Price Series Research Articles

Long Memory Time-series Model (ARFIMA) Based Modelling of Jute Prices in the Samsi Market of Malda District, West Bengal

The objective of this paper is modeling and forecasting the weekly jute prices of Samsi market in the Malda district of West Bengal in the presence of long memory process. The long memory behavior of series is investigated by the ACF plot and Hurst R/S analysis. A fractionally integrated autoregressive moving-average (ARssFIMA) model is fitted using 668 weekly data (January 2009-November 2022). This study shows the efficiencies of the Hurst exponent, GPH, Smoothed periodogram, Local Whittle, and Wavelet methods used to estimate the fractional difference parameter in the ARFIMA model. Furthermore, we compared the forecasting abilities of the ARFIMA and ARIMA models. The results show that long memory is present in the jute price series. The models selected according to each method are ARFIMA (3,0.348,0), ARFIMA (3,0.291,1), ARFIMA (2,0.487,0), ARFIMA (3,0.461,0), ARFIMA (2,0.311,0), and ARIMA (2,1,1) on the basis of minimum AIC and BIC using 534 in-sample data. Finally, the wavelet method based ARFIMA (2,0.311,0) model is found to be the best optimal model in terms of MAE, RMSE, and MAPE criteria using 134 out-of-sample data. A comparative study indicates that the forecasting performance of the ARFIMA model is strongly better than that of the ARIMA model in this regard.

Empirical Assessment of Maize Price Dynamics and Market Integration in Selected Regional Markets of Benue State, Nigeria.

This study investigated how maize prices move in selected regional markets in Benue State, Nigeria. The researchers looked at monthly maize price data from January 2008 to December 2018 to determine if the price series in these different markets were connected. Specific tests were used to determine if the prices influenced each other over time (market integration) and, if so, which market (rural or urban) tended to lead the price changes in the state. The result revealed that maize prices eventually stabilized over time (became stationary) and that rural and urban markets were indeed connected. There was also evidence of some level of market integration in the short term. Interestingly, the price changes in one market didn't always directly cause changes in the other market. In some cases, price changes seemed to flow in both directions. Based on these results, this study therefore recommends that the all stakeholders provide information centers and use media channels to improve communication between maize producers and consumers in the state; as this would allow actors to make more informed decisions about maize marketing.

DeepAR-Attention probabilistic prediction for stock price series

DeepAR-Attention probabilistic prediction for stock price series

Effects of weather and climate on fluctuations of grain prices in southwestern Bohemia, 1725–1824 CE

Abstract. Grain prices in early modern Europe reflected the effects of weather and climate on crop yields and a complex array of societal and socio-economic factors. This study presents a newly developed series of grain prices for Sušice (southwestern Bohemia, Czech Republic) for the period 1725–1824 CE, based on various archival sources. It aims to analyse their relationships with weather and climate, represented by temperature, precipitation, and drought (self-calibrated Palmer Drought Severity Index, scPDSI) reconstructions, as well as particular weather extremes and anomalies reported in documentary evidence. Wheat, rye, barley, and oats series in Sušice showed high mutual correlations. The mean highest prices during the year typically occurred from May to July before the harvest, while prices usually declined afterwards. Wheat, rye, and barley prices were significantly negatively correlated with spring temperatures and positively correlated with scPDSI from winter to summer. This indicates that wetter winters, cooler and wetter springs, and wetter summers contributed to higher prices. The extremely high grain prices in the years 1746, 1771–1772, 1802–1806, and 1816–1817 were separately analysed with respect to weather and climate patterns and other socio-economic and political factors. The results obtained were discussed in relation to data uncertainty, factors influencing grain prices, and the broader European context.

Retail electricity prices in New Zealand: recent trends and the relationship to market shares

This paper constructs a monthly price series for electricity retailers in New Zealand, broken down by region and retailer, from January 2018 until June 2023. Since 2014, there has been no such price series for New Zealand that is disaggregated by retailer. We find that, over the time period of our dataset, while average prices across the largest five retailers are consistently higher than that of the smaller retailers in our dataset, the gap has narrowed substantially. The narrowing of this price gap is associated with the following trends in market shares. While, from January 2018 to September 2021, the smaller retailers, taken as a group, made considerable gains in market share at the expense of the larger retailers, the growth in the market share of the smaller retailers in our dataset stagnated in the remainder of the time period.

