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.     

Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?

Recent electricity price forecasting studies have shown that decomposing a series of spot prices into a long-term trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate point predictions than an approach in which the same regression or neural network model is calibrated to the prices themselves. Here, considering two novel extensions of this concept to probabilistic forecasting, we find that (i) efficiently calibrated non-linear autoregressive with exogenous variables (NARX) networks can outperform their autoregressive counterparts, even without combining forecasts from many runs, and that (ii) in terms of accuracy it is better to construct probabilistic forecasts directly from point predictions. However, if speed is a critical issue, running quantile regression on combined point forecasts (i.e., committee machines) may be an option worth considering. Finally, we confirm an earlier observation that averaging probabilities outperforms averaging quantiles when combining predictive distributions in electricity price forecasting.     

Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO

We use a unique set of prices from the German EPEX market and take a closer look at the fine structure of intraday markets forelectricity, with their continuous trading for individual load periods up to 30 min before delivery. We apply the least absolute shrinkage and selection operator (LASSO) in order to gain statistically sound insights on variable selection and provide recommendations for very short-term electricity price forecasting.     

考虑市场力的短期边际电价预测

电价波动较负荷波动剧烈,使得整个电价的预测精度降低。造成这种价格波动的主要原因是由于在电力市场中,发电商拥有的市场力具有能够支配电价上下波动的能力,使得电价的变化更加难以预测。因此市场力在电价预测中是必须考虑的重要因素之一。提出将市场供需比指标作为电价预测的一个输入量,将其引入到预测模型中作为影响电价的因素,使预测精度得到提高。     

Predicting housing prices in China based on modified Holt's exponential smoothing incorporating whale optimization algorithm

The forecast of the real estate market is an important part of studying the Chinese economic market. Most existing methods have strict requirements on input variables and are complex in parameter estimation. To obtain better prediction results, a modified Holt's exponential smoothing (MHES) method was proposed to predict the housing price by using historical data. Unlike the traditional exponential smoothing models, MHES sets different weights on historical data and the smoothing parameters depend on the sample size. Meanwhile, the proposed MHES incorporates the whale optimization algorithm (WOA) to obtain the optimal parameters. Housing price data from Kunming, Changchun, Xuzhou and Handan were used to test the performance of the model. The housing prices results of four cities indicate that the proposed method has a smaller prediction error and shorter computation time than that of other traditional models. Therefore, WOA-MHES can be applied efficiently to housing price forecasting and can be a reliable tool for market investors and policy makers.     

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.     

煤炭价格对燃煤发电项目内部收益率影响关系的研究

煤炭价格对燃煤发电项目经济效益具有决定性影响,煤价与电价的相对水平决定了煤炭行业、电力行业、政府间的利益分配。电力行业的市场化进度相对落后于其上游的煤炭产业,煤炭价格波动对电力企业的运营构成较大的成本风险。内部收益率是燃煤电厂经济效益的关键指标,文章以内部收益率作为研究对象,分析煤炭价格变动对内部收益率的影响,运用回归方法拟合煤炭价格对内部收益率的影响曲线,进一步通过数值差分法得到煤炭价格对内部收益率的微分曲线即边际影响曲线,并利用双曲函数拟合边际影响曲线。研究结果表明,在较低价格区间内,煤炭价格变化对内部收益率的边际影响度较小;在较高价格范围内,边际影响度以双曲函数的形式快速下降。     

A Smooth Transition Logit Model of The Effects of Deregulation in the Electricity Market

This paper introduces the smooth transition logit (STL) model that is designed to detect and model situations in which there is structural change in the behaviour underlying the latent index from which the binary dependent variable is constructed. The maximum likelihood estimators of the parameters of the model are derived along with their asymptotic properties, together with a Lagrange multiplier test of the null hypothesis of linearity in the underlying latent index. The development of the STL model is motivated by the desire to assess the impact of deregulation in the Queensland electricity market and ascertain whether increased competition has resulted in significant changes in the behaviour of the spot price of electricity, specifically with respect to the occurrence of periodic abnormally high prices. The model allows the timing of any change to be endogenously determined and also market participants' behaviour to change gradually over time. The main results provide clear evidence in support of a structural change in the nature of price events, and the endogenously determined timing of the change is consistent with the process of deregulation in Queensland. Copyright © 2015 John Wiley & Sons, Ltd.     

