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1
Rifin, A., and D. Nauly. "Vector error correction model relationship between three vegetable oil products." IOP Conference Series: Earth and Environmental Science 892, no. 1 (November 1, 2021): 012062. http://dx.doi.org/10.1088/1755-1315/892/1/012062.
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Abstract International price of palm oil fluctuated frequently. It is predicted that the international price of palm oil is affected by the other vegetable oil prices. Soybean oil, rapeseed oil and palm oil are the three most important vegetable oil in the word. These commodities compete but on the other hand the world prices are moving in the same direction. This paper analyzes the relationship of these three prices in the short-run and long-run. The method utilizes in the analysis is the vector error correction model (VECM) followed by Impulse Response and Variance Decomposition. The data used is monthly data from January 2003 until December 2020. The results indicate that in the short-run, only the lag of each vegetable oil prices affects their own price. Meanwhile, in the long-run the three prices have long-run relationship or in other words the prices are cointegrated. Using variance decomposition and impulse response shows that soybean oil price has more effect on rapeseed and palm oil prices. Therefore, it can be concluded, the fluctuation of rapeseed and palm oil prices will be affected by the price fluctuation of soybean oil price
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Tunang, Yulin, Tohap Manurung, and Nelson Nainggolan. "Penerapan Model Vector Autoregressive (VAR) untuk Memprediksi Harga Cengkeh, Kopra dan Pala di Sulawesi Utara." d'CARTESIAN 8, no. 2 (July 25, 2019): 100. http://dx.doi.org/10.35799/dc.8.2.2019.23967.
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YULIN TUNANG. Application of Vector Autoregressive (VAR) Model to Predict Prices of Clove, Copra and Nutmeg Commodities in North Sulawesi. Under the guidance of NELSON NAINGGOLAN as main supervisor and TOHAP MANURUNG as a co-supervisor.The purpose of this study is to determine the vector autoregressive (VAR) model of the prices of clove, copra and nutmeg commodities in North Sulawesi. The data used are data on monthly prices of cloves, copra and nutmeg for the period of January 2015 to March 2019. Parameter estimation results for clove prices are estimated parameter values of 0,174; 0,260; 0,151 while for the copra price, the estimated value of the parameter is 0,060; 0,004; 0,002; and for nutmeg prices the parameter value of 0,215 is obtained; 0,105; 0,625. Prediction results for April, May and June 2019, namely in April 2019 the price of cloves was Rp90.882, the price of copra was Rp4.461, and the price of nutmeg was Rp70.316. The prediction results in May 2019 of clove prices amounted to Rp90.231, copra prices amounted to Rp4.411, and nutmeg prices were Rp70.021. Predicted results in June 2019 of clove prices amounted to Rp89.392, copra prices of Rp4.356, and nutmeg prices of Rp69.532. Keywords: Vector Autoregressive (VAR) model, clove, copra, nutmeg.
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CHEN, Jieh-Haur, Chuan Fan ONG, Linzi ZHENG, and Shu-Chien HSU. "FORECASTING SPATIAL DYNAMICS OF THE HOUSING MARKET USING SUPPORT VECTOR MACHINE." International Journal of Strategic Property Management 21, no. 3 (July 11, 2017): 273–83. http://dx.doi.org/10.3846/1648715x.2016.1259190.
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This paper adopts a novel approach of Support Vector Machine (SVM) to forecast residential housing prices. as one type of machine learning algorithm, the proposed SVM encompasses a larger set of variables that are recognized as price-influencing and meanwhile enables recognizing the geographical pattern of housing price dynamics. The analytical framework consists of two steps. The first step is to identify the supporting vectors (SVs) to price variances using the stepwise multi-regression approach; and then it is to forecast the housing price variances by employing the SVs identified by the first step as well as other variables postulated by the hedonic price theory, where the housing prices in Taipei City are empirically examined to verify the designed framework. Results computed by nonparametric estimation confirm that the prediction power of using SVM in housing price forecasting is of high accuracy. Further studies are suggested to extract the geographical weights using kernel density estimates to reflect price responses to local quantiles of hedonic attributes.
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Prasada, I. made Yoga, Moh Wahyudi Priyanto, and Yahya Shafiyuddin Hilmi. "KETAHANAN PANGAN PENDUDUK DI PULAU JAWA: PENDEKATAN VECTOR ERROR CORRECTION MODEL." Agrisocionomics: Jurnal Sosial Ekonomi Pertanian 4, no. 1 (May 27, 2020): 85–95. http://dx.doi.org/10.14710/agrisocionomics.v4i1.5560.
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Food security over the past few decades has been a hot topic discussed in Indonesia. Food security can indirectly reflect the level of welfare of a household in a region. Various factors can influence the level of food security, both in the short term and in the long term. Therefore, this research was conducted with the aim to find out the factors that influence the food security of the population in the short term and in the long term. The data used in this study are secondary data sourced from the Central Bureau of Statistics (BPS) in 2008-2017, namely data on food and non-food expenditure, real per capita income, agricultural land area, real sugar prices, real beef prices, and real rice prices. The data were analyzed using the VECM (Vector Error Correction Model) model. The results showed that in the short-term the factors that influence food security are income per capita real lag 1, real sugar prices lag 1, and real beef prices lag 1, while the factors that influence food security in the long-term are per capita income 1, agricultural area lag 1, real sugar 1 lag price, real beef price lag 1, and real rice price lag 1.
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Usman, Mustofa, M. Komarudin, Nurhanurawati Nurhanurawati, Edwin Russel, Wamiliana Wamiliana, and Faiz A. M. Elfaki. "Analysis Forecasting of Gasoline Prices in Some ASEAN Countries by Using State Space Representation on Vector Autoregressive Model." International Journal of Energy Economics and Policy 13, no. 6 (November 10, 2023): 194–202. http://dx.doi.org/10.32479/ijeep.14893.
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Research on the price of gasoline has become a topic of research that has been carried out by many researchers. The topic is interesting because the price of gasoline has a relationship with many aspects of people's lives. This study aims to examine the relationship pattern of gasoline prices in several ASEAN countries: Indonesia, Malaysia, and Vietnam, and to make gasoline price forecasting in these three countries for the next 12 months. This study uses a multivariate time series approach; first, the best vector autoregressive (VAR(p)) model will be built based on Akaike's Information Criterion (AIC). Based on the best VAR(p) model, granger-causality analysis is discussed, and for forecasting gasoline prices, a state space model will be developed based on the best VAR(p). State vectors are built based on canonical correlation analysis. Based on the results of granger causality analysis, gasoline prices in Indonesia are affected by past gasoline prices in Vietnam; gasoline prices in Malaysia are affected by past gasoline prices in Indonesia and Vietnam. The results of forecasting analysis for the next 12 months using the state space model show that gasoline prices in Indonesia for the next 12 months tend to have a downward trend; gasoline prices in Malaysia for the next 12 months tend to have an upward trend; and the price of gasoline in Vietnam for the next 12 months tends to have an upward trend for the first 6 months and then has a downward trend for the next 6 months.
