Investor sentiment and energy futures predictability: Evidence from Feasible Quasi Generalized Least Squares

Contributing to the budding literature on how emotional and sentimental actions impact the performance of financial markets, this study examines the predictability of energy futures prices with investors’ sentiments. In particular, we examine which of the three (neutral, bear and bull) investors’ sentiments offer accurate forecast information on four energy futures prices. Using the predictability test proposed by Westerlund and Narayan (2015), we discover that all the forms of investors’ sentiments are significant predictors of the movements in energy futures prices. However, the bear sentiments outshine other variants in the forecast of crude oil futures prices, while the bull sentiments provide the most accurate forecast information for the remaining energy futures prices, namely heating oil, gasoline and natural gas. We also find this evidence consistent even when asymmetries are considered in the predictability models. Among other implications of these findings, investors in energy futures and portfolio managers are expected to consider often emotional perceptions in their portfolio constructions and the predictability of future gains.     

城市空间结构理论——单中心城市静态模型

首先介绍了在西方城市经济理论中被广泛接受的单中心城市空间结构静态模型。然后,利用比较静态分析方法(comparativestaticanalysis),分析区位、居民收入及交通费用(区位,居民收入及交通费用为模型的外生变量(exogenousvariable))对价格和房屋消费量(价格和房屋消费量为模型的内生变量(endogenousvariable))的影响。最后,理论模型通过引进一般住房生产函数,推导出土地价格及资本密度(即建筑高度)的空间变化规律。通过对城市经济模型进一步分析(对极值条件或解的微分解析分析)得出地价(地租),资本密度(或称容积率),人口密度的空间分布规律,这些规律对城市规划,城市政策,引进市场原则和价格机制来提高城市土地利用效率都有理论指导意义。     

Forecasting electricity prices with expert,linear, and nonlinear models

This paper compares several models for forecasting regional hourly day-ahead electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources, fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA-GARCH models—hence including parsimonious autoregressive specifications (known as expert-type models). The results support the adoption of a simple structure that is able to adapt to market conditions. Indeed, we include forecasted demand, wind and solar power, actual generation from hydro, biomass, and waste, weighted imports, and traditional fossil fuels. The inclusion of these exogenous regressors, in both the conditional mean and variance equations, outperforms in point and, especially, in density forecasting when the superior set of models is considered. Indeed, using the model confidence set and considering northern Italian prices, predictions indicate the strong predictive power of regressors, in particular in an expert model augmented for GARCH-type time-varying volatility. Finally, we find that using professional and more timely predictions of consumption and renewable energy sources improves the forecast accuracy of electricity prices more than using predictions publicly available to researchers.     

Estimation of Asymmetric Stochastic Volatility Models: Application to Daily Average Prices of Energy Products

Energy supply and demand, and as a consequence energy prices, are likely to represent one of the biggest challenges of the 21st century. Commodity markets exhibit increased volatility when there is little or no underutilized supply capability to meet natural fluctuations in demand. In the case of energy markets, the large capital requirements and significant lead times associated with energy production and delivery make them more susceptible to the imbalances in supply capability and demand. Energy price volatility has destructive impact on market agents, and this impact is intensified when the prices exhibit asymmetric volatility. This article pursues two aspects of the issue. First we consider general aspects, especially the asymmetric pattern of volatility of daily returns of different types of energy products. Then, we analyze the behaviour of daily returns by using traditional models of volatility that include AGARCH, TGARCH, EGARCH, and ARSV strategies, as well as a threshold asymmetric autoregressive stochastic volatility (TA-ARSV) model that we propose. The energy products considered in this analysis are probably the most relevant energy products for the economic activity of the nations and the economic relations between countries: Crude Oil (OPEC reference basket and London Brent index), Gasoline, Natural Gas, Butane, and Propane. We use spot prices and the time reference ranges from 1986–1993 to 2009 depending on the product.     

中国一线城市房地产市场辐射效应研究

近年来,我国城市房地产价格呈现出一线城市房价迅速攀升与全国城市房价普遍上涨的总体格局。本文运用空间变异VAR误差修正模型,实证分析一线城市对中西部城市房屋价格是否存在一定的辐射效应,同时厘清这种影响是正向的推动作用还是此消彼长的辐射影响,进而探讨如何在有效平抑一线城市过快上涨的房价,同时避免引起中西部城市房价可能出现的某种程度的上升,为促进房地产市场稳定健康发展提出相关政策建议。     

Improving Electricity Market Price Forecasting with Factor Models for the Optimal Generation Bid

In liberalized electricity markets, the electricity generation companies usually manage their production by developing hourly bids that are sent to the day‐ahead market. As the prices at which the energy will be purchased are unknown until the end of the bidding process, forecasting of spot prices has become an essential element in electricity management strategies. In this article, we apply forecasting factor models to the market framework in Spain and Portugal and study their performance. Although their goodness of fit is similar to that of autoregressive integrated moving average models, they are easier to implement. The second part of the paper uses the spot‐price forecasting model to generate inputs for a stochastic programming model, which is then used to determine the company's optimal generation bid. The resulting optimal bidding curves are presented and analyzed in the context of the Iberian day‐ahead electricity market.     

