Dataset Of Prices Research Articles

The influx of high-income foreign nationals and the housing market in a developing country: a case study of Suzhou Industrial Park, China

Increasingly globalising human and capital mobility has created new dynamics to the housing market. Foreign nationals, strengthened by inflows of foreign direct investment, have appeared in major Chinese cities. However, little scholarly attention has been paid to the impact of foreign nationals on China’s housing market. By using a case study of Suzhou, this research investigates the following research questions: How are foreign nationals associated with housing price increases in a globalising city? Where and why these impacts have been felt? This research conducted a survey questionnaire with 508 expatriate foreign nationals and built housing price data sets in Suzhou. A consumer theory provides a framework to understand different housing consumption behaviours of heterogeneous socio-economic groups. Findings represent that expatriate foreign nationals played a key role in a high-end housing market due to their higher income levels and housing subsidies provided by their employer. They tended to form ethnic communities in a prime location to better access language-specific services and quality amenity facilities.

How Wave-Wavelet Trading Wins and Beats the Market

The market refers to the S&P 500 index SPY, which is an important benchmark of US stock performance. The following problem is of great interest: is there any trading system that can “beat” the market consistently? Proponents of the random walk hypothesis (RWH) and efficient market hypothesis (EMH) assert that stocks take an unpredictable path, and that it is impossible for a trader to outperform the overall market in the long run because stocks always trade at their fair values on stock exchanges. Therefore, the buy and hold (BH) investor has the best strategy. This paper showcases a website (GeometricWavelet.com) containing an explicit wavelet trading strategy (WT) that “beats” the market consistently, contradicting the above assertion. The data set of SPY historical prices is used to show that WT “beats” the market. This data set is then fitted with a Geometric Brownian motion (GBM). Simulation shows that WT always outperforms the fitted GBM eventually and has larger risk adjusted returns. The numbers generated show that BH’s performance is far inferior to WT’s in the long run.

The Organization of International Trade

This paper discusses how international trade is organized from export to trans-boundary transport to import. All evidence suggests that the transport sector is independent, may exercise market power and features strong economies of scale. We develop a model of a transport industry that operates under imperfect competition and economies of scale and two generic trade models in which export and import activities are either organized at arm’s length or in a vertical partnership. Using a large dataset of maritime transport costs, tariffs and export prices, we test the model predictions and find that economies of scale beat market power: a decline in the tariff implies a decline in freight rates. Furthermore, our results are consistent only with international trade being organized in vertical partnerships because a tariff increase does not lead to a decrease in export prices.

Why 1990 International Geary-Khamis Dollars Cannot Be a Foundation for Reliable Long Run Comparisons of GDP

Using a large, new dataset of agricultural prices and quantities for many countries and regions, we create five new international Geary-Khamis pounds – for 1870, 1845, 1775, 1705, and a superior chained series. We show that estimated levels and changes in output per worker look very different – more extreme – using 1705 international pounds and 1990 international dollars, compared to all other series; growth rates appear substantially higher using 1990 international dollars. In short, out-of-sample baskets and/or prices create extremely unreliable output estimates. We also show that individual country price indices (rather than international indices) can generate substantially different estimated growth rates.

Revisiting Supply and Demand Indexes in Real Estate

A common definition of liquidity in real estate investment is the ability to sell property assets quickly at full value, as reflected by transaction volume. The present paper makes methodological and conceptual contributions in the study and understanding of liquidity. First, we extend the Fisher et al. (2003, 2007) methodology for the separate tracking of changes in reservation prices on the demand (potential buyers) and supply (potential sellers) sides of the asset market. We show how to apply the methodology to a repeat sales indexing framework, allowing application to typical commercial property transaction price datasets, which lack appraisal valuations or complete data regarding property characteristics. We also use a Bayesian, structural time series approach to estimate the indexes. These methodological enhancements enable much more granular supply and demand index estimation, including at the metropolitan level. Second, we propose a Liquidity Metric based on the indexes, and show that the normal liquidity dynamic in commercial property asset markets is “pro-cyclical”, that is, price and trading volume tend to move together, with demand tending to lead supply. But we also observe an “anomalous” dynamic that occurs about 25 percent of the time, in which the Liquidity Metric declines while consummated prices are still rising. This anomalous dynamic is often associated with the end of a period of rapid growth in market values.