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Ali, Mostafa, Gang Sun, and Mohammed Ali Arshad Chowdhury. "Dynamic Interaction Between Macroeconomic Fundamentals and Stock Prices in Bangladesh." Indonesian Journal of Management and Business Economics 1, no. 1 (January 26, 2018): 66. http://dx.doi.org/10.32455/ijmbe.v1i1.53.
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This study attempts to investigate whether dynamics in fundamental macroeconomic factors significantly influence the stock prices of Bangladesh by applying cointegration test, Granger causality test based on the Vector Error Correction Model (VECM), Variance Decomposition and Impulse Response Analysis. Johansen and Juselius cointegration test detect six cointegrating vectors and a short-run and long-run relationship is investigated by normalizing the first cointegrating vector corresponding to the largest Eigen-value. We find a long-run positive relationship between stock price and IP, CPI, EX, and RT but a negative relationship between stock price and M2 and interest rate (both TB & GB). Empirical findings of this study reveal that no macroeconomic variables except TB Granger cause stock price in short run. Variance Decomposition analysis shows that most of the stock price variance can be explained by its own shocks in the shorter horizon but its magnitude diminishes over the long horizon which is about 26.77% after 24 months. Therefore, empirical results suggest that stock prices are weakly exogenous relative to the macroeconomic variables. Findings of the study have important implications to market participants and financial analysts when they have chosen to invest in Bangladesh stock market.
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Roman, Monika, Aleksandra Górecka, and Joanna Domagała. "The Linkages between Crude Oil and Food Prices." Energies 13, no. 24 (December 11, 2020): 6545. http://dx.doi.org/10.3390/en13246545.
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This paper aims to indicate the linkages between crude oil prices and selected food price indexes (dairy, meat, oils, cereals, and sugar) and provide an empirical specification of the direction of the impact. This paper reviews the fuel–food price linkage models with consideration to the time series literature. This study adopts several methods, namely the Augmented Dickey–Fuller test, Granger causality test, the cointegration test, the vector autoregression model, and the vector error correction model, for studying the price transmission among the crude oil and five selected food groups. The data series covers the period between January 1990 and September 2020. The empirical results from the paper indicate that there are long-term relationships between crude oil and meat prices. The linkage of crude oil prices occurred with food, cereal, and oil prices in the short term. Furthermore, the linkages between the analyzed variables increased in 2006–2020.
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Pai, Ping-Feng, and Wen-Chang Wang. "Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices." Applied Sciences 10, no. 17 (August 23, 2020): 5832. http://dx.doi.org/10.3390/app10175832.
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Real estate price prediction is crucial for the establishment of real estate policies and can help real estate owners and agents make informative decisions. The aim of this study is to employ actual transaction data and machine learning models to predict prices of real estate. The actual transaction data contain attributes and transaction prices of real estate that respectively serve as independent variables and dependent variables for machine learning models. The study employed four machine learning models-namely, least squares support vector regression (LSSVR), classification and regression tree (CART), general regression neural networks (GRNN), and backpropagation neural networks (BPNN), to forecast real estate prices. In addition, genetic algorithms were used to select parameters of machine learning models. Numerical results indicated that the least squares support vector regression outperforms the other three machine learning models in terms of forecasting accuracy. Furthermore, forecasting results generated by the least squares support vector regression are superior to previous related studies of real estate price prediction in terms of the average absolute percentage error. Thus, the machine learning-based model is a substantial and feasible way to forecast real estate prices, and the least squares support vector regression can provide relatively competitive and satisfactory results.
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Baranowski, Paweł, and Aleksandra Hałka. "Inflacja importowana w Polsce." Wiadomości Statystyczne. The Polish Statistician 2012, no. 8 (August 28, 2012): 44–54. http://dx.doi.org/10.59139/ws.2012.08.3.
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This article attempts to assess the impact of import price index on prices of goods and services (total CPI) in Poland and the producer price in the domestic market (domestic PPI). Based on monthly data, dependences were examined according to both long-term (price analysis, using the method co-integrative) and short-term (monthly analysis of price dynamics, using a vector error correction model). The study shows that both producer prices and the consumer react with a considerable delay to changes in import prices. Long-term effect of elasticity of import prices on producer prices is more than double than on consumer price and amounted to approximately 0,5. Moreover, for both measures, the long-term impact of domestic prices was stronger than in the short term.
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Algahtani, Goblan J. "The Effect of Oil Price Shocks on Economic Activity in Saudi Arabia: Econometric Approach." International Journal of Business and Management 11, no. 8 (July 20, 2016): 124. http://dx.doi.org/10.5539/ijbm.v11n8p124.
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<p>This paper is attempt to investigate the effect of oil price shocks on the Saudi's economic activity using annual data (1970-2015) to cover all of oil price shocks; particularly the recent decline in oil prices amid 2014. The vector autoregressive (VAR) and vector error correction model (VECM) were utilized to investigate the long-run and the short-run relationships between variables. The findings suggest a positive and significant relationship between oil prices and the Saudi's GDP in the long run. </p><p> </p>
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Gunay, Samet. "Fractionally Cointegrated Vector Autoregression Model: Evaluation of High/Low and Close/Open Spreads for Precious Metals." SAGE Open 8, no. 4 (October 2018): 215824401881264. http://dx.doi.org/10.1177/2158244018812649.
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Daily high/low and close/open prices are the key parameters of candlestick approach in technical analysis. Besides, the price spreads are also important as they represent an upward or a downward trend. In this study, we investigate the relationship between daily high/low prices and close/open prices for precious metals: gold, copper, palladium, and silver. Empirical analysis has been performed through fractionally cointegrated vector autoregression (FCVAR) model. To observe the relationships, the trends are tested for their characteristics in both states: positive and negative spreads in close/open prices. Results indicated that for a positive trend, high/low spreads have a negative impact on close/open spreads in long run relationship. However, when closing price is less than the opening prices, it is revealed that expanding range in high/low spreads causes a rise in close/open spreads for copper and silver differently from gold price.
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Akbar, Muhammad Wahyu, Sri Mulatsih, and Sahara Sahara. "Analysis Of Factors Affecting Price Movements Indonesia Stock Exchange Industrial Classification." Agregat: Jurnal Ekonomi dan Bisnis 7, no. 1 (April 30, 2023): 10–28. http://dx.doi.org/10.22236/agregat_vol7/is2pp10-28.
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This study aims to analyze the impact of movements in exchange rates, world gold price, world crude oil price and Covid-19 cases on Indonesia Stock Exchange Industrial Classification (IDX-IC). This study uses Vector Autoregression/ Vector Error Correction Model with daily data from January 26, 2021, to April 28, 2022. The results of this study show that exchange rate has a positive effect on industrial, transportation and logistics sector. Then, world gold price has a negative effect on several sectors, namely energy, industry, health and finance. Covid-19 case has a negative effect on transportation and logistics sector. The impulse response function test results show that each sector responded differently to shocks to exchange rates, world gold prices, world crude oil prices and Covid-19 cases. However, all sectors responded to these shocks in the second period and returned to a stable point in the fifth. The results of forecast error variance decomposition test show that the sectoral stock price index contributes the most for itself, followed by world gold prices, exchange rates, Covid-19 cases and world crude oil prices.
