Price Series Research Articles

Pricing evolution in the Colombian electricity market: Does regulation matter?

Electricity pricing ($/kWh) is a topic of widespread discussion, both for developing and developed countries, because it has begun a worrying upward trend that impacts the well-being of the entire population. Initially, it is expected to reflect the marginal costs of generation and to be affected mainly by the availability of primary resources for electricity generation and the interaction of agents in the market. However, regulations may lead to changes in this expected behavior, for Colombian case, several studies have highlighted the significant impact of the reliability charge (current capacity market in Colombia) on spot price formation, although this capacity market features remuneration independent of the energy market, and these revenues must reflect the costs associated with reliability. The present study conducts an impact evaluation on the time series of the electricity spot price in Colombia to establish whether the resolution that established the reliability charge (CREG071/06) significantly affected the formation of the spot market price. The results suggest that the resolution under study increased the bid of generators in the short and long term, which was accompanied by an average increase of approximately 12% in the spot price of electricity in Colombia.

Spatial Interactions and Price Transmission in Yam Markets in Côte d'Ivoire

Spatial interactions are essential factors in the process of price transmission. However, very few studies have taken these factors into account in the analysis of price transmission. This study attempts to analyze the price transmission in the yam markets in Côte d’Ivoire by emphasizing on spatial interactions. To do this, based on the two types of data, rainfall and the price series of four varieties of yams (kponan, krenglè, assawa and bêtè-bètè) and cassava, the dynamic Durbin spatial model has been used. The results show that prices are transmitted both between markets and yam varieties. Indeed, the prices of krenglè and assawa varieties influence the price of the kponan variety. The rainfall also increases the price of the kponan variety. Therefore, it would be important to take into account the different varieties of yam in price stability reforms and encourage producers to produce more of these varieties. Out-of-season production will prevent risks from climatic hazards.

Denoising or distortion: Does decomposition-reconstruction modeling paradigm provide a reliable prediction for crude oil price time series?

Reliable forecasting of the crude oil price has important implications for production planning, investment decisions and policy making. Though the effectiveness of denoising hybrid models in crude oil price forecasting has been widely demonstrated, we argue that the denoised series used for model fitting may be affected by the out-of-sample information due to the one-time denoising operation applied to the entire price series. To verify whether the denoising paradigm does improve the crude oil price forecasting, a rolling window mechanism is introduced into two denoising hybrid models based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD). The novel R-EEMD-RVFL and R-VMD-RVFL models with the random vector functional link (RVFL) neural network are proposed. Then, both monthly and weekly West Texas Intermediate crude oil spot prices are used for multi-step-forward prediction. The experiment reports that: Firstly, the prediction provided by conventional denoising hybrid models is unreliable due to the one-time denoising procedure. Secondly, the proposed R-VMD-RVFL model achieves superior and robust accuracies in both monthly and weekly crude oil price predictions, especially when the forecast horizon is relatively long. Furthermore, this study provides theoretical supports for the findings using the weak-form efficient market hypothesis and behavioral finance theory as well as technical analysis from the perspective of denoising methods.

Driven by crises: Price integration on the grain market in late medieval Flanders

AbstractAt the centre of the debate on pre‐industrial economic growth is the study of market integration, a topic that has increasingly been the focus of intense scientific interest in recent decades. However, this has remained limited to the early modern and modern periods, mainly due to the availability of relevant data. New grain price series have been constructed for several Flemish cities dating back to the early fourteenth century. As one of the most populated regions in the late Middle Ages, the case of Flanders shows that the extraordinary sequence of price shocks in the mid‐fourteenth century had a positive impact on the degree of price integration in the grain market. The Flemish grain market functioned better in times of crisis, but caused prices to rise steadily across the entire integrated system during the prolonged crisis period. Whereas many studies have labelled the late Middle Ages – particularly the fifteenth century – as an age of economic contraction with more isolated trade networks, this study shows that Flanders remained a highly economically integrated region.

