Price Series Research Articles

Application of Cluster Analysis in the Evaluation of Automotive Fuel Prices Marketed in Goiás

Objective: The aim of this study is to present economic indicators constructed through cluster analysis, aiming to identify which gas station brands offer the lowest overall fuel prices. Theoretical Framework: The increasing demand for low-cost products, particularly automotive fuels, is a constant in the Brazilian market, significantly impacting both the economy and consumers' daily lives. This scenario underscores the analysis of prices as a crucial tool for understanding market behavior. Method: Data were obtained from the open data portal of the National Agency of Petroleum, Natural Gas and Biofuels (ANP), focusing on the historical price series of regular gasoline, premium gasoline, and ethanol in the state of Goiás. Cluster analysis was employed to group eight gas station brands into three distinct clusters. Results and Discussion: Findings reveal that Rodoil offers the lowest overall fuel prices, whereas Alesat, Ciapetro, and Raízen have the highest overall prices. These discoveries provide valuable insights into the competitive dynamics of the fuel market in Goiás. Research Implications: The significant debates generated in society by the rising prices of gasoline, ethanol, and other products such as cooking gas and diesel underscore the urgent need to revise fuel pricing policies in the country. Originality/Value: This study contributes uniquely by applying cluster analysis to identify price patterns among different gas station brands, offering an innovative approach to understanding competitiveness in the fuel distribution sector in Goiás.

Oil-price forecasting based on ARIMA, exponential smoothing, and autoregressive neural network models

Oil prices have an impact on the global economy. Demand shocks, such as those caused by the recent COVID-19 epidemic, or supply shocks caused by production reductions by major global producers, result in sudden price changes. Oil industry has been collecting price data since the late 19th century. A time series of this magnitude allows for consistent analysis of price behavior forecasts following demand or supply shocks. Financial time series are sensitive to exogenous shocks. From this perspective, this work presents a comparative analysis of predictive crude oil prices scenarios, obtained from a historical series of average annual prices. Two approaches were used: first, a combination of classic strategies based on exponential smoothing, and ARIMA models. Second, an autoregressive neural network model. Both approaches are complementary when used for long-term forecasting of oil prices and show good response to volatile data. Therefore, we are able to present an alternative data analysis, in a field where there is a great amount of relevant historical series, using probabilistic and non-linear models in order to observe predictions and make more effective decisions.

A Research of Fine-Tuning CNN-LSTM Model for Gold Price Prediction

Gold price and foreign exchange price prediction are usually regarded as serialized data with strong time correlation. It is precisely because of this feature that the performance of traditional convolutional neural networks is often not as good as that of recurrent neural networks. This paper fine-tunes the CNN-LSTM model in two ways. The first is to simplify the input data, convert the time series into binary data, and change the model task from predicting prices to predicting price fluctuations. The second is to add a dropout layer and use the dropout layer to improve the model's overfitting problem. The results demonstrate that the improved model outperforms the first model, with prediction accuracy rising from 54% to 57%. Furthermore, the refined model is not limited to processing the gold price series; it can also be applied to other time series, and when paired with other models, it could yield even better outcomes.

Assessing the risk of overexploitation to a tarantula species in the pet trade.

The global pet trade in invertebrates remains poorly understood and underrepresented in policy and research. Tarantulas are a highly traded invertebrate group. Many individuals in trade are wild caught, and trade regulation is often lacking, raising concerns about the effect of trade on local ecosystems and populations. We addressed local concerns surrounding the international trade of Tapinauchenius rasti (Caribbean diamond tarantula), which is endemic to Saint Vincent and the Grenadines. We assessed the extinction risks associated with this widely traded species by analyzing supply of and demand for the species with the COM-B (capability, opportunity, motivation, behavior) model. This model breaks down decision-making into capability, opportunity, and motivation. We interviewed stakeholders throughout the supply chain and analyzed content of an online hobbyist forum and a time series of prices for this species and other tarantulas. In terms of motivation, there was limited preference for the Caribbean diamond tarantula due to its morphology and behavior. In terms of opportunity, the species was readily available, primarily through captive breeding. Collecting wild specimens is challenging due to enforcement measures and logistical difficulties, making it an unprofitable endeavor. In terms of capability, the species was relatively low in price, likely because it is easy to breed in captivity. As a result, the current wildlife trade does not pose a significant threat to the Caribbean diamond tarantula. Our methodology can serve as a valuable tool for assessing potential threats posed by trade to other spider species and possibly other invertebrates. Understanding these threats is crucial for promoting responsible and sustainable trade practices that minimize risks to wild populations while ensuring equitable benefits for communities coexisting with wildlife.