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Raji, Rahman olanrewaju. "Exchange Rate Pass Through in a Small Open Economy: A case study of West African Monetary Zone." Journal of Global Economy 9, no. 4 (December 28, 2013): 275–90. http://dx.doi.org/10.1956/jge.v9i4.301.
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The study investigated the magnitude of exchange rate pass through to import prices and domestic prices   (consumer price index) in WAMZ economy using quarterly time-series data between 2000 and 2010 with the aids of Vector autoregressive (VAR) modeling technique supported with Johansen co-integration approach cross country analysis comprising of Gambia, Ghana, Nigeria and Sierra-Leone. The study discovered that transmission of exchange rate to import prices is more when compared with consumer price in the zone while the contributions of exchange rate to import price are not less 13 percent at average in entire zone. Consumer price index was explained by exchange rate pass through with an average of 26 percent in the zone where the pass through to consumer price is less than two percent in Ghanaian economy. The Taylor (2000) hypothesis was observed in the study where Ghana and Nigeria are the outlier economies while Nigeria established a positive relationship between interest rate volatility and exchange rate pass through to import prices.
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Chang, Dongfeng, and Apostolos Serletis. "OIL, UNCERTAINTY, AND GASOLINE PRICES." Macroeconomic Dynamics 22, no. 3 (August 18, 2016): 546–61. http://dx.doi.org/10.1017/s1365100516000249.
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In this paper we investigate the relationship between crude oil and gasoline prices and also examine the effect of oil price uncertainty on gasoline prices. The empirical model is based on a structural vector autoregression that is modified to accommodate multivariate GARCH-in-Mean errors. We use monthly data for the United States over the period from January 1976 to September 2014. We find that there is an asymmetric relationship between crude oil and gasoline prices, and that oil price uncertainty has a positive effect on gasoline price changes. Our results are robust to alternative model specifications and alternative measures of the price of oil.
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Verma, Neetu, Sujoy Das, and Namita Srivastava. "Multiple kernel support vector regression for pricing nifty option." International Journal of Applied Mathematical Research 4, no. 4 (September 29, 2015): 488. http://dx.doi.org/10.14419/ijamr.v4i4.5023.
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<p>The goal of present experiments is to investigate the use of multiple kernel learning as a tool for pricing options in the context of Indian stock market for Nifty index options. In this paper, fair price of an option is predicted by Multiple Kernel Support Vector Regression (MKLSVR) using linear combinations of kernels and Single Kernel Support Vector Regression (SKSVR). Prices of option highly depend on different money market conditions like deep-in-the-money, in-the-money, at-the-money, out-of-money and deep-out-of-money condition. The experimental study attempts to identify the forecasting errors with the help of mean square error; root meant square error, and normalized root meant square error between the market option prices and the calculated option prices by model for all market conditions. The results reflect that multiple kernel support vector regression performed fairly well in comparison to support vector regression with single kernel.</p>
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Babula, Ronald A., and David A. Bessler. "The Corn-Egg Price Transmission Mechanism." Journal of Agricultural and Applied Economics 22, no. 2 (December 1990): 79–86. http://dx.doi.org/10.1017/s1074070800001838.
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Abstract A vector autoregression (VAR) model of corn, farm egg, and retail egg prices is estimated and shocked with a corn price increase. Impulse responses in egg prices, t-statistics for the impulse responses, and decompositions of forecast error variance are presented. Analyses of results provide insights on the corn/egg price transmission mechanism and on how corn price shocks pulsate through the egg-related economy.
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Krisna, Bayu, Firmansyah Firmansyah, and Fachroerrozi Hoesni. "Analisis Integrasi Pasar Spasial Harga Daging Sapi di Provinsi Jambi." J-MAS (Jurnal Manajemen dan Sains) 6, no. 2 (October 27, 2021): 374. http://dx.doi.org/10.33087/jmas.v6i2.299.
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This study aims to analyze the spatial market integration of beef prices in supporting the stabilization of prices for basic livestock products in Jambi Province. Spatial Market Integration Analysis Beef prices in Jambi Province use secondary data which is weekly time series data for the period 2018 – 2021 (August). The data analyzed in this study is the price of beef in the markets of Jambi City and Bungo Regency. The analytical method used to see the level of spatial integration of the beef market in supporting the stabilization of livestock staples both in the short and long term in Jambi Province is the VAR (Vector Autoregression) / VECM (Vector Error Correction Model) model. The study concludes that the weekly average beef price in Jambi City and Bungo Regency during the period 2018 to 2021 (August) is cointegrated. There is a long-term relationship between weekly average beef prices in Jambi City and Bungo Regency. The VECM model is more accurate in forecasting the weekly average beef price in Jambi City and Bungo Regency in the future.
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Luppold, William G., and Jeffrey P. Prestemon. "Tests for Long-Run Relationships in Hardwood Lumber Prices." Forest Science 49, no. 6 (December 1, 2003): 918–27. http://dx.doi.org/10.1093/forestscience/49.6.918.
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Abstract Hardwood lumber prices are unique because of the large number of marketable species and variability of prices across species. Previous research showed that long-run fashion decisions regarding species selection may be influenced by price, so the interaction between fashion and species price may act to keep prices (hence, demand) of different hardwood species together in the long run. To test this hypothesis, we examined the joint lumber price behavior of six major hardwood species representing different appearance characteristics in the Appalachian hardwood region. Bivariate and multivariate price cointegration tests within lumber grades of these mainly nonstationary price series, conducted using a consistent vector error-correction rank and lag-order model selection procedure, revealed no stable long-run statistical relationships, rejecting the principal null hypothesis. Current relative price levels therefore cannot be used to infer future relative levels. Supplementary vector autoregressions of mostly differenced series, however, indicate that some interspecies price relationships exist. Such relationships, however, were mostly confined within appearance groups and only rarely across groups.
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Pokrivčák, J., and M. Rajčaniová. "Crude oil price variability and its impact on ethanol prices." Agricultural Economics (Zemědělská ekonomika) 57, No. 8 (August 23, 2011): 394–403. http://dx.doi.org/10.17221/42/2010-agricecon.
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The world annual biofuel production has exceeded 100 billion litres in 2009. The development of the biofuel production is partly influenced by the government support programs and partly by the development of oil prices. The main purpose of this paper is to analyze the statistical relationship between ethanol, gasoline and crude oil prices. We aim to check the correlation among these variables and to analyze the strength and direction of a possible linear relationship among the variables. We are interested in analyzing how each variable is related to another, so we evaluate the inter-relationship among the variables in the Vector Autoregression (VAR) and the Impulse Response Function (IRF). In order to achieve our goal, we first collected weekly data for each variable from January, 2000 to October, 2009. The results provide evidence of the cointegration relationship between oil and gasoline prices, but no cointegration between ethanol, gasoline and ethanol, oil prices. As a result, we used a VAR model on first differences. After running the Impulse Response Function, we found out that the impact of the oil price shock on the other variables is considerable larger than vice versa. The largest impact of oil price shock was observed on the price of gasoline.
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Bentsos, Christos, Demetris Koursaros, Kyriaki G. Louka, Konstantinos D. Melas, and Nektarios A. Michail. "Liquefied Natural Gas Prices and Their Relationship with a Country’s Energy Mix: A Case Study for Greece." Energies 16, no. 22 (November 13, 2023): 7554. http://dx.doi.org/10.3390/en16227554.