A Crude Oil Spot Price Forecasting Method Incorporating Quadratic Decomposition and Residual Forecasting

The world economy is affected by fluctuations in the price of crude oil, making precise and effective forecasting of crude oil prices essential. In this study, we propose a combined forecasting scheme, which combines a quadratic decomposition and optimized support vector regression (SVR). In the decomposition part, the original crude oil price series are first decomposed using empirical modal decomposition (CEEMDAN), and then the residuals of the first decomposition (RES) are decomposed using variational modal decomposition (VMD). Additionally, this work proposes to optimize the support vector regression model (SVR) by the seagull optimization algorithm (SOA). Ultimately, the empirical investigation created the feature-variable system and predicted the filtered features. By computing evaluation indices like MAE, MSE, R2, and MAPE and validating using Brent and WTI crude oil spot, the prediction errors of the CEEMDAN -RES.-VMD -SOA-SVR combination prediction model presented in this paper are assessed and compared with those of the other twelve comparative models. The empirical evidence shows that the combination model being proposed in this paper outperforms the other related comparative models and improves the accuracy of the crude oil price forecasting model.

Price Forecasting of Marine Fish Based on Weight Allocation Intelligent Combinatorial Modelling.

China is a major player in the marine fish trade. The price prediction of marine fish is of great significance to socio-economic development and the fisheries industry. However, due to the complexity and uncertainty of the marine fish market, traditional forecasting methods often struggle to accurately predict price fluctuations. Therefore, this study adopts an intelligent combination model to enhance the accuracy of food product price prediction. Firstly, three decomposition methods, namely empirical wavelet transform, singular spectrum analysis, and variational mode decomposition, are applied to decompose complex original price series. Secondly, a combination of bidirectional long short-term memory artificial neural network, extreme learning machine, and exponential smoothing prediction methods are applied to the decomposed results for cross-prediction. Subsequently, the predicted results are input into the PSO-CS intelligence algorithm for weight allocation and to generate combined prediction results. Empirical analysis is conducted using data illustrating the daily sea purchase price of larimichthys crocea in Ningde City, Fujian Province, China. The combination prediction accuracy with PSO-CS weight allocation is found to be higher than that of single model predictions, yielding superior results. With the implementation of weight allocation intelligent combinatorial modelling, the prediction of marine fish prices demonstrates higher accuracy and stability, enabling better adaptation to market changes and price fluctuations.

Forecasting EV Stock Trends Based on ARIMA Model Represented by Tesla and BYD

In recent years, an increasing number of people have turned their attention to renewable energy, and sales of electric vehicles have skyrocketed, making electric vehicles (EVs) a very popular topic. To better understand the development of the electric vehicle market and provide some insights for investors, this study forecasts EV stock trends of two leading companies in the Electric Vehicle industry, Tesla and BYD, by the Autoregressive Integrated Moving Average (ARIMA) model. The data in this research includes a daily time series of stock prices. ARIMA model is chosen in this research for its effectiveness in predicting stationary time series data, for its effectiveness in predicting stationary time series data, particularly suitable given the limited data available in the recently emerged EV industry. This research demonstrates that both Tesla and BYD’s stock closing prices are not stationary data. After differencing, for Tesla, the predicted result suggests a slightly increasing trend, while BYD's prices are predicted to remain at the same level and relatively stable in future 100 days. The study provides insights into the EV stock market, indicating a potential upward yet not pronounced trend in the future. But such a stable price might be a good choice for the investor low-risk takers.

Dynamic dependency between health-care sector and Islamic industry: before, during and after COVID-19 lockdown evidences