Do interest rate options contain information about excess returns?

There is strong empirical evidence that long-term interest rates contain a time-varying risk premium. Options may contain valuable information about this risk premium because their prices are sensitive to the underlying interest rates. We use the joint time series of swap rates and interest rate option prices to estimate dynamic term structure models. The risk premiums that we estimate using option prices are better able to predict excess returns for long-term swaps over short-term swaps. Moreover, in contrast to the previous literature, the most successful models for predicting excess returns have risk factors with stochastic volatility. We also show that the stochastic volatility models we estimate using option prices match the failure of the expectations hypothesis.     

Forecasting crude oil prices with DSGE models

In this study, we conducted an oil prices forecasting competition among a set of structural models, including vector autoregression and dynamic stochastic general equilibrium (DSGE) models. Our results highlight two principles. First, forecasts should exploit the fact that real oil prices are mean reverting over long horizons. Second, models should not replicate the high volatility of the oil prices observed in samples. By following these principles, we show that an oil sector DSGE model performs much better at real oil price forecasting than random walk or vector autoregression.     

Household location in a linear multi-center metropolitan area

The application of monocentric models of residential location to the analysis of metropolitan areas with more than one center of economic activity, produces a distorted view of the spatial distribution of urban variables such as land values, housing prices, etc. This distortion results from the fact that monocentric models tend to underestimate the values of these variables in areas lying between the centers, and yield wider residential areas toward the outskirts of the city. In this paper, a model of household location is developed, which attempts to correct this distortion by simultaneously considering the urban centers during the residential location process.     

Comparative empirical study of binomial call-option pricing methods using S&P 500 index data

In this paper, several binomial models are tested empirically on S&P500 Index on the levels of tradability, proximity to market (RMS) prices and profitability, especially close to expiration day. These comparisons will be carried out for many different business environments, including different market trends and moneyness levels traded. Among the models under analysis we assess the quality of the SH model, developed by the authors in previous work, in relation to other models. The option price in the SH model is affected by the players’ assessments about the behavior of the prices of the underlying asset up to the expiration day and by their “eagerness” levels (i.e., players’ readiness to respond to a given bid proposed by their opponent). We found that for all models, the higher the moneyness, the greater the proximity of models prices to actual market prices and that, eagerness parameters have a decisive effect on tradability. We also found that there was no correlation between the degree of proximity of modeled prices to actual prices and the expected profit gained by players that act according to a given model and that the SH model traded relatively small number of options. The expected profit is highest for the SH model in the ITM and ATM for days that are far from the expiration day.     

中国城市软实力对住房价格的影响——基于主成分分析法和面板数据模型

在采用主成分分析法合成软实力综合指标构建了两阶段回归模型,运用面板数据GMM估计方法就软实力对房价的影响进行估计后发现,中国35个大中城市的软实力对城市房价均具有显著的正向作用,且这种影响从东部到西部呈现出逐渐减弱的态势。也就是说,在全国范围内,城市软实力对房价均具有显著的正向影响,且不同地区的城市软实力对房价的作用程度有所差别。由此可见,城市的人文社会因素、生态环境质量、交通区位条件对城市商品房价格的影响较为显著,对城市软实力的日益重视决定着中国城市人口的居住选择。     

A simple framework for analysing bull and bear markets

Bull and bear markets are a common way of describing cycles in equity prices. To fully describe such cycles one would need to know the data generating process (DGP) for equity prices. We begin with a definition of bull and bear markets and use an algorithm based on it to sort a given time series of equity prices into periods that can be designated as bull and bear markets. The rule to do this is then studied analytically and it is shown that bull and bear market characteristics depend upon the DGP for capital gains. By simulation methods we examine a number of DGPs that are known to fit the data quite well—random walks, GARCH models, and models with duration dependence. We find that a pure random walk provides as good an explanation of bull and bear markets as the more complex statistical models. In the final section of the paper we look at some asset pricing models that appear in the literature from the viewpoint of their success in producing bull and bear markets which resemble those in the data. Copyright © 2002 John Wiley & Sons, Ltd.     

School Quality and Real House Prices: Inter- and Intrametropolitan Effects

This study focuses on explaining variations in real constant-quality house prices in jurisdictions located in multiple MSAs. Using a hedonic house price framework, we test competing theories of house price determination. Using two variants of the random coefficients model, we find that public school quality has a very large impact on real constant-quality house prices. Our results suggest that capitalization of school quality differences occurs on a per lot basis rather than per square foot of land. Also important to the explanation of variations in house prices are variables derived from urban theory, such as distance to the CBD, and from the amenity literature, such as a community's crime rate, arts, and recreational opportunities     

Urban economic base as a catalyst for movements in real estate prices

We examine the relation between housing prices in an MSA and its urban economic base. We create and employ new forward-looking employment growth indices that measure the urban economic strength of an MSA and find that it accounts for a significant and sizeable portion of the house price movements in that MSA. We argue that the forward looking measure is an indicator of future agglomeration growth for the MSAs. We further partition the urban economic growth into growth by various industries and track the attribution of their growth to housing prices over time. We find that for some MSAs, home prices are driven by the same set of industries over time, whereas for others, home prices are driven by a totally new set of industries in the later quarters, due to the birth of new industries. We also analyze the impact of the diversification of an urban economic base on home price volatility and observe that diversification decreases home price volatility. The result has larger implications for urban policymakers in selecting the appropriate type of industries relative to their existing mix of industries.     