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Using daily data, we investigate the relationship between European LNG prices, carbon prices (CO2), electricity wholesale prices and changes in the electricity sector’s energy mix in Greece, using a vector error correction model (VECM). The results indicate that an increase in the daily average price of natural gas has the expected impact on Greece’s wholesale electricity price. As expected, gas and other fossil fuels act as substitute goods, while higher imports of electricity lower prices and have a negative impact on fossil fuel shares. Interestingly, carbon prices do not appear to have any significant impact on any variables, while the higher production of electricity from renewable sources pushes wholesale electricity prices down.
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Taliki, Sunarto, Ivo Colanus Rally Drajana, and Andi Bode. "SUPPORT VECTOR MACHINE BERBASIS CHI SQUARE UNTUK PREDIKSI HARGA BERAS ECER KABUPATEN POHUWATO." JOURNAL OF SCIENCE AND SOCIAL RESEARCH 5, no. 2 (July 12, 2022): 436. http://dx.doi.org/10.54314/jssr.v5i2.899.
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One of the staple foods for most Indonesians is rice. Rice is one of the staple foods most consumed by the people of Indonesia, the need for rice is also increasing, considering the very large and scattered population of Indonesia. The ups and downs of rice prices also have an impact on farmers because of their large production. The solution to dealing with uncertain changes in the retail price of rice is to predict prices. One way to find out the estimated retail price of rice is to make predictions using the Support Vector Machine algorithm using Chi Square. The results of the experiments that have been carried out, the prediction of rice prices has been successfully carried out. The smallest error rate in the Support Vector Machine algorithm model is RMSE 733,061. Then the proposed model approaches the value of perfection, because the comparison of the experimental results of rice price predictions produces an average accuracy value of 95.82%. Thus, the proposed method is declared successful.
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Gričar, Sergej, and Štefan Bojnec. "Prices of short-stay accommodation: time series of a eurozone country." International Journal of Contemporary Hospitality Management 31, no. 12 (December 9, 2019): 4500–4519. http://dx.doi.org/10.1108/ijchm-01-2019-0091.
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Purpose This paper aims to provide a reliable statistical model for time-series prices of short-stay accommodation and overnight stays in a eurozone country. Design/methodology/approach Exploiting the unit root feature, the cointegrated vector autoregressive model solves the problem of misspecification. Subsequently, variables are modelled for a long-run equilibrium with included deterministic variables. Findings The empirical results confirmed that overnight stays for foreign tourists were positively associated with the prices of short-stay accommodation. Research limitations/implications The major limitation lies in the data vector and its time horizon; its extension could provide a more specific view. Practical implications Findings can assist practitioners and hotel executives by providing the information and rationale for adopting seasonal volatility pricing. Structural breaks in price time-series have practical implications for setting seasonal-pricing schemes. Tourists could benefit either from greater price stability or from differentiated seasonal prices, which are important in the promotion of the price attractiveness of the tourist destination. Originality/value The originality of the paper lies in the applied unit root econometrics for tourism price time-series modelling and the prediction of short-stay accommodation prices.
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ASAD KHAN, ABDUL QADIR SHAH, ZIA UR REHMAN, and MUHAMMAD IBRAHIM KHAN. "The Conundrum of Oil Prices, Stock Returns and Exchange Rate." Journal of Business & Tourism 3, no. 2 (November 5, 2021): 11–29. http://dx.doi.org/10.34260/jbt.v3i2.68.
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This study imperially investigated the impact of oil prices and exchange rate on stock returns over the period of demand driven oil shock from 2001 to 2008 and supply driven oil shock from 2009 to 2016. To further explore the variation due to frequency of data, the study used daily, weekly and monthly data. The data was analyzed by applying Johansen Cointegration test, Vector error correction model, Granger causality test and Impulse response function. The Johansen Cointegration and vector error correction models confirm the long run relationship between oil prices and stock returns in all six samples. In short run, oil prices and exchange rate are not associated with the changes in stock returns. However, during demand driven oil price shocks, results confirm bidirectional relationship between oil prices and stock return.
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Kusumawati, Yupie, Karis Widyatmoko, and Candra Irawan. "Gold Price Prediction Using Support Vector Regression." Journal of Applied Intelligent System 7, no. 1 (May 19, 2022): 89–102. http://dx.doi.org/10.33633/jais.v7i1.6124.
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In this modern era, one of the businesses that continues to grow is investment. Gold has a more stable value. In Indonesia, there are futures exchange companies that offer gold investment with an online transaction system (E-Trade). The amount of demand and supply, the rate of inflation, economic conditions, and many more can affect the high and low prices of gold. Due to changes in the conditions above, the price of gold may increase, decrease, or remain constant every day. The price of gold that can go up and down causes the need for gold price predictions so that future gold trading investment prospects can be seen. In this final project, the accuracy of Support Vector Regression will be investigated to find out how accurate it is in predicting gold prices with High, Low, Open, Close, and Volume variables. Based on the calculation of the best RMSE in the study, it was found that the best RMSE was to use a Linear kernel with a C of 35 and using a Y variable dataset of 7.4615. The Support Vector Regression Algorithm can predict quite well, as evidenced by the acquisition of fairly good RMSE results. It is necessary to do a simulation of buying and selling gold based on the prediction results and comparing the advantages of the testing data and the actual data.
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Choi, Mun-Seong. "A Study on Price Leadership of Marine Fuel Oil in East Asia." Korea Association for International Commerce and Information 25, no. 3 (September 30, 2023): 71–87. http://dx.doi.org/10.15798/kaici.2023.25.3.71.
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This study analyzes the presence of price leadership of marine fuel oil market(Korea, Japan, Hong Kong, and Singapore) in the East Asia using cointegration test and vector error correction model (VECM) and examine to determine whether the prices of marine fuel oil are set based on international oil prices. As a result of the analysis, cointegration relationships exist among marine fuel oil prices in the East Asia. Results of a weak exogeneity test to examine long-run price leadership show that the marine fuel price of Singapore lead the prices among ports in East Asia in the long-run, and when Dubai international oil prices are included, Dubai oil price leads the prices of fuel oil markets in East Asia. Results of the Granger causality show that Korea-Japan and Japan-Hong Kong have a two-way causality, resulting in short-run price competition between them. When including the Dubai oil price, it was found that the Dubai oil price unilaterally affects the marine fuel prices of all ports in East Asia in the short-run.
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EL QALLI, YASSINE. "RECURSIVE BAYESIAN ESTIMATION IN FORWARD PRICE MODELS IMPLIED BY FAIR PRICING." International Journal of Theoretical and Applied Finance 13, no. 02 (March 2010): 301–33. http://dx.doi.org/10.1142/s0219024910005784.
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In this paper we describe a recursive Bayesian algorithm for the estimation of forward price models. The forward price is modeled within the benchmark framework for a forward price volatility function which includes a stochastic variable; a forward price with a liquidly traded maturity. A relationship between the bond price, the spot price and certain forward prices is stated. We set up the stochastic real world dynamics for these discretely compounded market observed forward prices. We propose a dynamic Bayesian estimation algorithm for a Monte Carlo time-discretized version of the resulting forward prices dynamics. The parameter to be estimated is a vector consisting of the forward price volatility parameters and the benchmarked bond price volatility parameters.