A hybrid carbon price prediction model based-combinational estimation strategies of quantile regression and long short-term memory

In recent years, global warming mitigation has received increasing attention. Reasonable carbon prices in stable carbon markets can reduce greenhouse gas emissions. Therefore, to accurately predict carbon prices and describe their fluctuations, a hybrid model was developed based on Complete Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), SampEn (SE), Long Short-Term Memory (LSTM), Quantile regression (QR), and Kernel density estimation (KDE). First, CEEMDAN was used to decompose the original carbon price series into several Intrinsic Mode Functions (IMFs), and the SE of each decomposed series was calculated to reconstruct a new series. The actual influencing factors determine the number of new series. The prediction model, named QRLSTM, combines QR and LSTM to achieve point and interval predictions. Finally, KDE was used to obtain a probabilistic prediction of the daily carbon price. Compared with other methods, the experimental results in Hubei, China, showed that the proposed CEEMDAN-SE-QRLSTM model had the best performance. For point prediction, the MSE, MAE, RMSE, MAPE, and R2 were 0.19, 0.33, 0.43, 0.01, and 91.7%, respectively. In interval prediction, the coverage width criterion (CWC) in the 95%, 90%, and 80% confidence intervals was small, with values of 0.37, 0.32, and 0.27, respectively. In probabilistic prediction, the continuous ranked probability score (CRPS) with 95% confidence was small, with a value of 0.25. In addition, the improvement in point prediction and stability of the hybrid model were also proved. The proposed model can provide more accurate results and valuable information for decision-makers.

An ARIMA-LSTM model for predicting volatile agricultural price series with random forest technique

Machine learning mechanism is establishing itself as a promising area for modelling and forecasting complex time series over conventional statistical models. In this article, focus has been made on presenting a machine learning algorithm with special attention to deep learning model in form of a potential alternative to statistical models such as Autoregressive Integrated Moving Average (ARIMA) and ARIMA-Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Further, an improved hybrid ARIMA-Long Short-Term Memory (LSTM) model based on the random forest lag selection criterion has been introduced. ARIMA model has been used to estimate the mean effect and the GARCH model is employed with the residuals obtained from the ARIMA model to estimate the volatile behaviour of the series. ARIMA-GARCH models act as superior statistical models over ARIMA models based on the lowest AIC and BIC values. LSTM model is employed on all normalised training data series. After which we built a comparison scenario independently between ARIMA, ARIMA-GARCH, LSTM and ARIMA-LSTM models on forecasting accuracy in terms of the lowest RMSE, MAPE and MASE values. The proposed random forest-based ARIMA-LSTM model proved its superiority over the conventional statistical models with an improvement to the tune of 8–25% for RMSE, 2–28% for MAPE and 2–29% for MASE. The proposed hybrid model has been successfully applied to volatile monthly price indices of pulses namely gram, moong and urad. This piece of work will enrich the literature on machine learning and further intrigue researchers to apply it to various other volatile data sets.

Agricultural Commodity Price Prediction using Long Short-Term Memory (LSTM) based Neural Networks

Background: Agricultural price forecasting is one of the research hotspots in time series forecasting due to its unique characteristics. In this paper, we develop a standard long short-term memory (LSTM) for accurately predicting a nonstationary and nonlinear agricultural price series. Methods: An LSTM model effectively analyses and captures short-term and long-term temporal patterns of a complex time series due to its recurrent neural architecture and the memory function used in the hidden nodes. Result: The empirical results using the international monthly price series of maize demonstrate the superiority of the developed LSTM model over other models in terms of various forecasting evaluation criteria. Overall, LSTM model shows great potential for improving the accuracy and reliability of agricultural price predictions, benefiting farmers, traders, and policymakers in making informed decisions.