Purpose This study aims to empirically explore and compare the dynamic dependency between health-care sector and Islamic industries before, during and after the COVID-19 pandemic. Design/methodology/approach Time-varying student-t copula is used for before, during and after COVID-19 periods. The data used are the daily frequency price series of the selected markets from February 2017 to October 2023. Findings Empirical results found strong evidence of significant impact of the COVID-19 pandemic on the dependence structure of the studied indexes: Co-movements between various sectors are certain. The authors assist also in the birth of new dependence structure with the health-care industry in response to the COVID-19 crisis. This reflects the contagion occurrence from the health-care sector to other sectors. Originality/value By specifically examining the Islamic industry, this study sheds light on the resilience, challenges and opportunities within this sector, contributing novel perspectives to the broader discourse on pandemic-related impacts on economies and industries. Also, this paper conducts a comprehensive temporal analysis, examining the dynamics before, during and after the COVID-19 lockdown. Such approach enables an understanding of how the relationship between the health-care sector and the Islamic industry evolves over time, accounting for both short-term disruptions and long-term effects. By considering the pre-pandemic context, the paper adopts a longitudinal perspective, enabling a deeper understanding of how historical trends, structural factors and institutional frameworks shape the interplay between the health-care sector and the Islamic industry.

The Application of Neural Network Models to Explain the Relationship Between Stock Value, Returns Value, and Information on Prices

This study aims to identify features for stock market value predictions based on time series grouping. This research uses various data sets for different activities. We have the balance sheet data set, which includes a series of quarterly balance sheets. Next, there is the ratio data set. The clustering data set consists of a series of daily prices. There are two data sets for testing activities: pilot forecasts and investments with daily data, projections, and investments. To speed up the development and exploration of different methods, testing begins with a subset of the data, with additional shares of witnesses. This research uses ARIMA and artificial neural networks to predict stock prices. Predicted results provide an investment strategy that compares the results obtained with what happens, resulting in generally positive profits. The capacity of artificial neural networks to manage non-linearities in financial data explains this. Second, many experiments demonstrate that not all data can be utilized using predicting techniques. These results indicate that improved estimations do not always result from more significant data. Forecast accuracy and performance on the test set will likely be negatively impacted by noise and competing signals introduced by data from unrelated stocks. Put another way, by including unrelated data, the information about the action supplied by the group of which it is a member is obscured. As such, the forecasts more closely align with the market's average behavior than with the performance of a specific stock.

On the quadratic variation in limit order markets

This paper explores the quadratic variation (QV) as an alternative measure to the bid-ask spread in limit order markets when observed at high resolution. Although the spread cannot be precisely estimated because of microstructure noise, the QV of the price series, consisting of the transaction prices and midquotes (an expanded filtration of transaction prices), in limit order markets is an important property of high-frequency datasets. An empirical examination of a sample of stocks from the NASDAQ 100 is used to estimate and examine the QV. The examination is done at high resolution (subsecond timescale). The QV of our sample best fits a log-normal distribution with higher skewness. The QV of trades (29.73E–05) is distant from the QV measured with pretrade and posttrade price series, as they are characterized by a high range in QV. Similarly, the QV is generally lower with pretrades than posttrades. Using a high resolution, the revealed state of the limit order book presents a solution that reduced the impact cost of trading. The QV is higher with the limit order book (68.5E–05) than with trades. A measure called the limit-to-market order signal shows a consistent pattern of decline in stocks with increasing activity. The limit-to-market signal ranged from an average of 3.55 in the first quartile of stocks based on messages to 1.84 in the third quartile. This signal decreases because of a relatively larger decline in uncertainty in QV using the limit order book.

Hidden Markov guided Deep Learning models for forecasting highly volatile agricultural commodity prices

Predicting agricultural commodity prices accurately is of utmost importance due to various factors such as perishability, seasonality, production uncertainty etc. Moreover, the substantial volatility that may be exhibited in time series further adds to the complexity and constitutes a significant challenge. In this paper, a Hidden Markov (HM) guided Deep Learning (DL) models has been developed on nonlinear and nonstationary price data of agricultural commodities for forecasting by considering technical indicators viz., Moving Average (MA), Bollinger Bands (BB), Moving Average Convergence Divergence (MACD), Exponential MA (EMA) and Fast Fourier Transformation (FFT). HM Models (HMMs) can effectively handle the sequential dependencies and hidden states, while DL approach can learn complex patterns and relationships within the price series and thus the drawback of lack of generalization capability in the DL model has been overcome by HMM. In this study, the Potato price data of the Champadanga district of West Bengal, India has been utilized to assess the performance of the proposed technique. HMM has been combined with six baseline DL models viz., Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional LSTM (BiLSTM) and Bidirectional GRU (BiGRU) for forecast modeling. Performance evaluation metrics viz., Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and the insightful Diebold–Mariano (DM) test revealed that Hidden Markov hybridized with DL models surpassed baseline DL models in forecasting accuracy for 1-week, 4-week, 8-week and 12-week ahead DL predictions. The proposed approach holds significant promise for enhancing the precision of agricultural commodity price forecasting with far-reaching implications for various stakeholders such as farmers and planners.

Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids. Power transformers are key equipment in power grid projects, and their price changes have a significant impact on cost control. However, the prices of power transformer materials manifest as nonsmooth and nonlinear sequences. Hence, estimating the acquisition costs of power grid projects is difficult, hindering the normal operation of power engineering construction. To more accurately predict the price of power transformer materials, this study proposes a method based on complementary ensemble empirical mode decomposition (CEEMD) and gated recurrent unit (GRU) network. First, the CEEMD decomposed the price series into multiple intrinsic mode functions (IMFs). Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF. Then, an empirical wavelet transform (EWT) was applied to the aggregation sequence with a large sample entropy, and the multiple subsequences obtained from the decomposition were predicted by the GRU model. The GRU model was used to directly predict the aggregation sequences with a small sample entropy. In this study, we used authentic historical pricing data for power transformer materials to validate the proposed approach. The empirical findings demonstrated the efficacy of our method across both datasets, with mean absolute percentage errors (MAPEs) of less than 1% and 3%. This approach holds a significant reference value for future research in the field of power transformer material price prediction.

Wired together: Integration and efficiency in European electricity markets

This study investigates the efficiency dynamics of spot and futures electricity markets across a sample of European countries, while taking into account the impact of market couplings. The analysis employs three distinct efficiency measures, namely entropy, Hurst measure, and fractal dimension, to examine the markets of ten different countries. Specifically, entropy is used to gauge the randomness in market prices, while Hurst measure captures the long-term memory of prices, and fractal dimension measures the roughness and irregularity of price series. Our results indicate that these markets display analogous patterns of behavior, with improvements in efficiency observed following the introduction of structural and regulatory changes, such as market couplings. These findings underscore the potential advantages of market liberalization and energy market integration for both consumers and industry players, thus offering important insights for policy makers in this area.

Optimal portfolio selection with volatility information for a high frequency rebalancing algorithm

We propose a high-frequency rebalancing algorithm (HFRA) and compare its performance with periodic rebalancing (PR) and threshold rebalancing (TR) strategies. PR refers to the process of adjusting the relative weight of assets within portfolios at regular time intervals, whereas TR is a process of setting allocation limits for portfolios and rebalancing when portfolios exceed a specific percentage of deviation from the target allocation. The HFRA is constructed as an integration of pairs trading and a threshold-based rebalancing strategy, and the profitability of the HFRA is examined to determine the optimal portfolio size. The HFRA is applied to a dataset of real price series from cryptocurrency exchange markets across various trends and volatility regimes. Using cointegrated price data, it is shown that increasing the number of assets in a portfolio supports the profitability of the HFRA in an up-trend and reduces the potential loss of the HFRA in a down-trend in a high-volatility environment. For low-volatility regimes, although increasing portfolio size marginally enhances the HFRA’s profitability, the profits of portfolios of varied sizes do not significantly differ. It is demonstrated that when volatility is relatively high and the trend is upward, the HFRA can yield a substantial return via portfolios of large sizes. Moreover, the profitability of the HFRA is compared with that of the PR and TR strategies for long-term application. The HFRA is more profitable than the PR and TR strategies. This achievement of the HFRA is also validated statistically using the Fisher–Pitman permutation test.

Dynamics of cereal product price transmission between markets in Burkina Faso

PurposeEffective agricultural product price regulation policies depend on market integration and the degree of symmetry in the transmission of agricultural product price signals. This study analyzes the transmission and asymmetry of the price series between the Ouagadougou consumer market and assembly markets considering three primary cereal products in Burkina Faso.Design/methodology/approachThis study applies the nonlinear autoregressive distributed lag (NARDL) econometric model, which is an asymmetric extension of the ARDL cointegration model. The price series examined covers the period extending from January 2005 to December 2020.FindingsOur analysis provides novel insights regarding short- and long-term asymmetric effects in the transmission of price signals between assembly markets and the consumer market. We also determine that the effects of negative shocks are more persistent than those of positive shocks in several markets.Research limitations/implicationsFor markets that exhibit symmetrical responses of assembly market prices to consumer market prices, the results could reflect the continuous efforts of market players, particularly the government, to eliminate market failures and ensure the long-term efficiency of cereal markets. To this end, an agricultural market information system can have a crucial role in easing information access for all market players.Originality/valueThis study provides new evidence regarding the nature of the transmission and asymmetry of price information on primary cereal products in the largest markets in Burkina Faso. Applying the NARDL model makes it possible to simultaneously estimate short- and long-term asymmetry.