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Cahyono, Rokhmad Eko, Judi Prajetno Sugiono, and Suhatati Tjandra. "Analisis Kinerja Metode Support Vector Regression (SVR) dalam Memprediksi Indeks Harga Konsumen." JTIM : Jurnal Teknologi Informasi dan Multimedia 1, no. 2 (August 30, 2019): 106–16. http://dx.doi.org/10.35746/jtim.v1i2.22.
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The stability of commodity prices for food is very influential on the economy of a region because stable prices have a direct impact on the level of people's purchasing power. The need to maintain the stability of food commodity prices is the background of this research and this can be anticipated by forecasting the Consumer Price Index (CPI). The CPI is an index number that calculates the average change in prices of goods and services consumed by households and society. The purpose of this study is to predict the CPI of the Foodstuff Group using the Support Vector Regression (SVR) method by utilizing Linear, Polynomial, Gaussian Radial Basis Function (RBF) and SPLine Kernel Functions. Selection of this SVR method, because SVR is able to map input vectors into higher dimensions and can produce a function with a trend of bumpy data following the data path formed, resulting in more accurate predictive values Research is limited to the city of Surabaya, the period of time the study begins January 1, 2016 until December 31, 2018. The data source used is the Surabaya Food Basic Price data as an input variable with 34 input attributes and the CPI data for Surabaya city Foodstuffs group as output variables. The results of this study are expected to be able to predict CPI with an error rate below 5%, which is indicated by MSE (Mean Square Error) < 0.05
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Penone, Carlotta, and Samuele Trestini. "Testing for asymmetric cointegration of Italian agricultural commodities prices: Evidence from the futures-spot market relationship." Agricultural Economics (Zemědělská ekonomika) 68, No. 2 (February 18, 2022): 50–58. http://dx.doi.org/10.17221/226/2021-agricecon.
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The volatility of food prices still raises concerns among agricultural market players, increasing interest in the futures markets, thus calling for a better understanding of the connection between the futures and the Italian spot prices. This study uses symmetric and asymmetric vector error correction models to investigate the relationship between futures and spot prices for the Italian agricultural markets of soybean, corn, and milling wheat. The results confirm the leading role of the futures contract prices for all the considered commodities. Moreover, the non-linear cointegration analysis results suggest price transmission's asymmetries for all the agricultural commodity prices. This research provides critical insight into the shape of the futures-spot price transmission.
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Xu, Pei, Todd Lone, and Naydith Torres. "Market Integration and Price Discovery in California’s Almond Marketing: A Vector Auto-Regressive (VAR) Approach." International Journal of Business and Management 17, no. 9 (August 3, 2022): 43. http://dx.doi.org/10.5539/ijbm.v17n9p43.
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California almonds production and marketing have been the focus of the state’s economy. Since almonds are a high-cost food product facing high market price volatility, reducing price forecasting error increases the likelihood of success (profitability) at the farm level. By focusing on the linkage between the local wholesale inshell price from 2015 to 2021 and international export prices to major trading partners in Europe and Eastern Asian countries, this study contributes to understanding how export prices affect the farm level wholesale price and what causes price shocks in the system. A clear result of this study is farmers can rely on current market price when forecasting local almond price in the short run of upcoming two months. This study also finds the California local almond wholesale market is integrated into the world almond market, as well as the markets of its trading partners in Europe and East Asia. Specifically, when U.S. export price to the world increase in the current month, its export price to East Asian countries will automatically adjust and decrease in the following months. Lastly, analysis of the sample of prices considered in this study does not establish a long-run equilibrium nor market price integration.
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Yang, Guangye. "Does Rising Commodity Prices Pose an Inflation Risk." BCP Business & Management 23 (August 4, 2022): 223–29. http://dx.doi.org/10.54691/bcpbm.v23i.1354.
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In the context of rising commodity prices, this paper mainly analyzes the transmission between domestic upstream and downstream prices and the impact of commodity prices on upstream and downstream prices, and aims to study whether rising commodity prices will cause inflation risks. In this paper, a Vector Autoregressive Model (VAR) is constructed by using time series data of RMPI, PPI, CGPI, CPI and commodity prices in China, and it is founded through impulse response analysis that the upper and midstream prices in China have a significant dynamic transmission effect on the downstream prices, while the downstream prices have a reverse transmission mechanism against the midstream price and the midstream price on the upstream price.Based on this, this paper believes that there is an indirect dynamic transmission mechanism between the rise in commodity prices and the risk of inflation, and there is a transmission time delay, and it is necessary to pay close attention to the structural changes in consumer prices. Finally, relevant policy recommendations were proposed.
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Baek, Jungho, and Won W. Koo. "Price Dynamics in the North American Wheat Market." Agricultural and Resource Economics Review 35, no. 2 (October 2006): 265–75. http://dx.doi.org/10.1017/s1068280500006717.
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Perron's test, Johansen cointegration analysis, and a vector error-correction (VEC) model are used to identify structural change, as well as to examine price dynamics in the U. S. and Canadian hard red spring (HRS) and durum wheat markets. It is found that, due to the U. S. Export Enhancement Program (EEP), price instability experienced in June 1986 has resulted in structural changes for Canadian HRS and durum prices. We also find that Canadian prices have significant effects on the determination of the U. S. prices in the North American wheat market.
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Mushi, Vianey John. "Housing Finance and Markets Dynamics in Tanzania: An Analysis of Cross-sector Linkages." JOURNAL OF AFRICAN REAL ESTATE RESEARCH 5, no. 1 (June 1, 2020): 16–31. http://dx.doi.org/10.15641/jarer.v5i1.800.
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This study examines whether feedback from housing price shocks factored into the availability of mortgage credit in Tanzania between 2008 and 2018. This was done by estimating a Vector Error Correction Model (VECM) with mortgage financing and using three measures of house pricing trends in the luxury, mid-end and economy sub-markets as dependent variables. Results showed that mortgage credit expansion is related to housing price growth in the long-run, but the impact mostly ran from housing price shocks to mortgage growth. In the short-term, changes in price for luxury houses led to a mortgage growth in the first quarter after the shocks, which in turn stimulated changes in housing prices. However, variations on mortgage credit flows had a more significant short-term impact on prices of housing units than it did for houses priced on mortgage credit. The dynamic response between mortgage credit flow and housing prices disappeared when housing price indicators for the economy and mid-end sub-markets were used in the analysis. In addition, both mortgage credit and housing markets were highly persistent, but the effect of previous shocks lasted longer in the mortgage lending process. The paper concludes that the substantial increase in housing prices might be a major concern for policymakers, in particular, because it foreshadows a mortgage crisis.
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Rustam, Zuherman, and Puteri Kintandani. "Application of Support Vector Regression in Indonesian Stock Price Prediction with Feature Selection Using Particle Swarm Optimisation." Modelling and Simulation in Engineering 2019 (April 21, 2019): 1–5. http://dx.doi.org/10.1155/2019/8962717.
