Russia

Ruta Aidis, Julia Korosteleva and Tomasz Mickiewicz 1. Introduction to Russia Russia is the world’s largest country, a nuclear superpower with unsurpassed energy resources. It is also a country which finds itself at the crossroads of possible development paths. Market-oriented mechanisms have been introduced but Soviet era laws remain on the books. Corruption has become a way of life and freedom of the press has been gradually eliminated in the early 2000s. Within this backdrop, private entrepreneurship has emerged, albeit in a distorted way. As the heart of the Soviet empire, Russia had tremendous control of enormous amounts of natural resources and human capital. Yet, 20 years ago, in the late 1980s, it was a country where entrepreneurship was marginal, the economy was stagnant and the ruling communist hierarchy had no clear formula for solving the deepening crisis. Unfortunately the reforms characterizing Russia’s attempts at rebuilding statehood after the collapse of the Soviet Union in the 1990s, first under Mikhail Gorbachev and then Boris Y’eltsin, were inconsistent and did not foster macroeconomic stabilization. However, since 2000, under Vladimir Putin’s leadership, macroeconomic stabilization as well as institutional stability has been achieved. In addition, an unprecedented increase in the price and demand for oil and gas resources has resulted in a rapid growth of Russia’s gross domestic product (GDP). Russia now has a large private sector, though not without its limitations. At first glance, ‘de jure’ regulations often seem reasonable, yet it is the selective and arbitrary manner by which they are...

Techniques to improve forecasting models: applications to energy demand and price

This thesis is a study of three techniques to improve performance of some standard fore-casting models, application to the energy demand and prices. We focus on forecasting demand and price one-day ahead. First, the wavelet transform was used as a pre-processing procedure with two approaches: multicomponent-forecasts and direct-forecasts. We have empirically compared these approaches and found that the former consistently outperformed the latter. Second, adaptive models were introduced to continuously update model parameters in the testing period by combining ?lters with standard forecasting methods. Among these adaptive models, the adaptive LR-GARCH model was proposed for the fi?rst time in the thesis. Third, with regard to noise distributions of the dependent variables in the forecasting models, we used either Gaussian or Student-t distributions. This thesis proposed a novel algorithm to infer parameters of Student-t noise models. The method is an extension of earlier work for models that are linear in parameters to the non-linear multilayer perceptron. Therefore, the proposed method broadens the range of models that can use a Student-t noise distribution. Because these techniques cannot stand alone, they must be combined with prediction models to improve their performance. We combined these techniques with some standard forecasting models: multilayer perceptron, radial basis functions, linear regression, and linear regression with GARCH. These techniques and forecasting models were applied to two datasets from the UK energy markets: daily electricity demand (which is stationary) and gas forward prices (non-stationary). The results showed that these techniques provided good improvement to prediction performance.

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

A comparison of sales response predictions from demand models applied to store-level versus panel data

In order to generate sales promotion response predictions, marketing analysts estimate demand models using either disaggregated (consumer-level) or aggregated (store-level) scanner data. Comparison of predictions from these demand models is complicated by the fact that models may accommodate different forms of consumer heterogeneity depending on the level of data aggregation. This study shows via simulation that demand models with various heterogeneity specifications do not produce more accurate sales response predictions than a homogeneous demand model applied to store-level data, with one major exception: a random coefficients model designed to capture within-store heterogeneity using store-level data produced significantly more accurate sales response predictions (as well as better fit) compared to other model specifications. An empirical application to the paper towel product category adds additional insights. This article has supplementary material online.

Cross-category demand effects of price promotions

Do promotions in a certain category lead to higher revenues in other categories? If so, to what degree? The answers to these questions are highly relevant for retailers that supply products in different categories. Empirical findings in studies that consider a limited number of categories indicate small promotional cross-category effects. This study develops a framework to determine the impact of price promotions on category revenues that include interdependencies among a substantial number of categories at the category demand level. The own- and cross-category demand effects are moderated by variables such as promotion intensity, category characteristics (own-category effects), and spatial distances between shelf locations (cross-category effects). The empirical results based on daily store-level scanner data show that approximately half of all price promotions expand own-category revenues, especially for categories with deeper supported discounts. There is a high probability (61%) that a price promotion affects sales of at least one other category. The number of categories affected is not greater than two. Moderate evidence supports the existence of cross-promotional effects between categories more closely located in a store.

An incentivized auction based group-selling approach for demand response management in V2G systems

Vehicle-to-Grid (V2G) system with efficient Demand Response Management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at EVs. An incentivilized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling based auction. In the proposed approach, a number of aggregators sit on the first level auction responsible to communicate with a group of EVs. EVs as bidders consider Quality of Energy (QoE) requirements and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation.

