driving behaviours, the present study extended the predictions of Eysenck and Calvo’s Attentional Control Theory, proposing that anxiety affects driving behaviours, in particular driving lapses, through its impact across the Central Executive. Seventy-five Australian drivers participated in the study, completing the Parametric Go/No-Go and n-back tasks, as well as the State-Trait Anxiety Inventory and the Driving Behaviour Questionnaire. While both trait anxiety and processing efficiency of the Central Executive was found to significantly predict driving lapses, trait anxiety remained a strong predictor of driving lapses after processing efficiency was controlled for. It is concluded that while processing efficiency of the central Executive is a key determinant of driving lapses, another Central Executive function that is closer to the driving lapses in the trait anxiety – driving lapses relationship may be needed. Suggestions regarding how to improve future trait anxiety – driving behaviours research are discussed.