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Stock investing is one of the most popular types of investments since it provides the highest return among all investment types; however, it is also associated with considerable risk. Fluctuating stock prices provide an opportunity for investors to make a high profit. We can see the movement of groups of stock prices from the stock index, which is called Jakarta Composite Index (JKSE) in Indonesia. Several studies have focused on the prediction of stock prices using machine learning, while one uses support vector regression (SVR). Therefore, this study examines the application of SVR and particle swarm optimisation (PSO) in predicting stock prices using stock historical data and several technical indicators, which are selected using PSO. Subsequently, a support vector machine (SVM) was applied to predict stock prices with the technical indicator selected by PSO as the predictor. The study found that stock price prediction using SVR and PSO shows good performances for all data, and many features and training data used by the study have relatively low error probabilities. Thereby, an accurate model was obtained to predict stock prices in Indonesia.
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Özdemir, Letife. "Causality Relationship between Spot and Futures Bitcoin Prices in CME." Journal of corporate governance, insurance and risk management 8, no. 2 (May 15, 2021): 158–69. http://dx.doi.org/10.51410/jcgirm.8.2.11.
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To protect against risks arising from fluctuations in spot prices and better manage risk, investors might evaluate futures markets. The role of price discovery in the futures markets and the possibility of reducing certain risks increase the importance of researching the relationship between spot and futures prices. This study aims to determine whether there is a relationship between the Bitcoin spot prices and the Bitcoin futures prices. To this end, the relationship between the two markets is analyzed using Johansen Cointegration analysis and Vector Error Correction Model (VECM) using the daily data of the period 02.23.2017 – 08.31.2021. Unit root tests show that each series are not stationary at the level values and that the first differences of the series are stationary. The results of the cointegration analysis show that there is a long-term equilibrium relationship between the bitcoin spot market and the bitcoin futures market, and it is a single cointegration vector. The Granger causality test based on the vector error correction model was used to determine the causality relationship between the series. It has been determined that there is a unidirectional causality relationship from the Bitcoin spot market to the Bitcoin futures market. Bitcoin is a new financial tool that attracts the attention of investors. Investors make transactions on Bitcoin for speculative purposes. Therefore, unlike other investment instruments, spot prices in the bitcoin market affect futures prices.
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Atmaja, Dinul Darma, Widowati Widowati, and Budi Warsito. "FORECASTING STOCK PRICES ON THE LQ45 INDEX USING THE VARIMAX METHOD." MEDIA STATISTIKA 14, no. 1 (March 8, 2021): 98–107. http://dx.doi.org/10.14710/medstat.14.1.98-107.
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Forecasting using the Autoregressive Integrated Moving Average (ARIMA) method is not appropriate to predict more than one stock price because this method is only able to model one dependent variable. Therefore, to expect more than one stock prices, the ARIMA method expansion can be used, namely the Vector Autoregressive Integrated Moving Average (VARIMA) method. Furthermore, this research will discuss forecasting stock prices on the LQ45 index using the Vector Autoregressive Integrated Moving Average with Exogenous Variable (VARIMAX) method. Then, after the initial model formation process, the best model is the VARIMAX (0,1,2) model. Finally, the results of this study using the VARIMAX (0,1,2) model obtained the predictive value of the prices and the error values of stocks on the LQ45 index.
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Acquah, Beverly. "An Empirical Analysis of Macroeconomic Variability on the Ghanaian Stock Market: A Vector Autoregression Approach." International Finance and Banking 3, no. 2 (August 29, 2016): 49. http://dx.doi.org/10.5296/ifb.v3i2.9821.
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This study investigates the dynamic interrelationships among stock prices and selected macroeconomic indicators namely; economic activity, global commodity price index, inflation and interest rates in Ghana. By employing a Vector Autoregression (VAR) Model, the empirical results reveal that stock prices depreciate with an increase in global commodity prices and interest rates indicating a negative relationship. On the other hand, stock prices appreciate with an increase in inflation and economic activity indicating a positive relationship. Examining stock market variability on the selected macroeconomic variables also showed that inflation and interest rates respond negatively to changes in asset prices while the stock market itself is not found to be a leading indicator for economic activity. The evidence suggests that the listed equities on the GSE are a hedge against inflation in Ghana. Increasing economic activity over time is advantageous for the Ghanaian stock market.
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Rahmania, Septia Tri, and Ali Anis. "Dampak Guncangan Harga Minyak Dunia Terhadap Dinamika Inflasi di Indonesia." Jurnal Kajian Ekonomi dan Pembangunan 6, no. 1 (March 1, 2024): 13. http://dx.doi.org/10.24036/jkep.v6i1.15834.
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This study aims to identify and analyze the impact of world oil price shocks on inflation dynamics in Indonesia. This research is a quantitative research using secondary data with quarterly data from 1993-2022. The analysis used is descriptive analysis and inductive analysis. In inductive analysis there are several tests, namely: Vector Auto Regression (VAR) Analysis, Empirical Models Vector Auto Regression (VAR) Analysis, Vector Auto Regression (VAR) Analysis Steps such as: Stationarity Test, Cointegration Test, Optimum Lag, Model Stability Test, Impulse Response Function (IRF), Variance Decomposition. The results of this study indicate that (1) world oil prices have a significant influence on the dynamics of inflation in Indonesia. This means that world oil prices can affect the dynamics of inflation in Indonesia, especially in the short term. (2) Shocks to world oil prices also have an impact on aggregate demand and supply of world oil which then affect the dynamics of the national macro economy which in turn will cause changes in the inflation rate in Indonesia
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Zhu, Yingjie, Jiageng Ma, Fangqing Gu, Jie Wang, Zhijuan Li, Youyao Zhang, Jiani Xu, Yifan Li, Yiwen Wang, and Xiangqun Yang. "Price Prediction of Bitcoin Based on Adaptive Feature Selection and Model Optimization." Mathematics 11, no. 6 (March 9, 2023): 1335. http://dx.doi.org/10.3390/math11061335.
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Bitcoin is one of the most successful cryptocurrencies, and research on price predictions is receiving more attention. To predict Bitcoin price fluctuations better and more effectively, it is necessary to establish a more abundant index system and prediction model with a better prediction effect. In this study, a combined prediction model with twin support vector regression was used as the main model. Twenty-seven factors related to Bitcoin prices were collected. Some of the factors that have the greatest impact on Bitcoin prices were selected by using the XGBoost algorithm and random forest algorithm. The combined prediction model with support vector regression (SVR), least-squares support vector regression (LSSVR), and twin support vector regression (TWSVR) was used to predict the Bitcoin price. Since the model’s hyperparameters have a great impact on prediction accuracy and algorithm performance, we used the whale optimization algorithm (WOA) and particle swarm optimization algorithm (PSO) to optimize the hyperparameters of the model. The experimental results show that the combined model, XGBoost-WOA-TWSVR, has the best prediction effect, and the EVS score of this model is significantly better than that of the traditional statistical model. In addition, our study verifies that twin support vector regression has advantages in both prediction effect and computation speed.
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Aliu, Florin, Jiří Kučera, and Simona Hašková. "Agricultural Commodities in the Context of the Russia-Ukraine War: Evidence from Corn, Wheat, Barley, and Sunflower Oil." Forecasting 5, no. 1 (March 22, 2023): 351–73. http://dx.doi.org/10.3390/forecast5010019.