Adding dummy variables: A simple approach for improved volatility forecasting in electricity market

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity volatility based on existing HAR models. The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon, whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily, weekly, and monthly horizons. The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility, and in most cases, dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects. The out-of-sample results were robust across three different methods. More importantly, we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.     

Identifying the true nature of price discovery and cross-market informational flow in the investment grade CDS and equity markets

We examine the comparative efficiency of systematic investment grade credit default swap (CDS) and equity markets using a time-varying coefficient vector autoregression. This modeling framework enables a view of cross-market informational flow along each point in the time-period under investigation by taking into account parameter instability. We obtain smoothing estimates of parameters capturing such flow between CDS and equity markets using daily data from 2004 to 2019, and measure the strength of flow via relative predictive gains. In contrast to prior studies, we find a two-way interactive effect in which certain types of information are captured more efficiently in prices by each market. We also find that the time-varying coefficient vector autoregression results in superior forecasting gains relative to models not accounting for price discovery. These results have implications for systematic investors, arbitrageurs and stakeholders who monitor systematic markets for their informational content.     

Option value and the price of teardown properties

The introduction of uncertainty over the future price of structural capital into a model of teardowns implies a value to delaying the demolition vs. preservation decision, and that the market price of a redeveloped property may increase with its quantity of structural capital. Using data from an active teardown market, we test the model’s prediction that hedonic price function coefficients depend on the expected time between sale and demolition. As predicted, structural variables have significant effects on the sales prices of both teardown and non-teardown properties, and the effects are generally much larger the lower the estimated teardown probability.     

A calibration procedure for analyzing stock price dynamics in an agent-based framework

In this paper we introduce a calibration procedure for validating of agent based models. Starting from the well-known financial model of (Brock and Hommes, 1998), we show how an appropriate calibration enables the model to describe price time series. We formulate the calibration problem as a nonlinear constrained optimization that can be solved numerically via a gradient-based method. The calibration results show that the simplest version of the Brock and Hommes model, with two trader types, fundamentalists and trend-followers, replicates nicely the price series of four different markets indices: the S&P 500, the Euro Stoxx 50, the Nikkei 225 and the CSI 300. We show how the parameter values of the calibrated model are important in interpreting the trader behavior in the different markets investigated. These parameters are then used for price forecasting. To further improve the forecasting, we modify our calibration approach by increasing the trader information set. Finally, we show how this new approach improves the model׳s ability to predict market prices.     

Mixed random forest,cointegration, and forecasting gasoline prices

One of the most successful forecasting machine learning (ML) procedures is random forest (RF). In this paper, we propose a new mixed RF approach for modeling departures from linearity that helps identify (i) explanatory variables with nonlinear impacts, (ii) threshold values, and (iii) the closest parametric approximation. The methodology is applied to weekly forecasts of gasoline prices, cointegrated with international oil prices and exchange rates. Recent specifications for nonlinear error correction (NEC) models include threshold autoregressive models (TAR) and double-threshold smooth transition autoregressive (STAR) models. We propose a new mixed RF model specification strategy and apply it to the determinants of weekly prices of the Spanish gasoline market from 2010 to 2019. In particular, the mixed RF is able to identify nonlinearities in both the error correction term and the rate of change of oil prices. It provides the best weekly gasoline price forecasting performance and supports the logistic error correction model (ECM) approximation.     

Forecasting bulk prices of Bordeaux wines using leading indicators

Agricultural price forecasting has been being abandoned progressively by researchers ever since the development of large-scale agricultural futures markets. However, as with many other agricultural goods, there is no futures market for wine. This paper draws on the agricultural prices forecasting literature to develop a forecasting model for bulk wine prices. The price data include annual and monthly series for various wine types that are produced in the Bordeaux region. The predictors include several leading economic indicators of supply and demand shifts. The stock levels and quantities produced are found to have the highest predictive power. The preferred annual and monthly forecasting models outperform naive random walk forecasts by 27.1% and 3.4% respectively; their mean absolute percentage errors are 2.7% and 3.4% respectively. A simple trading strategy based on monthly forecasts is estimated to increase profits by 3.3% relative to a blind strategy that consists of always selling at the spot price.     