Analysis of the Relationship between Futures Prices and Mandi Spot Prices of Spices in India

For this study, we used secondary data and applied various statistical methods, including the compound growth rate, ADF test, seasonality analysis, Johansen's cointegration test, and the Granger Causality Test. The findings revealed a positive trend in the prices of selected spices, indicating a significant growth rate for both prices and arrivals. Seasonality in prices was observed, particularly during the peak season. The price series were found to be cointegrated at a significance level of 1 per cent, and a uni-directional causality was identified in both spices. It suggested that futures prices played a dominant role in the price discovery process. It was recommended to improve warehouse structures and storage chambers to enhance the retention capacity of farmers, thereby emphasizing the importance of proper storage facilities.

Platinum and palladium price forecasting through neural networks

To many commodity market participants, forecasts of price series represent a critical task. In this work, nonlinear autoregressive neural network models’ potential is explored for forecasting daily prices series of platinum and palladium over about a fifty-year period. For this purpose, one hundred and twenty model settings are examined, including different training algorithms, numbers of hidden neurons and delays, and ratios used to segment the data. With the analysis, two models leading to stable and accurate forecast results are constructed for the prices of the two commodities. In particular, the models’ performance in terms of the relative root mean square error is 1.86% and 3.61% for platinum and palladium, respectively, for the overall sample. Results in this work could help technical forecasts and policy analysis. The forecast framework might be extended to other different commodities.

EVALUATING TIME SERIES PREDICTION MODELS: EGG PRICES IN MEXICO

Mexico is the world’s largest consumer of eggs, producing 3.05 million Mg in 2021. The high variation in wholesale prices is a feature of the egg production system, which is important to producers and government institutions that need to forecast future prices for activity planning. As a result, it is necessary to propose tools that can reliably predict egg prices. The goal of this paper was to compare the performance of various statistical models by analyzing the time series of egg prices using the Akaike index and forecast error to determine which model best predicts the wholesale price of white eggs. The models evaluated were the autoregressive integrated moving average model (ARIMA), ARIMA with interventions, ARIMA with transfers, and regression with ARIMA errors. Two time series were used: the wholesale price of white eggs, constructed with data from the National System of Information and Market Integration (SNIIM) and the Agrifood and Fisheries Information Service (SIAP), and egg imports, calculated with data from the Economic Information System. The latter was used as an exogenous variable to explain the price of eggs. Both cover the period from January 2006 to December 2021. According to the Akaike index, the model with the best adjustment was ARIMA (0,1,1)(1,0,1)[12] with interventions. In the evaluation of forecast error, the best models were the regression models with ARIMA (1,1,0)(1,0,1)[12] and ARIMA (1,1,0)(1,0,1)[12] errors with transfer.

CEEMDAN-Based Hybrid Machine Learning Models for Time Series Forecasting Using MARS Algorithm and PSO-Optimization

Accurate prediction of time series data is crucial for informed decision-making and economic development. However, predicting noisy time series data is a challenging task due to their irregularity and complex trends. In the past, several attempts have been made to model complex time series data using both stochastic and machine learning techniques. This study proposed a CEEMDAN-based hybrid machine learning algorithm combined with stochastic models to capture the volatility of weekly potato price in major markets of India. The smooth decomposed component is predicted using stochastic models, while the coarser components, selected using MARS, are fitted into two different machine learning algorithms. The final predictions for the original series are obtained using optimization techniques such as PSO. The performance of the proposed algorithm is measured using various metrics, and it is found that the optimization-based combination of models outperforms the individual counterparts. Overall, this study presents a promising approach to predict price series using a hybrid model combining stochastic and machine learning techniques, with feature selection and optimization techniques for improved performance.