Volatility Spillover Effect of NCDEX Spot and Futures Prices in Spices

This research is an attempt to assess the volatility in spot and futures prices in spices on NCDEX. Spot price and futures price Volatility is prominent indicators of the commodity futures market to protect the interest of beneficiaries and to hedge sharp price fluctuations in commodity markets. The study tested price patterns using data with 1777 observations for FY2015 to FY2022 daily spot and futures price series of near month contracts. VECM test and EGARCH were computed to examine spot and futures prices for three selected spices, namely jeera (cumin), turmeric and coriander. The results revealed that Turmeric and Coriander had significant bidirectional relationship and Jeera had significant unidirectional relationship between futures and spot market prices. Higher half-life (days) values of futures prices indicates more stability than spot prices of selected spices. Higher asymmetric effect value of spot market indicates higher volatility impacts because of noisy shocks than futures market.

Framework for multivariate carbon price forecasting: A novel hybrid model

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

A combined model based on decomposition and reorganization, weight optimization algorithms for carbon price point and interval prediction

With the increasing environmental and climate problems caused by global warming, more and more countries are paying attention to reducing carbon emissions. Accurately predicting carbon prices can help create a stable carbon market and mitigate global warming. Therefore, a combined model which has four modules is proposed, they are the decomposition of carbon price series, reorganization of subsequences, point forecasting, and interval forecasting. For data preprocessing of decomposition, it can reduce the non-linearity of sequences. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the original series into multiple Intrinsic Mode Functions (IMFs). Reorganization uses SampEn (SE) and T-test to reorganize the decomposed sequence into three new sequences according to actual meanings. For point prediction, the combined prediction model uses Particle Swarm Optimization (PSO) to obtain the optimal weights of Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), which can integrate their strengths to improve prediction accuracy. Interval prediction is based on fitting the optimal distribution function by Maximum Likelihood Estimate (MLE). Different significance intervals can be obtained from the point prediction results and the distribution function. Empirical and comparative experiments show this combined model has excellent point and interval predictions. Besides, the significance and stability are demonstrated. Finally, some valuable references are provided for policymakers.

Hedonic Models Incorporating Environmental, Social, and Governance Factors for Time Series of Average Annual Home Prices

Hedonic Models Incorporating Environmental, Social, and Governance Factors for Time Series of Average Annual Home Prices

Neural network analysis in time series forecasting

Objectives. To build neural network models of time series (LSTM, GRU, RNN) and compare the results of forecasting with their mutual help and the results of standard models (ARIMA, ETS), in order to ascertain in which cases a certain group of models should be used.Methods. The paper provides a review of neural network models and considers the structure of RNN, LSTM, and GRU models. They are used for modeling time series in Russian macroeconomic statistics. The quality of model adjustment to the data and the quality of forecasts are compared experimentally. Neural network and standard models can be used both for the entire series and for its parts (trend and seasonality). When building a forecast for several time intervals in the future, two approaches are considered: building a forecast for the entire interval at once, and step-by-step forecasting. In this way there are several combinations of models that can be used for forecasting. These approaches are analyzed in the computational experiment.Results. Several experiments have been conducted in which standard (ARIMA, ETS, LOESS) and neural network models (LSTM, GRU, RNN) are built and compared in terms of proximity of the forecast to the series data in the test period.Conclusions. In the case of seasonal time series, models based on neural networks surpassed the standard ARIMA and ETS models in terms of forecast accuracy for the test period. The single-step forecast is computationally less efficient than the integral forecast for the entire target period. However, it is not possible to accurately indicate which approach is the best in terms of quality for a given series. Combined models (neural networks for trend, ARIMA for seasonality) almost always give good results. When forecasting a non-seasonal heteroskedastic series of share price, the standard approaches (LOESS method and ETS model) showed the best results.