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.     

Experience curve models in the electricity supply industry

There has been much controversy over the use of the Experience Curve for forecasting purposes. The Experience Curve model has been criticised both on theoretical grounds and because of the practical problems of using it. An alternative model of experience effects due to Towill has certain attractions from the standpoint of theory. However, a rather deeper question is whether experience curve type models produce superior forecasts to those derived using extrapolative techniques.This paper examines these questions in the context of three time series taken from the electricity supply industry, viz: average thermal efficiency; works costs; and price of electricity. The two latter series require price deflation. Both the implied GDP consumption deflator, and a wholesale price index for fuel and electricity were used for this purpose. It is argued that because of the absence of substitutes and of the effects of competition, along with the high quality of data available on the electricity supply industry, these three series provide a favourable test of the experience curve approach to forecasting. The two experience curves performed on the whole markedly worse than the simpler extrapolative methods on the two financial series examined. For the average thermal efficiency series the Towill model and the Experience Curve model marginally outperformed the extrapolative methods.Overall, there was little support for using either the Experience Curve or Towill models. These are obviously more difficult to use than simple univariate models and do not provide significantly better forecasts. Moreover, the Towill model gave rise to considerable estimation and specification problems with the data used here.     

Long‐run relations in European electricity prices

This paper analyses the interdependencies existing in wholesale electricity prices in six major European countries. The results of a robust multivariate long‐run dynamic analysis reveal the presence of four highly integrated central European markets (France, Germany, the Netherlands and Austria). The trend shared by these four electricity markets appears to be common also to gas prices, but not to oil prices. The existence of a common long‐term dynamics among electricity prices and between electricity prices and gas prices can be explained by the similarity of the market design across Europe and by the same marginal generation technology. Since standard unit root and cointegration tests are not robust to the peculiar characteristics of electricity prices time series, we also develop a battery of robust inference procedures that should assure the reliability of our results. Copyright © 2009 John Wiley & Sons, Ltd.     

Forecast on silver futures linked with structural breaks and day-of-the-week effect

Silver future is crucial to global financial markets. However, the existing literature rarely considers the impacts of structural breaks and day-of-the-week effect simultaneously on the volatility of silver future price. Based on heterogeneous autoregressive (HAR) theory, we establish six new type heterogeneous autoregressive (HAR) models by incorporating structural breaks and day-of-the-week effect to forecast the volatility. The empirical results indicate that new models’ accuracy is better than the original HAR model. We find that structural breaks and the day-of-the-week effect contain much forecasting information on silver forecasting. In addition, structural breaks have a positive effect on the silver futures’ volatility. Day-of-the-week effect has a significantly negative influence on silver futures’ price volatility, especially in the mid-term and the long-term. Our works is the first to combine the structural breaks and day-of-the-week effect to identify more market information. This paper provides a better forecasting method to predict silver future volatility.     

建筑产品价格探讨

建筑产品价格的变动趋势影响业主、承包商、金融机构及其他相关利益团体的投资决策，随着我国加入WTO及建筑业与世界接轨，计划经济时期以行政命令方式决定建筑产品价格、计划经济向市场经济转变时期依据统一定额和费率决定建筑产品价格的模式已不适应市场经济的发展了，西方市场经济国家成熟的无底招标模式在我国的应用也正在探索之中。为了正确理解建筑产品价格并进一步为建筑产品价格的预测奠定一定的基础，结合我国建筑业的实际，探讨建筑产品价格的特点、不同经济时期建筑产品价格形成的模式以及建筑产品价格运动的规律，并初步探讨了可能影响建筑产品价格的宏观经济变量。     

Investment Incentives and Electricity Spot Market Competition

In this paper we analyze investment decisions of strategic firms that anticipate competition on many consecutive spot markets with fluctuating (and possibly uncertain) demand. We study how the degree of spot market competition affects investment incentives and welfare and provide an application of the model to electricity market data. We show that more competitive spot market prices strictly decrease investment incentives of strategic firms. The effect can be severe enough to even offset the beneficial impact of more competitive spot markets on social welfare. Our results obtain with and without free entry. The analysis demonstrates that investment incentives necessarily have to be taken into account for a serious assessment of electricity spot market design.