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The Russian invasion of Ukraine on 24 February 2022 accelerated agricultural commodity prices and raised food insecurities worldwide. Ukraine and Russia are the leading global suppliers of wheat, corn, barley and sunflower oil. For this purpose, we investigated the relationship among these four agricultural commodities and, at the same time, predicted their future performance. The series covers the period from 1 January 1990 to 1 August 2022, based on monthly frequencies. The VAR impulse response function, variance decomposition, Granger Causality Test and vector error correction model were used to analyze relationships between variables. The results indicate that corn prices are an integral part of price changes in wheat, barley and sunflower oil. Wheat prices are also essential but with a weaker influence than that of corn. The additional purpose of this study was to forecast their price changes ten months ahead. The Vector Autoregressive (VAR) and Vector Error Correction (VECM) fanchart estimates an average price decline in corn, wheat, barley and sunflower oil in the range of 10%. From a policy perspective, the findings provide reliable signals for countries exposed to food insecurities and inflationary risk. Recognizing the limitations that predictions maintain, the results provide modest signals for relevant agencies, international regulatory authorities, retailers and low-income countries. Moreover, stakeholders can become informed about their price behavior and the causal relationship they hold with each other.
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Bernal, Bruno, Juan Carlos Molero, and Fernando Perez De Gracia. "Impact of fossil fuel prices on electricity prices in Mexico." Journal of Economic Studies 46, no. 2 (March 4, 2019): 356–71. http://dx.doi.org/10.1108/jes-07-2017-0198.
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Purpose The purpose of this paper is to examine the impact of fossil fuel prices – crude oil, natural gas and coal – on different electricity prices in Mexico. The use of alternative variables for electricity price helps to increase the robustness of the analysis in comparison to previous empirical studies. Design/methodology/approach The authors use an unrestricted vector autoregressive model and the sample covers the period January 2006 to January 2016. Findings Empirical findings suggest that crude oil, natural gas and coal prices have a significant positive impact on electricity prices – domestic electricity rates – in Mexico in the short run. Furthermore, crude oil and natural gas prices have also a significant positive impact on electricity prices – commercial and industrial electricity rates. Originality/value Two are the main contributions. First, this paper explores the nexus among crude oil, natural gas, coal and electricity prices in Mexico, while previous studies focus on the US, UK and some European economies. Second, instead of using one electricity price as a reference of national or domestic electricity sector, the analysis considers alternative Mexican electricity prices.
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Andani, Gina, and Mahrus Lutfi Adi Kurniawan. "Analisis Variabel Makroekonomi terhadap Indeks Saham Kompas 100: Pendekatan VECM." Journal of Advances in Accounting, Economics, and Management 1, no. 3 (March 31, 2024): 1–17. http://dx.doi.org/10.47134/aaem.v1i3.216.
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This study aims to determine the effect of world gold prices, interest rates, inflation, exchange rates, and the LQ45 Index on the stock price of the Kompas 100 index. This research was conducted using research data in 2015-2020 and analyzed using the Vector Error Correction Model approach. From the VECM estimation results, it is found that world gold prices, interest rates, inflation, exchange rates, and the LQ45 index affect the Kompas 100 index stock price in the long term. Meanwhile, in the short term, world gold prices, interest rates, inflation, exchange rates, and the LQ45 index have no effect on the stock price of the Kompas 100 index.
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Rohimuddin and Jihad Lukis Panjawa. "THE IMPACT OF FOOD COMMODITY PRICES ON INFLATION IN BEKASI." MARGINAL JOURNAL OF MANAGEMENT ACCOUNTING GENERAL FINANCE AND INTERNATIONAL ECONOMIC ISSUES 2, no. 1 (September 15, 2022): 193–206. http://dx.doi.org/10.55047/marginal.v2i1.376.
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This research aims to examine the influence of price fluctuations on volatile food commodities such as rice, chilies, and meat in Bekasi. This research is quantitative study with the use of secondary data taken from Strategic Food Price Information Center in Bekasi in the form of commodity food prices from all markets. The data taken are meat, chilies, and rice price data in all markets in Bekasi City in the 2018-2022 period. The data analysis method used in this study is Vector Autoregression (VAR) analysis. Based on the results obtained that in the short term there are several variables that affect inflation in Bekasi City inflation two months earlier, inflation five months earlier, inflation eight months earlier, rice prices one month earlier, rice prices three months earlier, rice prices four months earlier, rice prices seven months earlier, cayenne pepper prices 2 months earlier, cayenne pepper prices 5 months earlier, the price of cayenne pepper 8 months in advance, the price of beef 1 month in advance, the price of beef 3 months in advance, the price of beef 4 months in advance and the price of beef 6 months in advance. Meanwhile, long-term price inflation is also influenced by the price of rice, meat, and chilies.
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Prabowo, Eddy, Harianto Harianto, Bambang Juanda, and Dikky Indrawan. "Dynamic Relationship of Macro Variables and Liquefied Petroleum Gas Subsidy Transformation Program." Binus Business Review 14, no. 2 (June 6, 2023): 133–45. http://dx.doi.org/10.21512/bbr.v14i2.8557.
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Most Indonesians rely on liquefied petroleum gas as one of their primary sources of energy. Liquefied petroleum gas is classified into subsidized and non-subsidized. Subsidized liquefied petroleum gas is primarily used by low-income households, small businesses, and poor fishermen and farmers for cooking. However, no exit strategy has been established to overcome the increase in government spending on subsidized kerosene introduced in 2008. The problem is that macro variables may influence liquefied petroleum gas economic prices. The research aimed to identify the relationship between macro variables that might affect liquefied petroleum gas economic prices. It applied a quantitative method with Vector Auto Regression (VAR) and Vector Error Correction Model (VECM). The results demonstrate that inflation rate have a significant impact on the economic price of liquefied petroleum gas. Then, gross domestic product, inflation rate, and world gas price have positive correlations to the economic prices in liquefied petroleum gas. Meanwhile, currency exchange and world oil price have negative coefficients. The regression model indicates that a rise in inflation increases market prices in liquefied petroleum gas. Furthermore, the increased subsidized fuel means more poor people cannot afford liquefied petroleum gas. It is because high inflation reduces purchasing and potentially increases the number of poor people.
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Paramita, Desak Putu Ristami, Nunung Nuryartono, and Noer Azam Achsani. "ANALISIS FAKTOR YANG MEMPENGARUHI HARGA DAN INTEGRASI HARGA OLEIN." JURNAL EKONOMI DAN KEBIJAKAN PEMBANGUNAN 4, no. 1 (February 4, 2018): 28–48. http://dx.doi.org/10.29244/jekp.4.1.2015.28-48.
