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1
Huang, Shaojia, Yisen Zhu, Jingde Huang, Enguang Zhang, and Tao Xu. "Analysis of Circular Price Prediction Strategy for Used Electric Vehicles." Sustainability 16, no. 13 (July 5, 2024): 5761. http://dx.doi.org/10.3390/su16135761.
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As the car price war has intensified in China from 2023, the continuous decline in prices of new cars for both conventional fuel vehicles and electric vehicles (EVs) has led to a sharp decline in used cars. In particular, the EV market appears more vulnerable as the prime cost of battery raw materials has decreased since January 2023. And thus, a second-hand EV price prediction system is urgent. This study compares several methods for used EVs in China. We find that the random forest method and the gradient boosting regression tree (GBRT) method have good effects on predicting used EV prices in respecting price ranges. Timed EV data capture is applied to guarantee the real-time property of our prediction system. Then, we propose the concept of circular pricing, which means that the obsolete data for the priced car will be repriced according to the latest data. In this way, such a system can guide the used car dealers to adjust the price in time.
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Ahmadi, Ahmadi, and R. Adisetiawan. "Multivariate Time Series in Macroeconomics." Eksis: Jurnal Ilmiah Ekonomi dan Bisnis 11, no. 2 (November 23, 2020): 151. http://dx.doi.org/10.33087/eksis.v11i2.209.
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Gold is one of the most popular commodities and investment alternatives. Gold prices are thought to be influenced by several other factors such as the US Dollar, oil price, inflation rate, and stock exchange so that gold price modeling is not only influenced by its own value. This research was conducted to determine the best forecasting model and to find out what factors influence the price of gold. This research modeled the price of gold in a multivariate and reviewed the univariate modeling that will be used as a comparison model of multivariate modeling. Univariate modeling is done using ARIMA model where the modeling results state that gold price fluctuations as white noise. Multivariate gold price modeling is done using Vector Error Correction Model with gold, oil, US Dollar and Dow Jones indices, and inflation rate as predictors. The results showed that the VECM model has been able to model the gold price well and all the factors studied influenced the gold price. The US dollar and oil prices are negatively correlated with gold prices, while the inflation rate is positively correlated with gold prices. The Dow Jones index was positively correlated with gold prices in just two periods.
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Wang, Diankai, Inna Gryshova, Mykola Kyzym, Tetiana Salashenko, Viktoriia Khaustova, and Maryna Shcherbata. "Electricity Price Instability over Time: Time Series Analysis and Forecasting." Sustainability 14, no. 15 (July 25, 2022): 9081. http://dx.doi.org/10.3390/su14159081.
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Competition in electricity markets leads to volatile conditions which cause persistent price fluctuations over time. This study explores the problem of electricity pricing fluctuations in the DE-LU bidding zone from October 2018 to March 2022 by applying time series analysis. The determinants of electricity price fluctuations are broken down into three groups: exogenous prices (gas, coal and CO2 prices), internal (consumption and generation) and external (net import between neighboring bidding zones) electricity flows. Based on the SARIMAX model, we tried to combine all these factors to forecast electricity prices in the single bidding zone. It was found that the SARIMAX (1, 1, 2) × (3, 1, 0, 7) model with exogenous prices, internal and external electricity flows, which has the lowest AIC and MAPE values, is the best-fitted model for the DE-LU bidding zone. Anonymous trading and unpredictable individual bidding strategies lead to persistent price volatility, which causes electricity prices to deviate from fundamental trends. To reveal the risk factors, the SARIMAX model of electricity prices needs to be supplemented with a GARCH model of the residual returns. For forecasting electricity price residual volatility in the DE-LU bidding zone, the SARIMAX model with exogenous prices, internal and external electricity flows must be accompanied with the GARCH (7, 0) model.
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Kim, Dong-Hwan, and Jin Kim. "Price Prediction Analysis in Seoul APT Market Using Time Series Model." Korea Real Estate Society 71 (March 30, 2024): 193–209. http://dx.doi.org/10.37407/kres.2024.42.1.193.
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The purpose of this study is to make a model to forecasting apartment trade price and household rent price in apartment housing market using ARIMA model. On the basis of those models, I tried to forecast the fluctuations of short-term apartment trade price and household rent price in Seoul apartment market. To analyze the ARIMA model, quarterly data during 2011 1/4∼2023 4/4 are used for identification, estimation, diagnosis, and prediction of the ARIMA model. Using ARIMA model, the outcome ARIMA(1,1,0) model is applied to Sepol apartment trade price in the rate of apartment trade price forecasting model, and ARIMA(1,1,1) model is applied to Seoul apartment household rent price in the rate of apartment rent price. According to the forecast results of Seoul apartment trade price and apartment household rent price in the ARIMA model, looking at the quarterly rate of change in Seoul apartment sales prices, it appears that they will continue to go up the 2024 1/4, and will continue to rise but without significant fluctuations. Looking at the quarterly trend of Seoul apartment rental prices on the 2024 1/4 and continue until the 2024 4/4, and the upward trend will be larger than Seoul apt sale price. While the government's housing policy is sluggish, Seoul apartment sales prices and Jeonse prices are expected to be affected by a decrease in apartment supply and interest rates due to a decrease in permits and occupancy this year.