A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting

In this study, a novel hybrid model for share price index futures forecasting named WT-ARIMA-LSTM is proposed. In this hybrid model, share price index futures are decomposed to extract data characteristics at different time scales by the wavelet transform and the ARIMA-LSTM model are applied to predict the close price of futures. The findings of the study are as follows. 1) The DWT hybrid model and the MODWT hybrid model have higher forecasting accuracy than some commonly used forecasting models under the three metrics of MAE, MAPE and RMSE. The DWT-ARIMA-LSTM model has better forecasting performance when the forecasting performance in different markets and the operational efficiency of the method are combined. 2) The DWT method is more applicable than the MODWT method in forecasting models of futures closing price series; the approximate signals obtained from the DWT decomposition have lower volatility and can better characterise the original signals. 3) The LSTM model has better prediction performance for noisy residual series, while the ARIMA model has better prediction performance for less noisy approximate signals. 4) Based on the forecasting results, a timing trading strategy is constructed that can maintain a robust return performance under different market conditions, especially on the risk side with significant advantages. In addition, this work examines the impact of the unexpected event of the COVID-19 epidemic on the forecasting performance of the model, and the results show that the model can adapt to different data structures to achieve more robust forecasting performance. This work provides insights into the integration of deep learning methods with econometric methods in the field of asset pricing.

Development Trajectory of Blockchain Platforms: The Role of Multirole

Understanding the development trajectory of digital platforms is central to digital platform management. We develop a parametric model that investigates the development trajectories of blockchain platforms, accounting for the feedback between blockchains’ utility change and people’s adoption and abandonment behavior. We consider a typical blockchain participant to simultaneously play three roles on the platform, user, investor, and laborer, each contributing to blockchains’ multi-faceted utility: providing service for transaction/interaction, providing a medium for digital investment, and providing workspace for online labor. The model describes a three-phase development trajectory for blockchain platforms: a chaotic initial stage, a rapid growth stage, and a mature stage of stable market cycles. The model was used to match 112 token price series, demonstrating robust performance across different fitting setups and outperforming existing models. The study identifies two temporal parameters, the time delay in quitting the platform and the holding time of the platform’s token, that significantly differentiate blockchains’ development trajectories. We extend the model to study forking events; results suggest that fork launch time is more important than forking amplitude in influencing the main chain’s subsequent development and that forking can increase the exposure of the forked platform.

MARKET INTEGRATION OF SWEET LIME BETWEEN DOMESTIC AND INTERNATIONAL MARKETS

The study focused on the integration of domestic and international markets for sweet limes during 2021. The co-integration of commodity prices was considered to be necessary for the implementation of a successful marketing reform for which both domestic and international market prices are required. Due to the lack of information regarding international prices, export price information was gathered from thewebsite of the Agricultural and Processed Food Products Export Development Authority (APEDA), and secondary data regarding domestic prices of sweet lime was gathered from the website of the Agricultural Marketing Information Network (AGMARKNET). The annual average prices data for the chosen markets were gathered, and the Pearson correlation coefficient and Johansen’s co-integration analysis approaches were used to analyse the relationship between the chosen market pair. Both markets were found to be correlated through correlation analysis, and the findings of the Johansen Co-integration rank, trace test, and eigen value statistics test were used to analyse the integration among the markets. The study’s findings on market integration reveal how closely related the selected market places’ pricing are to one another. Additionally, this research demonstrated that the model’s variables exhibited an equilibrium longrun co-movement with the market price series.