Productivity and labour demand effects of inward and outward foreign direct investment on UK industry

We relate the technological and factor price determinants of inward and outward foreign direct investment (FDI) to its potential productivity and labour market effects on both host and home economies. This allows us to distinguish clearly between technology-sourcing and technologyexploiting FDI, and to identify FDI that is linked to labour cost differentials. We then empirically examine the effects of different types of FDI into and out of the UK on domestic (i.e. UK) productivity and on the demand for skilled and unskilled labour at the industry level.

Some further empirical evidence on the impact of oil price changes on petrol prices

This paper analyses the asymmetries in the response of petrol prices to oil price shocks. We show that previous work, based on the determination of asymmetric responses, can be improved upon by allowing for asymmetries in short term dynamics. The paper shows that a significant determinant of the response of petrol prices to oil price changes, is the extent to which petrol price can be seen to have departed from its long run equilibrium level.

Productivity and labour demand effects of inward and outward FDI on UK industry

We relate the technological and factor price determinants of inward and outward FDI to its potential productivity and labour market effects on both host and home economies. This allows us to distinguish clearly between technology sourcing and technology exploiting FDI, and to identify FDI which is linked to labour cost differentials. We then empirically examine the effects of different types of FDI into and out of the United Kingdom on domestic (i.e. UK) productivity and on the demand for skilled and unskilled labour at the industry level. Inward investment into the UK comes overwhelmingly from sectors and countries which have a technological advantage over the corresponding UK sector. Outward FDI shows a quite different pattern, dominated by investment into foreign sectors which have lower unit labour costs than the UK. We find that different types of FDI have markedly different productivity and labour demand effects, which may in part explain the lack of consensus in the empirical literature on the effects of FDI. Our results also highlight the difficulty for policy makers of simultaneously improving employment and domestic productivity through FDI.

Stock price distributions and news:evidence from index options

We estimate the shape of the distribution of stock prices using data from options on the underlying asset, and test whether this distribution is distorted in a systematic manner each time a particular news event occurs. In particular we look at the response of the FTSE100 index to market wide announcements of key macroeconomic indicators and policy variables. We show that the whole distribution of stock prices can be distorted on an event day. The shift in distributional shape happens whether the event is characterized as an announcement occurrence or as a measured surprise. We find that larger surprises have proportionately greater impact, and that higher moments are more sensitive to events however characterised.

Measuring the Big Five with single items using a bipolar response scale

The convergence on the Big Five in personality theory has produced a demand for efficient yet psychometrically sound measures. Therefore, five single-item measures, using bipolar response scales, were constructed to measure the Big Five and evaluated in terms of their convergent and off-diagonal divergent properties, their pattern of criterion correlations and their reliability when compared with four longer Big Five measures. In a combined sample (N?=?791) the Single-Item Measures of Personality (SIMP) demonstrated a mean convergence of r?=?0.61 with the longer scales. The SIMP also demonstrated acceptable reliability, self–other accuracy, and divergent correlations, and a closely similar pattern of criterion correlations when compared with the longer scales. It is concluded that the SIMP offer a reasonable alternative to longer scales, balancing the demands of brevity versus reliability and validity.

An econometric analysis of international tourism demand in Hong Kong : a cointegration and error correction approach

The purpose of this study is to develop econometric models to better understand the economic factors affecting inbound tourist flows from each of six origin countries that contribute to Hong Kong’s international tourism demand. To this end, we test alternative cointegration and error correction approaches to examine the economic determinants of tourist flows to Hong Kong, and to produce accurate econometric forecasts of inbound tourism demand. Our empirical findings show that permanent income is the most significant determinant of tourism demand in all models. The variables of own price, weighted substitute prices, trade volume, the share price index (as an indicator of changes in wealth in origin countries), and a dummy variable representing the Beijing incident (1989) are also found to be important determinants for some origin countries. The average long-run income and own price elasticity was measured at 2.66 and – 1.02, respectively. It was hypothesised that permanent income is a better explanatory variable of long-haul tourism demand than current income. A novel approach (grid search process) has been used to empirically derive the weights to be attached to the lagged income variable for estimating permanent income. The results indicate that permanent income, estimated with empirically determined relatively small weighting factors, was capable of producing better results than the current income variable in explaining long-haul tourism demand. This finding suggests that the use of current income in previous empirical tourism demand studies may have produced inaccurate results. The share price index, as a measure of wealth, was also found to be significant in two models. Studies of tourism demand rarely include wealth as an explanatory forecasting long-haul tourism demand. However, finding a satisfactory proxy for wealth common to different countries is problematic. This study indicates with the ECM (Error Correction Models) based on the Engle-Granger (1987) approach produce more accurate forecasts than ECM based on Pesaran and Shin (1998) and Johansen (1988, 1991, 1995) approaches for all of the long-haul markets and Japan. Overall, ECM produce better forecasts than the OLS, ARIMA and NAÏVE models, indicating the superiority of the application of a cointegration approach for tourism demand forecasting. The results show that permanent income is the most important explanatory variable for tourism demand from all countries but there are substantial variations between countries with the long-run elasticity ranging between 1.1 for the U.S. and 5.3 for U.K. Price is the next most important variable with the long-run elasticities ranging between -0.8 for Japan and -1.3 for Germany and short-run elasticities ranging between – 0.14 for Germany and -0.7 for Taiwan. The fastest growing market is Mainland China. The findings have implications for policies and strategies on investment, marketing promotion and pricing.