Assessing the Asymmetric Effect of Global Climate Anomalies on Food Prices: Evidence from Local Prices

This paper uses time-varying smooth transition autoregressive model to investigate the asymmetric nature of El Niño Southern Oscillation (ENSO) —an exogenous climatic factor—with respect to the nonlinear dynamics of food prices in sub-Saharan Africa. Curating food price series from more than 1100 markets from 36 SSA countries, the study finds that ENSO (linearly or nonlinearly) affects roughly half of food prices considered, with most nonlinear models exhibiting strong asymmetric properties with shock-inflicted persistence. Moreover, in terms of the location of the burden of ENSO impact, I find a geographical and food product divide. Specifically, ENSO appears to be more efficacious on maize prices in Southern, Eastern, and some parts of Central Africa. Conversely, local rice, cassava, millet, and animal products are least affected. The policy implication of this dichotomy is that response to ENSO news should be subregion-specific rather than region-specific, depending on how the subregions absorb the shock.

Gaussian Process Regression Based Silver Price Forecasts

A significant number of market participants have placed a high level of importance on price estimates for the primary metal commodities for a considerable amount of time. To tackle the problem, we investigate the daily reported price of silver in our study. The sample that is being analyzed spans a period of 13 years, starting on April 20, 2011, and ending on April 19, 2024. The price series which is being investigated has important implications for the commercial world. Specifically, when it comes to this unique circumstance, Gaussian process regression models are developed utilizing cross-validation strategies and Bayesian optimization procedures. The forecasting of prices is therefore accomplished via the use of the methods that are developed as a result of the situation. For the out-of-sample evaluation period that extends from October 5, 2021 to April 19, 2024, our empirical forecasting approach yields price estimates deemed reasonably accurate. The relative root mean square error reached for the silver price is 0.2257%, with the corresponding root mean square error of 0.0515, mean absolute error of 0.0389, and correlation coefficient of 99.967%. Due to the availability of models that forecast prices, investors and governments are supplied with the information they need to make educated judgments on the silver market by providing them with the knowledge they require. The framework of the Gaussian process regression with Bayesian optimizations demonstrates its good potential for modeling and forecasting sophisticated commodity price series for market participants.

Carbon price forecasting using leaky integrator echo state networks with the framework of decomposition-reconstruction-integration

Carbon trading is one of the strategies to achieve the goal of dual carbon. However, carbon prices exhibit non-stationary and nonlinear characteristics, posing significant challenges for accurate forecasting. Inspired by the ideas of “division and conquest” and “granularity reconstruction”, a decomposition-reconstruction-integration framework is built for carbon price forecasting. The proposed model leverages a comprehensive ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sample entropy (SE) to extract features from two perspectives of time and complexity, and thus the one-dimensional time series of carbon price is reconstructed into different items. Subsequently, leaky integrator echo state networks (LiESNs) are utilized to forecast each of these items individually. These individual forecasts are then integrated to generate the final point forecast. To evaluate the uncertainty of the point forecast, a Gaussian process model is applied to interval forecast. The proposed model comprehensively considers the time series features of carbon price including the temporal modal features in the decomposition stage, the entropy probability features in the reconstruction stage, and the essential temporal dependencies via dynamic reservoir of interconnected neurons in the integration forecasting stage. Experimental results demonstrate the model's exceptional forecasting performance, surpassing the effectiveness of alternative approaches.

A hybrid forecasting method considering the long-term dependence of day-ahead electricity price series

Day-ahead electricity market is crucial for ensuring the balance of electricity supply and demand. Its electricity price serves as a key guide for market participants and power dispatching agencies, making accurate short-term price forecasting essential. In this paper, a hybrid short-term electricity price forecasting method considering the long-term dependence of wavelet transform (WT) series is proposed. Different from the existing studies that do not distinguish the characteristics of WT series, this paper uses Hurst exponent and autocorrelation function (ACF) and partial autocorrelation function (PACF) plots to analyze the long-term dependence of subseries, and chooses seasonal autoregressive integrated moving average (SARIMA) or long short-term memory (LSTM) method to construct a hybrid forecasting model according to the characteristics of each subseries. Validation using real electricity prices from the NYISO and PJM markets shows that, compared to methods that do not consider long-term dependencies of subseries, the proposed method can improve prediction accuracy by up to 50.98 % and stability by up to 68.09 %.