The elasticity of substitution in urban housing production: A VES approach

Many of the models which have been developed to explain urban spatial structure and land-use patterns rest on the properties of production functions. Differing factor price ratios within urban areas, particularly land prices, result in capital-land ratios exemplified by high-rise apartments and single-family dwellings. The purpose of this paper is to explore a new functional form for the housing production function. Specifically, a variable elasticity of substitution production function is proposed and some preliminary empirical evidence is provided using data for single-family housing.     

A POST-KEYNESIAN AGE MODEL TO FORECAST ENERGY DEMAND IN SPAIN

This paper develops an extended input–output model for the estimation of energy demand and related issues. It is built on the last Spanish Symmetric Input–Output Table (IOT, 2005). It has been tested for the period 2005–2008 and used for forecasting energy demand for the years 2009–2012 under different economic scenarios. The model shares some traits of the computable and applied general equilibrium models where quantity and price systems are interwoven. The differences lie in the theories explaining output and prices. Our quantity system is based on Keynes’ principle of effective demand (broad energy multipliers are derived). The price system is based on the classical (Sraffian) theory of prices of production, akin to post-Keynesian full-cost prices. The general price system can be manipulated to account for the specificities of energy prices. Historical trends of energy coefficients are computed by extrapolation of past IOTs and calibration.     

Contradictions of the Climate-Friendly City: New Perspectives on Eco-Gentrification and Housing Justice

As local governments and corporations promote ‘climate friendliness’, and a low-carbon lifestyle becomes increasingly desirable, more middle- and upper-income urban residents are choosing to live near public transit, on bike- and pedestrian-friendly streets, and in higher-density mixed-use areas. This rejection of classical forms of suburbanization has, in part, increased property values in neighborhoods offering these amenities, displacing lower-income, often non-white, residents. Increased prevalence of creative and technology workers appears to accelerate this trend. We argue that a significant and understudied socio-environmental contradiction also occurs where the actual environmental outcomes of neighborhood transformation may not be what we expect. New research on greenhouse gas emissions shows that more affluent residents have much larger carbon footprints because of their consumption, even when reductions in transportation or building energy emissions are included. We describe an area in Seattle, Washington, the location of Amazon's headquarters, experiencing this contradiction and show a distinct convergence of city investments in low-carbon infrastructure, significant rises in housing prices and decreases in lower-income and non-white residents. We conclude with a discussion of a range of issues that require more attention by scholars interested in housing justice and/or urban sustainability.     

Forecasting electricity prices using bid data

Market liberalization and the expansion of variable renewable energy sources in power systems have made the dynamics of electricity prices more uncertain, leading them to show high volatility with sudden, unexpected price spikes. Thus, developing more accurate price modeling and forecasting techniques is a challenge for all market participants and regulatory authorities. This paper proposes a forecasting approach based on using auction data to fit supply and demand electricity curves. More specifically, we fit linear (LinX-Model) and logistic (LogX-Model) curves to historical sale and purchase bidding data from the Iberian electricity market to estimate structural parameters from 2015 to 2019. Then we use time series models on structural parameters to predict day-ahead prices. Our results provide a solid framework for forecasting electricity prices by capturing the structural characteristics of markets.     

Valuation of options on the maximum of two prices with default risk under GARCH models

In this paper, we work under GARCH models to value options on the maximum or the minimum of two prices. In addition, we consider not only two underlying asset prices but also geometric average ones. Further, default risk is also incorporated in a reduced-form model. In the proposed framework, closed-form pricing formulae of options on the maximum with or without default risk are derived and then used to perform numerical examples.     

The value of scarce water: Measuring the inefficiency of municipal regulations

Rather than allowing urban water prices to reflect scarcity rents during periods of drought-induced excess demand, policy makers have mandated command-and-control approaches, primarily rationing the use of water outdoors. While such policies are ubiquitous and likely inefficient, economists have not had access to sufficient data to estimate their economic impact. Using unique panel data on residential end-uses of water in 11 North American cities, we examine the welfare implications of urban water rationing in response to drought. Using estimates of expected marginal prices that vary both across and within markets, we estimate price elasticities specific to indoor and outdoor water use. Our results suggest that current policies do target water uses that households, themselves, are most willing to forgo. Nevertheless, we find that rationing outdoor water in cities has costly welfare implications, primarily due to household heterogeneity in willingness-to-pay for scarce water. We find that replacing rationing policies with a market-clearing “drought price” would result in welfare gains of more than 29% of what households in the sample spend each year on water.