Ethnic groups as market segments: implications of the demand structure

Ethnic market potential in Britain has not yet been thoroughly researched. Important recent trends have focused mainly on the affective and emotional aspects of ethnicity, and included deliberations on the emergence of a revitalised neo-ethnic consciousness; its identification; politicisation, and the impact on it; of a rising third-world consciousness. This investigation attempts to take cognizance of the consuner demand of the ethnic Asian and West Indian groups, as specific market segments. It discusses the rationale for ethnic segmentation on the underlying premise, that the starting point for all product marketing is a response to perceived market opportunities. On the basis of this approach, the UK laundry detergent and automobile markets were investigated; as being representative of product categories constitutirg extremes along the purchase-search-time continuun in consuner decision-making. Ethnic groups were further analysed for their retail patronage patterns; media usage, and the differential effectiveness of alternative advertisirg strategies. The basic technique of marketing research namely the sample survey, was used with the aim of applying scientific techniques in obtaining information on ethnic groups. The integrated marketirg framework utilised allowed, moreover, for the collection of market research data on the specific issues of ethnic product penetration dealing with retailing, advertising and product promotion. The evidence highlights the fact that the cultural orientations of ethnic groups are instrunental in providing for differential demand structures. It points to the answer that ethnicity is an anchor not only for a deeper sense of identity; but also serves as a focus for the economic interests of ethnic groups. On this basis it is argued here, that since cultural levelling would eventually produce stagnation; current marketing strategies should utilise ethnic diversity as an econanic artifact; which; per se is necessary for profitability and growth; especially in innovative product design and development.

Demand side management within a regional electricity company

The work presented in this thesis concerns itself with the application of Demand Side Management (DSM) by industrial subsector as applied to the UK electricity industry. A review of the origins of DSM in the US and the relevance of experience gained to the UK electricity industry is made. Reviews are also made of the current status of the UK electricity industry, the regulatory system, and the potential role of DSM within the prevalent industry environment. A financial appraisal of DSM in respect of the distribution business of a Regional Electricity Company (REC) is also made. This financial appraisal highlights the economic viability of DSM within the context of the current UK electricity industry. The background of the work presented above is then followed by the construction of a framework detailing the necessary requirements for expanding the commercial role of DSM to encompass benefits for the supply business of a REC. The derived framework is then applied, in part, to the UK ceramics manufacturing industry, and in full to the UK sanitaryware manufacturing industry. The application of the framework to the UK sanitaryware manufacturing industry has required the undertaking of a unique first-order energy audit of every such manufacturing site in the UK. As such the audit has revealed previously unknown data on the timings and magnitude of electricity demand and consumption attributable to end-use manufacturing technologies and processes. The audit also served to reveal the disparity in the attitudes toward energy services, and thus by implication towards DSM, of manufacturers within the same Standard Industrial Classification (SIC) code. In response to this, attempt is made to identify the underlying drivers which could cause this variation in attitude. A novel approach to the market segmentation of the companies within the UK ceramics manufacturing sector has been utilised to classify these companies in terms of their likelihood to participate in DSM programmes through the derived Energy Services approach. The market segmentation technique, although requiring further development to progress from a research based concept, highlights the necessity to look beyond the purely energy based needs of manufacturing industries when considering the utilisation of the Energy Services approach to facilitate DSM programs.

Price discovery for Chinese shares cross-listed in multiple markets

In this article we study the relationship between security returns cross-listed on the A share market of China and the H share market at the Stock Exchange of Hong Kong (SEHK). Most of these securities are also cross-listed on other markets. An important feature of this article is that we focus on the multilateral relationships between all cross-listed markets rather than concentrating only on the bi-lateral relationship between A and Hong Kong H shares. Using the impulse response functions and the variance decompositions from a Vector Autoregressive (VAR) process we show that the returns to the A share market are almost exclusively determined by domestic factors. In contrast, we find that the H share market is influenced by both the A share market within China and foreign stock markets elsewhere in the world. Impulse response functions suggest that innovations to the A share market and the Hong Kong H share market are partly transmitted to each other and to stock markets outside China. We show that liquidity has an important role to play in determining the impact that the home market has on cross-listed variance decompositions. © 2012 Copyright Taylor and Francis Group, LLC.