Estimated Value-at-Risk Using the ARIMA-GJR-GARCH Model on BBNI Stock

Stocks are investment instruments that are much in demand by investors as a basis in financial storage. Return and risk are the most important things in investing. Return is a complete summary of investment and the return series is easier to handle than the price series. The movement of risk of loss is obtained from stock investments with profits. One way to calculate risk is value-at-risk. The movement of stocks is used to form a time series so that the calculation of risk can use time series. The purpose of this study was to find out the Value-at-Risk value of BBNI Shares using the ARIMA-GJR-GARCH model. The data used in this study was the daily closing price for 3 years. The time series method used is the model that will be used, namely the Autoregressive Integrated Moving Average (ARIMA)-Glosten Jagannathan Runkle - generalized autoregressive conditional heteroscedastic (GJR-GARCH) model. The stage of analysis is to determine the prediction of stock price movements using the ARIMA Model used for the mean model and the GJR-GARCH model is used for volatility models. The average value and variants obtained from the model are used to calculate value-at-risk in BBNI shares. The results obtained are the ARIMA(1,0,1)-GJR-GARCH(1.1) model and a significance level of 5% obtained value-at-risk of 0.0705.

A study of European wheat price series forecasting based on multiple variants of Holt-Winters and LSTM models

Given the importance of wheat in global food security and agricultural economy, accurate price forecasts are of significant practical significance for growers, the food chain, and government macro-control. Four different time series forecasting models were used in this study, including the traditional Holt-Winters three-parameter exponential smoothing method and three improved models based on the Long Short-Term Memory Network (LSTM): the CNN-LSTM, the GRU, and the VMD-LSTM. By comparing the forecasting effects of these models under the same training and test sets, this study aims to select the best models using the best model for predicting European wheat prices from 2010 to 2023 after training. The results show that the CNN-LSTM model performs the best in predicting the European wheat price series, and the future wheat price will show a slow downward trend, and the CNN-LSTM model combines the feature extraction ability of convolutional neural network and the long-term dependency learning ability of LSTM, which can effectively capture the fluctuation characteristics of the price series and the trend characteristics. The GRU model also shows a better prediction ability, especially in dealing with long-term dependencies. in dealing with long-term dependencies. However, the VMD-LSTM model has certain shortcomings in its prediction effect when facing time series data with large fluctuations in the later period. In addition, the Holt-Winters model predicts that after a period of slow decline, wheat prices may rebound, which may be related to the adjustment of market supply and demand and changes in external factors.

Liberalization vis-à-vis non-liberalization trade policy: Exploring the impact of price volatility on producer share price and cocoa supply response in Nigeria and Ghana

Cocoa producers respond differently to volatile price shocks, but the nature of these responses remains unknown, especially in the liberalized and non-liberalized markets in Nigeria and Ghana, respectively. Therefore, we assess the extent to which price volatility affects the supply of cocoa while also analyzing the effect of price volatility on Nigeria's cocoa producer price share vis-à-vis Ghana's cocoa producer price share. We further analyze how producers react given the asymmetric nature of price volatility in Nigeria and Ghana. Annual secondary data were obtained from the World Development Index, National Bureau of Statistics, International Cocoa Organization, Central Bank of Ghana, Nigeria, and so on, from 1970 to 2019. We used the Ordinary Least Squares method, generalized autoregressive conditional heteroskedastic (GARCH) model, extensions, and the Vector Error Correction Model (VECM) for this study's analysis. Our results show the presence of volatility in price series and that volatility is asymmetric in nature. The results of the supply response show that price volatility has no significant relationship with cocoa supply in Ghana. In contrast, price volatility has a significantly positive relationship with the supply of cocoa in Nigeria at the 1 % level. The results of the VECM show that, in the long run, the cocoa producer price in Ghana will be negatively affected by both international price volatility and inflation rates at 5 % and 1 %, respectively. We suggest that cocoa farmers should have licensing to sell commodities to the international market directly without interference from the marketing board.