The Term Structure of Risk Premia with Heterogeneous Recursive Preferences and Beliefs

I investigate the effect of preference and belief heterogeneity on the term structure of risk premia in a continuous-time time economy with Epstein-Zin-Weil preferences. The slope of the term structure of equity risk premia is driven by heterogeneity in the agents' own prices of risk and the sensitivity of the equity market valuation to the changes in economic conditions. As a result, the slope can switch its sign in response to a significant shock to the aggregate consumption. Significant negative shocks shift the consumption and wealth toward the more agent i.e. the agent with a higher risk aversion or more pessimistic beliefs. As a result, the equity market valuation changes from being pro-cyclical to counter-cyclical, which inverts the term structure. Thus, the model can generate a switch in the sign of the slope of the term structure of the dividend strip risk premia after the 2008-2009 global financial crisis, a result consistent with recent empirical studies and my own calibration based on a proprietary dataset of dividend swap prices.

Model selection for the localized mixture of experts models

ABSTRACTIn this paper, we propose a penalized likelihood method to simultaneous select covariate, and mixing component and obtain parameter estimation in the localized mixture of experts models. We develop an expectation maximization algorithm to solve the proposed penalized likelihood procedure, and introduce a data-driven procedure to select the tuning parameters. Extensive numerical studies are carried out to compare the finite sample performances of our proposed method and other existing methods. Finally, we apply the proposed methodology to analyze the Boston housing price data set and the baseball salaries data set.

Quality pricing-to-market

This paper analyzes firm's pricing-to-market decisions in vertically differentiated industries. We first present a model featuring firms that sell goods of heterogeneous quality levels to consumers who are heterogeneous in their income and thus their marginal willingness to pay for quality increments. We derive closed-form solutions for the unique pricing game under costly international trade. The comparative statics highlight how firms' pricing-to-market decisions are shaped by the interaction of consumer income and good quality. We derive two testable predictions. First, the relative price of high qualities compared to low qualities increases with the income of the destination market. Second, the rate of cost pass-through into consumer prices falls with quality if destination market income is sufficiently high. We present evidence in support of these two predictions based on a dataset of prices, sales, and product attributes in the European car industry.

Model averaging based on rank

ABSTRACTIn this paper, we investigate model selection and model averaging based on rank regression. Under mild conditions, we propose a focused information criterion and a frequentist model averaging estimator for the focused parameters in rank regression model. Compared to the least squares method, the new method is not only highly efficient but also robust. The large sample properties of the proposed procedure are established. The finite sample properties are investigated via extensive Monte Claro simulation study. Finally, we use the Boston Housing Price Dataset to illustrate the use of the proposed rank methods.

Forecasting the Real Price of Oil Under Alternative Specifications of Constant and Time-Varying Volatility

This paper constructs a monthly real-time oil price dataset using backcasting and compares the forecast performance of alternative models of constant and time-varying volatility based on the accuracy of point and density forecasts of real oil prices of both real-time and ex-post revised data. The paper considers Bayesian autoregressive and autoregressive moving average models with respectively, constant volatility and two forms of time-varying volatility: GARCH and stochastic volatility. In addition to the standard time-varying models, more flexible models with volatility in mean and moving average innovations are used to forecast the real price of oil. The results show that time-varying volatility models dominate their counterparts with constant volatility in terms of point forecasting at longer horizons and density forecasting at all horizons. The inclusion of a moving average component provides a substantial improvement in the point and density forecasting performance for both types of time-varying models while stochastic volatility in mean is superfluous for forecasting oil prices.

Note on symmetric utility

This note studies necessary and sufficient conditions for consumer demand data to be generated by a symmetric utility function. We find that a dataset of prices and consumption decisions can be rationalized by a symmetric utility function if and only if the symmetrized dataset satisfies the generalized axiom of revealed preference.