АНАЛИЗ ЦЕНООБРАЗОВАНИЯ НА ЛЕКАРСТВЕННЫЕ ПРЕПАРАТЫ, ПРОИЗВЕДЕННЫЕ В УРАЛЬСКОМ ФЕДЕРАЛЬНОМ ОКРУГЕ

Introduction. Affordability of drugs is a determining factor of transiting to new price regulation mechanisms in the pharmaceutical market, such as reference pricing. Studying the social and economic determinants of wholesale and retail pricing in the pharmaceutical markets of various regions is of additional interest within the context of long- term transition to pharmaceutical insurance. Aim: To identify the signs of price discrimination, exemplified by some administrative-territorial entities of the Ural Federal District. Materials and Methods. A content analysis was performed regarding the aggregated offers available on the nation-wide pharmaceutical marketplace in the context of 10 drug manufacturers located in the Urals Federal District, and an assortment matrix was compiled (n=115). The assortment was analyzed using matrix-based and categorical methods of analysis, while pricing data were collected using the methods of descriptive statistics. Results and Discussion. Using the Sturges formula, interval series of prices were identified, and the variation coefficient was calculated for 115 trade names of drugs. The least variable were the lowest and highest price segments with high and low share of medicines included in the List of Vital and Essential Drugs, respectively. Two synthetic metrics were used to analyze the regional pricing features. Overall price variation was 14.6%, which suggests the presence of discriminatory pricing elements. The riskiest price segment is the range of RUB 100-200 per pack. Conclusions. Based on the findings of the pricing analysis for drugs manufactured in the regions of the Ural Federal District, the discriminatory pricing elements were found in the medium-low price segment.

A novel non-ferrous metal price hybrid forecasting model based on data preprocessing and error correction

Accurately forecasting the price of non-ferrous metals is of great significance for traders to avoid risks, enterprises to arrange production plans, and countries to formulate economic policies. In order to improve the forecasting accuracy of non-ferrous metal prices, this paper proposes a novel non-ferrous metal price hybrid forecasting model named IVWAIEE (IVMD-WPD-ARIMA-IELM-ECD). Firstly, the original price series is decomposed into several smoother IMFs using variational mode decomposition (VMD). Simultaneously, the improved sparrow search algorithm (IFASSA) is used to optimize the parameters of VMD to improve the adaptability of VMD. Secondly, wavelet packet decomposition (WPD) is used to decompose the residual sequence generated by VMD to further extract the information in the residual sequence. Then, the components generated by VMD and WPD are defined as high frequency components and low frequency components according to the zero-crossing rate. ARIMA is used to forecast the low frequency components with gentle fluctuations, and extreme learning machine optimized by IFASSA (IELM) is used to forecast the high frequency components with strong fluctuations. The forecasting results of each component are accumulated to obtain the initial forecasting results and error sequence of the non-ferrous metal price. Next, WPD is used to further decompose the error sequence, and the error subsequence is predicted by ARIMA and IELM to obtain the error prediction results. Finally, the error prediction results are used to correct the initial forecasting results, and the final forecasting results of non-ferrous metal prices are obtained. In order to verify the superiority of the proposed model, the copper, aluminum, and zinc futures prices of the London Metal Exchange (LME) are selected as empirical data to verify the model. The results show that the proposed IVWAIEE model has better prediction accuracy and robustness than other benchmark models. Its RMSE values in predicting copper, aluminum, and zinc futures prices are 0.2238, 0.1863, and 0.2137, respectively, and MAE values are 0.1696, 0.1171, and 0.1644, respectively, which are lower than those of other benchmark models; The proposed model not only enriches the application of secondary decomposition and error correction in the field of non-ferrous metal price forecasting, but also solves the problems of insufficient adaptability and underutilization of residual sequence in the traditional variational mode decomposition method; The research results of this paper can provide scientific and effective guidance for the investment, production, and decision-making of non-ferrous metal stakeholders.

Time–frequency domain based optimization of hedging strategy: Evidence from CSI 500 spot and futures

Considering the significance of the frequency domain information in price series, we propose a decomposition-integration hedging model from the perspective of time–frequency domain to improve the hedging effectiveness and robustness. First, the returns series of spot and futures are decomposed and reconstructed into high frequency, medium frequency, and low frequency sequences to incorporate the frequency information into the hedging model. Then, we construct a model pool to fit the various characteristics of the data in each frequency domain and further extract the corresponding information. Finally, by taking four moments of investors risk appetite into consideration, the multi-objective optimization is employed to integrate the final hedging ratio. The empirical results indicate that the decomposition-integration hedging model is superior to the single time domain models in comprehensive evaluation indices. In addition, within a certain range of market fluctuations, the structure of our model has some characteristics that are compatible with reality. To recapitulate briefly, the hedging ratio of our model is mainly determined by the high frequency and medium frequency information under drastic market fluctuations. While when the market volatility is small, the hedging ratio largely depends on low frequency information.