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Curry, David J., and Peter C. Riesz. "Prices and Price/Quality Relationships: A Longitudinal Analysis." Journal of Marketing 52, no. 1 (January 1988): 36–51. http://dx.doi.org/10.1177/002224298805200104.
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Though price and quality are recognized as important tactical and strategic variables for a marketing manager, few empirical data are available on the behavior of price or the correspondence between price and quality over time. The authors report results for three hypotheses derived from product life cycle theory, dynamic pricing policy, and economic information theory about price trends, price convergence, and the correspondence between price and quality among brands in 62 durable product forms. Results strongly confirm the hypotheses that prices converge as well as decrease in real terms. The decline in price variation apparently results from a narrowing of prices by all relevant competitors. Brands entering or exiting a category counterbalance one another and are nearly as likely to be priced below as above a category mean. Reduced correspondence between price and quality levels over time suggests that as pricing flexibility declines, competition may occur in the form of promotional expenditures rather than relative quality improvements. Implications of these findings for marketing strategy and consumer welfare are discussed.
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Cheruvu, Sai Manoj. "Stock Price Prediction Using Time Series." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 375–81. http://dx.doi.org/10.22214/ijraset.2021.39296.
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Abstract: Predicting Stock price of a company has been a challenge for analysts due to the fluctuations and its changing nature with respect to time. This paper attempts to predict the stock prices using Time series technique that proposes to observe various changes in a given variable with respect to time and is appropriate for making predictions in financial sector [1] as the stock prices are time variant. Keywords: Stock prices, Analysis, Fluctuations, Prediction, Time series, Time variant
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Yao, Jun, and Harmen Oppewal. "Unit pricing matters more when consumers are under time pressure." European Journal of Marketing 50, no. 5/6 (May 9, 2016): 1094–114. http://dx.doi.org/10.1108/ejm-03-2015-0122.
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Purpose This paper aims to first investigate how unit pricing affects consumers’ grocery purchase decisions and perceptions of the shopping task’s information load. The second goal is to test how time pressure enhances the behavioural and perceptual effects of displaying unit prices. Design/methodology/approach Two on-line experiments were conducted using national samples of shoppers. In Study 1, participants indicated their choices and perceptions in an inter-brand shopping scenario where prepackaged products have conflicting positions on retail price and unit price. In Study 2, participants conducted the same shopping task but now under a condition of time pressure. Findings Study 1 shows that unit pricing shifts consumer choices towards the lower unit priced options and improves their perceptions of task information load. Study 2 shows that when consumers are under time pressure, unit pricing shows stronger effects on choices but not on perceptions. Research limitations/implications The study comprised a fairly homogenous set of low involvement categories and relatively small assortments in a hypothetical purchase setting. Exploration of the role of unit pricing in more complex and more realistic purchase environments pose suitable avenues for future research. Practical implications This study shows that consumers benefit from unit pricing because it makes it easier for them to find the lower unit priced items and to more quickly complete their shopping task. Retailers will benefit from increased customer satisfaction and possibly an improved store image. Social implications The study shows that consumers generally benefit from the presence of unit pricing and that unit price information does not create harmful effects in terms of increasing their information load. Originality/value This study uses a specifically designed and controlled but nevertheless realistic grocery choice task to study the effects of unit pricing in an inter-brand context where there are only small differences in size and price. The study contributes to the literature by showing that in such conditions, unit prices help consumers compare the economic losses associated with product options. Their heuristic role is more pronounced when consumers are under time pressure. The study shows that consumers generally benefit from the presence of unit prices.
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Trofimov, G. "Competitive Storage and Commodity Price in Continuous Time." Higher School of Economics Economic Journal 26, no. 4 (2022): 523–51. http://dx.doi.org/10.17323/1813-8691-2022-26-4-523-551.
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Lee, Yun-Hong. "Effect of Changes in Fertility Rate and Demographic Structure on Housing Prices: Centering on Dongtan New Town." Korean Association for Housing Policy Studies 30, no. 3 (August 31, 2022): 113–39. http://dx.doi.org/10.24957/hsr.2022.30.2.113.
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As a result of analyzing the influence of variables related to the demographic structure, it was found that the price of apartments traded at a time when the total fertility rate was high, the lower the price. In the case of acetic age, the price of apartments traded at a time when the acetic age was higher was found to be lower. It was found that the ratio of single-person households had a negative effect on apartment transaction prices, and the ratio of double-person households had a positive effect on apartment transaction prices.
These results suggest that transaction prices tend to fall as apartments traded at a time when the number of single-person households composed of unmarried or students increases.