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Olein production increased by 107.5 percent from 2002 to 2013. There was a change in consumption patterns where the consumption of olein intended for export has risen from only 39 percent in 2002 to 65 percent in 2013. In the beginning of 2008, olein prices increased due to the global financial crisis. In the end of 2008, olein prices decreased but since then olein prices fluctuations until the end of 2014. Many factors affecting the price fluctuations such as macroeconomic and microeconomic variables. Commodity market participants need to take action in response to price fluctuations by participating in commodity futures trading. Olein futures trading commodity in Indonesia is not well developed. This is indicated by small volumes of the transaction of olein futures contracts in Indonesia Commodity and Derivatives Exchange (ICDX) causing market participants to not using ICDX futures prices as a reference. The participants actually use the price of the Rotterdam exchange for their transactions of buying and selling. Therefore, this study aims to analyze factors influencing olein prices and analyze olein prices integration by using Vector Error Correction Model (VECM) method. Results showed that exchange rates, interest rates, money supply, CPO prices, and Indonesia's GDP affect olein prices. In addition, there is an integration between the physical prices, futures prices, and world reference prices in the long term. Key words : Factors Affecting Price, Olein, Price Integration, VECM
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Paramita, Desak Putu Ristami, Nunung Nuryartono, and Noer Azam Achsani. "ANALISIS FAKTOR YANG MEMPENGARUHI HARGA DAN INTEGRASI HARGA OLEIN." JURNAL EKONOMI DAN KEBIJAKAN PEMBANGUNAN 4, no. 1 (February 4, 2018): 28–48. http://dx.doi.org/10.29244/jekp.4.1.28-48.
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Olein production increased by 107.5 percent from 2002 to 2013. There was a change in consumption patterns where the consumption of olein intended for export has risen from only 39 percent in 2002 to 65 percent in 2013. In the beginning of 2008, olein prices increased due to the global financial crisis. In the end of 2008, olein prices decreased but since then olein prices fluctuations until the end of 2014. Many factors affecting the price fluctuations such as macroeconomic and microeconomic variables. Commodity market participants need to take action in response to price fluctuations by participating in commodity futures trading. Olein futures trading commodity in Indonesia is not well developed. This is indicated by small volumes of the transaction of olein futures contracts in Indonesia Commodity and Derivatives Exchange (ICDX) causing market participants to not using ICDX futures prices as a reference. The participants actually use the price of the Rotterdam exchange for their transactions of buying and selling. Therefore, this study aims to analyze factors influencing olein prices and analyze olein prices integration by using Vector Error Correction Model (VECM) method. Results showed that exchange rates, interest rates, money supply, CPO prices, and Indonesia's GDP affect olein prices. In addition, there is an integration between the physical prices, futures prices, and world reference prices in the long term. Key words : Factors Affecting Price, Olein, Price Integration, VECM
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Mardiyanto, Ilyas Cahaya, and Panji Kusuma Prasetyanto. "PENGARUH HARGA TANAMAN PANGAN TERHADAP INFLASI DI KABUPATEN KENDAL." TRANSEKONOMIKA: AKUNTANSI, BISNIS DAN KEUANGAN 3, no. 1 (January 3, 2023): 98–109. http://dx.doi.org/10.55047/transekonomika.v3i1.346.
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This study aims to analyze the effect of food crop prices on inflation in Kendal Regency. The increasing demand for agricultural commodities has resulted in the price of agricultural commodities increasing due to the high demand. The impact arising from the increase in the price of agricultural commodities is an increase in inflation, which causes a decrease in people's purchasing power because prices are increasingly unaffordable. The data used in this study is time series data for the period 2016 to 2022, the data used is secondary data on monthly developments in food crop prices and inflation in Kendal Regency. The tool used in this research is the analysis of the VAR/VECM (Vector Autoregression (VAR) or Vector Error Correction Model (VECM)) model with the help of the Eviews 10 application. The findings show that a number of variables, including the price of garlic, large red chilies, and Red cayenne pepper has a significant effect on inflation in Kendal Regency in the long run. In the short term, several variables such as the price of shallots and red cayenne pepper also have a significant effect on inflation in Kendal Regency.
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Yu, Huayi, and Yanfen Huang. "Regional heterogeneity and the trans-regional interaction of housing prices and inflation: Evidence from China’s 35 major cities." Urban Studies 53, no. 16 (July 21, 2016): 3472–92. http://dx.doi.org/10.1177/0042098015617882.
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This paper proposes a theoretical framework to analyse the regionally heterogeneous responses of housing prices and inflation to the monetary aggregates shock and the trans-regional interaction of housing prices and inflation, which has seldom been discussed in previous literature. Using a GVAR (Globe Vector Autoregression) model, evidence based on China’s 35 major cities for this framework is provided. The results show that (1) the housing price shocks have weak positive influence on CPIs (consumer price index); (2) the housing price shocks, especially the shocks in first-tier cities and eastern cities, have strong positive influence on domestic housing price dynamics and housing prices of other cities; (3) monetary aggregates shock has strong influence on the housing prices of first-tier cities and eastern cities, while weak influence on that of central and western cities. CPIs are barely influenced by monetary aggregates shocks. The empirical results are in accordance with the theoretical explanation. Based on empirical results, this paper proposes policy recommendations for stabilising housing prices.
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Subhani, Muhammad Imtiaz. "Monetary Shocks or Real Shocks, Which matters the most for Share Prices." Information Management and Business Review 2, no. 6 (June 15, 2011): 246–51. http://dx.doi.org/10.22610/imbr.v2i6.904.
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This study examines that out of monetary shocks (∆M2) and real shocks in share prices (∆Yt-k), which one or both really explain share prices of Karachi stock exchange 100 index. The time series econometrics is used to investigate the data for the monthly period of January 1991 to January 2011 for money supply (M2) and share prices of KSE 100 index. The results of unit root test reveal that there is a real shock in share prices and it explains the share price of KSE 100 index temporarily, while Vector auto regression revealed that Share prices of KSE 100 index is meagerly explained by the monetary shocks.
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Ausubel, Lawrence M. "An Efficient Dynamic Auction for Heterogeneous Commodities." American Economic Review 96, no. 3 (May 1, 2006): 602–29. http://dx.doi.org/10.1257/aer.96.3.602.
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This article proposes a new dynamic design for auctioning multiple heterogeneous commodities. An auctioneer wishes to allocate K types of commodities among n bidders. The auctioneer announces a vector of current prices, bidders report quantities demanded at these prices, and the auctioneer adjusts the prices. Units are credited to bidders at the current prices as their opponents' demands decline, and the process continues until every commodity market clears. Bidders, rather than being assumed to behave as price-takers, are permitted to strategically exercise their market power. Nevertheless, the proposed auction yields Walrasian equilibrium prices and, as from a Vickrey-Clarke-Groves mechanism, an efficient allocation.
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Bergmann, Dennis, Declan O’Connor, and Andreas Thümmel. "Price and volatility transmission in, and between, skimmed milk powder, livestock feed and oil markets." Outlook on Agriculture 46, no. 4 (December 2017): 248–57. http://dx.doi.org/10.1177/0030727017744928.
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Price and volatility transmission effects between European Union (EU) and World skimmed milk powder (SMP) prices, as well as those between both SMP series, soybeans and crude oil prices from 2004 to 2014 were analysed using a vector error correction model combined with a multivariate GARCH model. The results show significant transmission effects between EU and World SMP prices, but no significant transmission effects from soybeans or crude oil to either of the SMP prices. For policymakers and modellers, these results indicate the need to consider World SMP prices when considering EU prices. On the other hand, the finding of no transmission effects from soybean to SMP prices reduces the opportunity for a successful cross-hedging for dairy commodities using well-established soybean derivative markets.