Optimization of the generalized covariance estimator in noncausal processes

This paper investigates the performance of routinely used optimization algorithms in application to the Generalized Covariance estimator (GCov) for univariate and multivariate mixed causal and noncausal models. The GCov is a semi-parametric estimator with an objective function based on nonlinear autocovariances to identify causal and noncausal orders. When the number and type of nonlinear autocovariances included in the objective function are insufficient/inadequate, or the error density is too close to the Gaussian, identification issues can arise. These issues result in local minima in the objective function, which correspond to parameter values associated with incorrect causal and noncausal orders. Then, depending on the starting point and the optimization algorithm employed, the algorithm can converge to a local minimum. The paper proposes the Simulated Annealing (SA) optimization algorithm as an alternative to conventional numerical optimization methods. The results demonstrate that SA performs well in its application to mixed causal and noncausal models, successfully eliminating the effects of local minima. The proposed approach is illustrated by an empirical study of a bivariate series of commodity prices.

THE APPLICATION OF STANDARD GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (SGARCH) MODEL IN FORECASTING THE STOCK PRICE OF BARITO PACIFIC

Stock potentially yields higher returns than other investments, but is riskier due to volatile prices. To minimize the risk of loss, investors can forecast the stock price to help in deciding whether to buy, sell, or hold the stock. Several methods are available for forecasting the stock price such as ARIMA, ARCH, and SGARCH. ARIMA model works best for series with a constant variance of error. However, almost all stock price series have a non-constant variance of error, known as heteroscedasticity, as such ARIMA isn’t suited for modeling the stock price. In contrast, the SGARCH model can handle series with heteroscedasticity. This makes it better suited for modeling stock prices as they have similar characteristics. PT Barito Pacific (BRPT) is a publicly traded firm that works mainly in petrochemical and geothermal energy. BRPT’s net profit increased in 2023 by 243% and the demand for geothermal energy is expected to increase due to the government's renewable energy transition project. Therefore, this study forecasts the BRPT’s stock price using the SGARCH model with R Studio. The stock price used ranges from October 1st, 2018 to August 16th, 2023 gotten from the Yahoo Finance Website. Based on the least AIC, this study found that ARMA(6,2)-SGARCH(1,1) is the best model for forecasting the stock price. This model gives a very accurate prediction of the stock price from April 1st, 2023 – April 19th, 2023 with a mean absolute error of 78.11, root mean square error of 89.51, and mean absolute percentage error of 9.81%.

The Spillover Effect of Stock Market Sentiment Intensity and Uncertainty on Housing Market in the United States

Investor sentiment is a key indicator of market performance. Many have studied how investor sentiment affects market returns or returns of different categories of stocks. Besides, many also studied how information revealed in media can affect the housing market. This paper focuses on the sentiment revealed in social media, Tweet, by combining these two key factors that have not been studied. This paper studies the spillover effect of stock market sentiment on the housing market by applying Vector Autoregression. The VAR model helps explain the relationship between housing prices and stock market sentiment from tweets, where sentiments are divided into sentiment intensity and sentiment uncertainty. The series of sentiment intensity and series of housing prices interact with each other, and the change in sentiment in the last period spills over to the housing market which affects housing prices, but the change in housing price does not affect sentiment in reverse. Similarly, the series of sentiment uncertainty and the series of housing prices interact with each other, and the change in sentiment diversity in the last period also spills over to the housing market which affects housing prices, but the change in housing price does not affect sentiment in reverse.

A note on the fractional momentum strategy

ABSTRACT The momentum strategy has been existent for a long time and under numerous versions all of which is based on some form of the past return. It was noted recently that this metric of taking the difference of two prices might be a too stringent transformation to achieve stationary. For this matter, a momentum strategy that is not based on simple past return was proposed, called fractional momentum strategy, that retains some memory in the price series. Empirical results have demonstrated significant returns improvement of this strategy over the traditional one albeit under weekly rebalancing. This article investigates the applicability of the strategy under more realistic rebalancing frequencies and finds that the strategy performs inferior to the traditional momentum strategy as rebalancing frequency is decreased.