Price level convergence and purchasing power divergence

AbstractWe construct four large data sets of bilateral real exchange rates based on traded good prices (food and clothing, respectively) and broader price indices (consumer price index, CPI, and wholesale price index, WPI). On these data sets, we run non‐parametric regressions to examine how the real exchange rate, the price differential, and the nominal exchange rate react to an overvalued real exchange rate over time. In line with the theory we develop, our regressions show the following: First, real exchange rates are mean‐reverting. Second, prices converge. Third, price convergence implies purchasing power divergence and thus does not contribute to real exchange rate convergence. Indeed, when price adjustment is fast (as for our traded good price data sets), we even observe diverging nominal exchange rates. Our findings suggest that movements both away from and towards PPP may be less related to traded good price movements than is commonly thought.

Immigrants and the City: The Relevance of Immigration on Housing Price Gradient

Foreign citizens are a more and more significant part of the population of Italian cities and society (8% of the country’s total population), and they contribute to changes in the cultural, social, and economic structure of the country. Our aim was to assess the incidence of the immigrant population on urban house price polarization, as measured using an original indicator: the center-periphery housing price gradient. While there is ample literature on the relationship between average prices and immigrant populations, less research has been conducted on immigration and the housing price gradient on a national scale. This price gradient may indicate whether immigration contributes to changing the residential market, also possibly revealing segregation phenomena. We ran multivariate regressions in several steps on an original dataset of housing prices and socio-economic factors concerning 112 Italian provincial capitals to elucidate whether immigration is correlated with the housing market divide. Our main findings confirmed that larger immigrant populations coincide with steeper housing price gradients on a national scale. Our tests also demonstrated that the relevance of this phenomenon varies for different urban forms, confirming related to housing price dynamics between the cities of northern and southern Italy the relevance of urban density in elucidating.

Analysis of electricity prices for Central American countries using dynamic conditional score models

In this paper, we compare the performance of dynamic conditional score (DCS) and standard financial time-series models for Central American energy prices. We extend the Student’s t and the exponential generalised beta distribution of the second kind stochastic location and stochastic seasonal DCS models. We consider the generalised t distribution as an alternative for the error term and also consider dynamic specifications of volatility. We use a unique dataset of spot electricity prices for El Salvador, Guatemala and Panama. We consider two data windows for each country, which are defined with respect to the liberalisation and development process of the energy market in Central America. We study the identification of a wide range of DCS specifications, likelihood-based model performance, time-series components of energy prices, maximum likelihood parameter estimates, the discounting property of conditional score, and out-of-sample forecast performance. Our main results are the following. (i) We determine the most robust models of energy prices, with respect to parameter identification, from a wide range of DCS specifications. (ii) For most of the cases, the in-sample statistical performance of DCS is superior to that of the standard model. (iii) For El Salvador and Panama, the standard model provides better point forecasts than DCS, and for Guatemala the point forecast precision of standard and DCS models does not differ significantly. (iv) For El Salvador, the standard model provides better density forecasts than DCS, and for Guatemala and Panama, the density forecast precision of standard and DCS models does not differ significantly.

THE DAY OF THE WEEK EFFECT IN BORSA ISTANBUL: A GARCH MODEL ANALYSIS

The aim of this study is to investigate the Day of the Week Effect (DWE) in Borsa Istanbul BIST-100 Index. For this purpose, the dataset of closing prices of the firms was gathered from 03.01.2005 to 06.11.2015. The data transformed to return series by taking logarithmic differences, and analyzed with GARCH (1,1) Model. According to the findings, although the coefficients representing the returns of Monday and Thursday are statistically significant, the returns of the trading days of the week are equal. Consequently, for the related period, DWE did not detected in BIST-100 Index. http://dx.doi.org/10.17130/ijmeb.2017331332

Who and what set the price of walnuts for small-scale farmers in Southwest China?