Crude oil price forecasting: An ensemble‐driven long short‐term memory model based on CEEMDAN decomposition and ALS‐PSO optimization

AbstractAs the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making.

SSTP: Stock Sector Trend Prediction with Temporal-Spatial Network

In financial big data field, most existing work of stock prediction has focused on the prediction of a single stock trend. However, it is challenging to predict a stock price series due to its drastic volatility. While the stock sector is a group of stocks belonging to the same sector, and the stock sector index is the weighted sum of the prices of all the stocks in the sector. Therefore the trend of stock sector is more stable and more feasible to predict than that of a single stock. In this paper, we propose a new method named Stock Sector Trend Prediction (SSTP) to solve the problem of predicting stock sector trend. In SSTP method, we adopt the Relative Price Strength (RPS) to describe the trend of the stock sector, which is the relative rank of stock sector trend. In order to learn the intrinsic probability distribution of the stock sector index series, we construct the multi-scale RPS time series and build multiple independent fully-connected stock sector relation graphs based on the real relationship among stock sectors. Then, we propose a Temporal-spatial Network (TSN) to extract the temporal features from the multi-scale RPS series and the spatial features from the stock sector relation graphs. Finally, the TSN predicts and ranks the trends of the stock sector trend with the temporal-spatial features. The experimental results on the real-world dataset validate the effectiveness of the proposed SSTP method for the stock sector trend prediction.

Cryptocurrency Price Prediction Using Frequency Decomposition and Deep Learning

Given the substantial volatility and non-stationarity of cryptocurrency prices, forecasting them has become a complex task within the realm of financial time series analysis. This study introduces an innovative hybrid prediction model, VMD-AGRU-RESVMD-LSTM, which amalgamates the disintegration–integration framework with deep learning techniques for accurate cryptocurrency price prediction. The process begins by decomposing the cryptocurrency price series into a finite number of subseries, each characterized by relatively simple volatility patterns, using the variational mode decomposition (VMD) method. Next, the gated recurrent unit (GRU) neural network, in combination with an attention mechanism, predicts each modal component’s sequence separately. Additionally, the residual sequence, obtained after decomposition, undergoes further decomposition. The resultant residual sequence components serve as input to an attentive GRU (AGRU) network, which predicts the residual sequence’s future values. Ultimately, the long short-term memory (LSTM) neural network integrates the predictions of modal components and residuals to yield the final forecasted price. Empirical results obtained for daily Bitcoin and Ethereum data exhibit promising performance metrics. The root mean square error (RMSE) is reported as 50.651 and 2.873, the mean absolute error (MAE) stands at 42.298 and 2.410, and the mean absolute percentage error (MAPE) is recorded at 0.394% and 0.757%, respectively. Notably, the predictive outcomes of the VMD-AGRU-RESVMD-LSTM model surpass those of standalone LSTM and GRU models, as well as other hybrid models, confirming its superior performance in cryptocurrency price forecasting.

Forecasting crude oil futures using an ensemble model including investor sentiment and attention

