Price dynamics, foreign exchange and anticipated shocks in a tourism economy

Using a dynamic model of an open monetary economy, this paper examines the effects of tourism-related anticipated shocks on goods prices and foreign exchange reserves. Foreign tourists consume mainly non-traded goods in holiday destinations, converting them into exportable goods. This gives rise to a tourism terms-of-trade effect that affects the accumulation of foreign exchange. Announcements of anticipated events bring tourist visits forward, resulting in an initial underadjustment or an over-adjustment in the prices of the non-traded goods when the tourism termsof-trade effect is positive or negative. This leads to an increase or a decrease in foreign reserves in the long run.

Dynamic price discrimination with customer recognition

This paper studies a general two-period model of product line pricing with customer recognition. Specifically, we consider a monopolist who can sell vertically differentiated products over two periods to heterogeneous consumers. Each consumer demands one unit of the product in each period. In the second period, the monopolist can condition the price-quality offers on the observed purchasing behavior in the first period. In this setup, the monopolist can price discriminate consumers in two dimensions: by quality as well as by purchase history. We fully characterize the monopolist's optimal pricing strategy when there are two types of consumers. When the type space is a continuum, we show that there is no fully separating equilibrium, and some properties of the optimal contracts (price-quality pairs) are characterized within the class of partitional perfect Bayesian equilibria.

Does oil price volatility matter for Asian emerging economies?

This article investigates the impact of oil price volatility on six major emerging economies in Asia using time-series cross-section and time-series econometric techniques. To assess the robustness of the findings, we further implement such heterogeneous panel data estimation methods as Mean Group (MG), Common Correlated Effects Mean Group (CCEMG) and Augmented Mean Group (AMG) estimators to allow for cross-sectional dependence. The empirical results reveal that oil price volatility has a detrimental effect on these emerging economies. In the short run, oil price volatility influenced output growth in China and affected both GDP growth and inflation in India. In the Philippines, oil price volatility impacted on inflation, but in Indonesia, it impacted on both GDP growth and inflation before and after the Asian financial crisis. In Malaysia, oil price volatility impacted on GDP growth, although there is notably little feedback from the opposite side. For Thailand, oil price volatility influenced output growth prior to the Asian financial crisis, but the impact disappeared after the crisis. It appears that oil subsidization by the Thai Government via introduction of the oil fund played a significant role in improving the economic performance by lessening the adverse effects of oil price volatility on macroeconomic indicators.

A spatial decomposition approach for investigating house price convergences

 Convergence of house prices indicates how prices are reaching an aggregate equilibrium in a long-run perspective. Identifying the convergence is important for cross-region housing development and investment. Few studies have identified house price convergences at different levels, with spatial effects on house prices predominantly ignored. The research presented here developed a spatial panel regression approach to investigate the convergences of house prices in Australian capital cities. Three hypotheses were tested to identify the level of house price convergence. The results demonstrate that a steady state in a system of regional house prices and spatial effects contribute to the convergence continuing.

Has oil price predicted stock returns for over a century?

This paper contributes to the debate on the role of oil prices in predicting stock returns. The novelty of the paper is that it considers monthly time-series historical data that span over 150. years (1859:10-2013:12) and applies a predictive regression model that accommodates three salient features of the data, namely, a persistent and endogenous oil price, and model heteroscedasticity. Three key findings are unraveled: first, oil price predicts US stock returns. Second, in-sample evidence is corroborated by out-sample evidence of predictability. Third, both positive and negative oil price changes are important predictors of US stock returns, with negative changes relatively more important. Our results are robust to the use of different estimators and choice of in-sample periods.

Oil price and stock returns of consumers and producers of crude oil

In this paper we investigate how differently stock returns of oil producers and oil consumers are affected from oil price changes. We find that stock returns of oil producers are affected positively by oil price changes regardless of whether oil price is increasing or decreasing. For oil consumers, oil price changes do not affect all consumer sub-sectors and where it does, this effect is heterogeneous. We find that oil price returns have an asymmetric effect on stock returns for most sub-sectors. We devise simple trading strategies and find that while both consumers and producers of oil can make statistically significant profits, investors in oil producer sectors make relatively more profits than investors in oil consumer sectors.

Electricity load and price forecasting with influential factors in a deregulated power industry

With the emergence of smart power grid and distributed generation technologies in recent years, there is need to introduce new advanced models for forecasting. Electricity load and price forecasts are two primary factors needed in a deregulated power industry. The performances of the demand response programs are likely to be deteriorated in the absence of accurate load and price forecasting. Electricity generation companies, system operators, and consumers are highly reliant on the accuracy of the forecasting models. However, historical prices from the financial market, weekly price/load information, historical loads and day type are some of the explanatory factors that affect the accuracy of the forecasting. In this paper, a neural network (NN) model that considers different influential factors as feedback to the model is presented. This model is implemented with historical data from the ISO New England. It is observed during experiments that price forecasting is more complicated and hence less accurate than the load forecasting.

Psychological oil price barrier and firm returns

In this paper, we investigate the psychological barrier effect induced by the oil price on firm returns when the oil price reaches US$100 or more per barrel. We find evidence of the negative effect of the US$100 oil price barrier for: (a) the entire sample of 1559 firms listed on the American stock exchanges; (b) both foreign and domestic firms, with domestic firms significantly more affected; (c) the 10 different sizes of firms, with the smaller firms less affected compared to the larger firms; and (d) 17 sectors of firms, with firms in the utilities, mining, and administration sectors being the least affected.

A factor analytical approach to price discovery

Existing econometric approaches for studying price discovery presume that the number of markets are small, and their properties become suspect when this restriction is not met. They also require making identifying restrictions and are in many cases not suitable for statistical inference. The current paper takes these shortcomings as a starting point to develop a factor analytical approach that makes use of the cross-sectional variation of the data, yet is very user-friendly in that it does not involve any identifying restrictions or obstacles to inference.

Prediction interval estimation for electricity price and demand using support vector machines

Uncertainty is known to be a concomitant factor of almost all the real world commodities such as oil prices, stock prices, sales and demand of products. As a consequence, forecasting problems are becoming more and more challenging and ridden with uncertainty. Such uncertainties are generally quantified by statistical tools such as prediction intervals (Pis). Pis quantify the uncertainty related to forecasts by estimating the ranges of the targeted quantities. Pis generated by traditional neural network based approaches are limited by high computational burden and impractical assumptions about the distribution of the data. A novel technique for constructing high quality Pis using support vector machines (SVMs) is being proposed in this paper. The proposed technique directly estimates the upper and lower bounds of the PI in a short time and without any assumptions about the data distribution. The SVM parameters are tuned using particle swarm optimization technique by minimization of a modified Pi-based objective function. Electricity price and demand data of the Ontario electricity market is used to validate the performance of the proposed technique. Several case studies for different months indicate the superior performance of the proposed method in terms of high quality PI generation and shorter computational times.