PurposeDomestic demand for walnuts has been on the rise for the last decades. Consumption outstrips domestic production capacities, which led to increasing prices until recently. Small-scale farmers are at the centre of walnut tree planting and walnut collection efforts. Farmers are now integrated into rapidly expanding agrifood value chains. The purpose of this paper is to investigate the walnut value chain originating in Yunnan (the dominant producer of walnuts in China). The authors are especially interested in the position of small-scale farmers in the chain and the factors affecting the price that they receive.Design/methodology/approachPrice and intra-chain governance information were collected through structured interviews with value chain actors like certified and conventional small-scale farmers, traders, processors, food manufacturers and wholesalers. The resultant price data set was analysed using a multiple regression analysis.FindingsTiming of harvest, distance to market and sales volume are correlated with the village-level price. Farmers are in a market governance segment of the chain. Lead firms (e.g. supermarkets) are price-setters and determine the value distribution, with farmers receiving a smaller share relative to downstream actors’ shares.Research limitations/implicationsImproved connectivity to markets, transparency of standards and price (formation), processing and certification could improve farmers’ profits.Originality/valueThe authors contribute to the growing literature of value chain studies focussing on farmers’ integration into food systems at different scales. The authors investigated the price determinants at the village level and additionally provide information on an organic marketing arrangement.

Herding behavior among wine investors

We propose a detailed and comprehensive examination of the two main regression-based techniques used to detect herding among investors. We also introduce a novel approach based on the autocorrelation of returns. We test all models on a unique dataset of wine prices. For the first two models, our conclusions highlight the importance of macroeconomic variables (US equities) on the dispersion of wine returns. Thus, if wine investors herd, it is essentially because of external contingencies and they are not driven by the state of the wine market itself. The third (new) model seems to indicate that there is at most weak evidence of herding and the conclusions are robust when controlling for the state of the US equity market.

Welfare implications of intertemporal marketing margin manipulation

PurposeIn Indonesia, rubber is the most valuable export crop produced by small scale agriculture and plays a key role for inclusive economic development. This potential is likely to be not fully exploited. The observed concentration in the crumb rubber processing industry raises concerns about the distribution of export earnings along the value chain. Asymmetric price transmission (APT) is observed. The paper aims to discuss these issues.Design/methodology/approachThis study investigates the price transmission between international prices and the factories’ purchasing prices on a daily basis. An auto-regressive asymmetric error correction model is estimated to find evidence for APT. In a subsequent step the rents that are redistributed from factories to farmers are calculated. The study then provides estimations of the size of this redistribution under different scenarios.FindingsThe results suggest that factories do indeed transmit prices asymmetrically, which has substantial welfare implications: around USD3 million are annually redistributed from farmers to factories. If the price transmission was only half as asymmetric as it is observed, the majority of this redistribution was re-diverted.Originality/valueThis study combines the approaches of non-parametric and parametric estimation techniques of estimating APT processes with a welfare perspective to quantify the distributional consequences of this intertemporal marketing margin manipulation. Especially the calculation of different scenarios of alternative price transmissions is a novelty. The data set of prices on such a disaggregated level and high frequency as required by this approach is also unique.

Note on Symmetric Utility

This note studies necessary and sufficient conditions for consumer demand data to be generated by a symmetric utility function. We find that a dataset of prices and consumption decisions can be rationalized by a symmetric utility function if and only if the symmetrized dataset satisfies the generalized axiom of revealed preference.

Composite forecasting approach, application for next-day electricity price forecasting

Accurate forecasting of electricity prices can provide significant benefits to energy suppliers when allocating their assets and to energy consumers for defining an optimal portfolio. There are numerous methods that efficiently support the forecasting of time series, such as electricity prices, which have high volatility. However, the performance of these approaches varies depending on data sets and operational conditions. In this work, the concept of composite forecasting is presented and implemented in a retrospective study, in real industrial forecasting conditions to show the potential of forecast performance improvement and comparable high consistency of a forecast performance across different ‘Day Peak’ and ‘Day Base’ electricity price data sets for different seasons. As individual methods support vector regression, artificial neural networks and ridge regression are implemented. The forecast performances of these methods are evaluated and compared with their forecast combination using different error measures. The results show that composite forecasting processes with ‘inverse root mean squared error’ combination approach can generate, on average, a more accurate and robust forecast than using an individual methods or other combination schemas.