PurposeSince crude oil is crucial to the nation's economic growth, crude oil futures are closely related to many other markets. Accurate forecasting can offer investors trustworthy guidance. Numerous studies have begun to consider creating new metrics from social networks to improve forecasting models in light of their rapid development. To improve the forecasting of crude oil futures, the authors suggest an integrated model that combines investor sentiment and attention.Design/methodology/approachThis study first creates investor attention variables using Baidu search indices and investor sentiment variables for medium sulfur crude oil (SC) futures by collecting comments from financial forums. The authors feed the price series into the NeuralProphet model to generate a new feature set using the output subsequences and predicted values. Next, the authors use the CatBoost model to extract additional features from the new feature set and perform multi-step predictions. Finally, the authors explain the model using Shapley additive explanations (SHAP) values and examine the direction and magnitude of each variable's influence.FindingsThe authors conduct forecasting experiments for SC futures one, two and three days in advance to evaluate the effectiveness of the proposed model. The empirical results show that the model is a reliable and effective tool for predicting, and including investor sentiment and attention variables in the model enhances its predictive power.Research limitations/implicationsThe data analyzed in this paper span from 2018 through 2022, and the forecast objectives only apply to futures prices for those years. If the authors alter the sample data, the experimental process must be repeated, and the outcomes will differ. Additionally, because crude oil has financial characteristics, its price is influenced by various external circumstances, including global epidemics and adjustments in political and economic policies. Future studies could consider these factors in models to forecast crude oil futures price volatility.Practical implicationsIn conclusion, the proposed integrated model provides effective multistep forecasts for SC futures, and the findings will offer crucial practical guidance for policymakers and investors. This study also considers other relevant markets, such as stocks and exchange rates, to increase the forecast precision of the model. Furthermore, the model proposed in this paper, which combines investor factors, confirms the predictive ability of investor sentiment. Regulators can utilize these findings to improve their ability to predict market risks based on changes in investor sentiment. Future research can improve predictive effectiveness by considering the inclusion of macro events and further model optimization. Additionally, this model can be adapted to forecast other financial markets, such as stock markets and other futures products.Originality/valueThe authors propose a novel integrated model that considers investor factors to enhance the accuracy of crude oil futures forecasting. This method can also be applied to other financial markets to improve their forecasting efficiency.

Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique

Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990s. Given these facts, this work presents a new decomposition–combination technique that employs several nonparametric regression methods and various time-series models to enhance the accuracy and efficiency of day-ahead electricity price forecasting. For this purpose, first, the time-series of the original electricity prices deals with the treatment of extreme values. Second, the filtered series of the electricity prices is decomposed into three new subseries, namely the long-term trend, a seasonal series, and a residual series, using two new proposed decomposition methods. Third, we forecast each subseries using different univariate and multivariate time-series models and all possible combinations. Finally, the individual forecasting models are combined directly to obtain the final one-day-ahead price forecast. The proposed decomposition–combination forecasting technique is applied to hourly spot electricity prices from the Italian electricity-market data from 1 January 2014 to 31 December 2019. Hence, four different accuracy mean errors—mean absolute error, mean squared absolute percent error, root mean squared error, and mean absolute percent error; a statistical test, the Diebold–Marino test; and graphical analysis—are determined to check the performance of the proposed decomposition–combination forecasting method. The experimental findings (mean errors, statistical test, and graphical analysis) show that the proposed forecasting method is effective and accurate in day-ahead electricity price forecasting. Additionally, our forecasting outcomes are comparable to those described in the literature and are regarded as standard benchmark models. Finally, the authors recommended that the proposed decomposition–combination forecasting technique in this research work be applied to other complicated energy market forecasting challenges.

Different momentum effects across countries: An explanation based on investors' behavior

AbstractWe establish a model in which speculators use feedback trading characteristics to infer the behavior of irrational investors and induce them to trade. We also discuss the stability and time series of asset prices. Our results show that: (1) speculators have speculation and arbitrage demands and make “noise” to induce irrational investors to trade, (2) the time series of asset prices show stable momentum and a reversal effect when fundamental traders dominate the market, and (3) momentums are unstable and perform poorly under extreme circumstances. Our article offers a unique approach to understanding the micro mechanism of different momentum effects in various markets and suggests a plausible theoretical framework to illustrate such differences.

Integration of mustard markets in Rajasthan State of India

The study assessed the integration between mustard market pairs in Rajasthan state of India using monthlymustard price series ofsix markets from2006-2014. The co-integration tests results indicate, Tonk and Alwar; Tonk and Dausa; Bharatpur and Alwar and Sriganganagar and Dausa are integrated in the long run at lag one. Tonk and Bharatpur and Bharatpur and Dausa are also integrated in the long run at lag two. Error correction model showed that the lowest speed of adjustment towards long run equilibrium was from Tonk to Bharatpur at rate of 43.2%. The highest speed of adjustment was 103.8%, runningBharatpur to Alwar market towards long run equilibrium. Impulse response function results shows when an unexpected positive shock is given to market price in Tonk, Sriganganagar and Bharatpur, the response thereof from other trading markets will be permanent over a period of twelve months.