On the contrary, it was found that apartments traded at a time when more double-person households move in are more likely to rise in price. The elderly ratio was found to have no significant effect on apartment transaction prices, but the higher the price of apartments traded at a time when more people in their 60s moved in. In addition, it was found that the high proportion of people in their 60s moved in and apartment prices rose during the period when their transactions were concentrated, mainly in high-priced apartment complexes. These results suggest that changes in fertility rates and demographic structure have a significant impact on apartment prices in new cities. Therefore, it seems important to predict changes in the fertility rate and demographic structure of the region when deciding on apartment supply policies to prevent soaring apartment prices.
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Doucouliagos, Chris. "Price exhaustion and number preference: time and price confluence in Australian stock prices." European Journal of Finance 11, no. 3 (June 2005): 207–21. http://dx.doi.org/10.1080/1351847042000254194.
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Bulfone, Liliana. "High prices for generics in Australia — more competition might help." Australian Health Review 33, no. 2 (2009): 200. http://dx.doi.org/10.1071/ah090200.
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It is commonly believed that dispensed prices of medicines in Australia are substantially lower than those in other developed countries, particularly the US. This article reports the results of an analysis comparing dispensed prices for the most commonly prescribed and the highest cost items in Australia with dispensed prices in the US. Although a large majority of items are less expensive in Australia than in the US, Australian prices are higher for a substantial number of products, particularly generic drugs. This article examines various policies affecting the pricing of generics in Australia. It is postulated that the main cause for higher prices for a substantial number of generic products is the lack of price competition. This results from government policy which ensures that a price reduction by one company is communicated immediately to all competitors in that market along with an invitation to match the reduced price. The dominant strategy for all suppliers is to only reduce their price in response to a reduction in price by a competitor. The result is a lack of differentiation in pricing across brands of a medicine on the Schedule of Pharmaceutical Benefits. The government could improve the structure of the generics market and encourage greater competition by ceasing to disclose competitor firms? offers to other competitors. The government could conduct pricing reviews of each generic product relatively infrequently (eg, only once annually or every 18 months). At the time of the pricing review, the government would request confidential offers on price for a generic from all players in the market. Brands should then all be listed under the Pharmaceutical Benefits Scheme (PBS) at the offered price. Prices offered by the individual supplier would apply until the next pricing review. The PBS would continue to subsidise up to the price of the lowest priced brand, with brand premiums applying to all brands priced higher than the benchmark price. Such an approach would provide opportunity for players in the market to capture market share by being the lowest priced brand.
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Sehman, Sehman. "Utilization Of Fuzzy Time Series Method Of Analyzing Changes In Rice Prices Throughout The Year In East Java." NEWTON: Networking and Information Technology 3, no. 3 (July 24, 2024): 4–10. http://dx.doi.org/10.32764/newton.v3i3.3959.
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The research aims to analyze the vertical transmission of rice selling prices from the producer (miller) level to the consumer and analyze what factors influence the formation of rice prices at the producer (miller) level in east java province. The main data used is 2023 data from january to september 2023 with a sample of each rice price in the district/city in east java province, while for the analysis of price formation factors using monthly data on rice prices at the producer (miller) level, rice prices in consumer level, rice consumption, price of imported rice 2. The method used in this research is a descriptive research method with a quantitative approach. The analysis tool used is fuzzy time series. The research results found that the rice price transmission pattern in east java province was symmetrical. In the short term, the rice price transmission pattern is asymmetric, meaning that there is a delay or indirect price transmission mechanism between producers and consumers, and in the long term, this means that consumer prices are transmitted directly to producer prices. (milling). Factors that influence the price of rice at the milling level in east java province are consumer prices, imported rice prices, rice consumption, and climate.
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Chen, Xi, and Michael Funke. "Real-Time Warning Signs of Emerging and Collapsing Chinese House Price Bubbles." National Institute Economic Review 223 (February 2013): R39—R48. http://dx.doi.org/10.1177/002795011322300105.
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The recent increase in Chinese house prices has led to concerns that China is vulnerable to asset price shocks. In this paper, we apply recently developed recursive unit root tests to spot the beginning and the end of potential speculative bubbles in Chinese house price cycles. Overall, we find that except for 2009–10 actual house prices are not significantly disconnected from fundamentals. Thus, the evidence for speculative house price bubbles in China is in general weak.
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Helmi, Mohamad Husam, Abdurrahman Nazif Çatık, Çağla Bucak, Esra Ballı, and Coşkun Akdeniz. "Time-Varying Income and Price Elasticities of Oil Demand in OECD Countries." International Journal of Energy Economics and Policy 14, no. 6 (November 1, 2024): 303–11. http://dx.doi.org/10.32479/ijeep.15895.
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This study examines the long-run income and price elasticities of oil demand in 21 OECD countries using quarterly data from 1980:Q1 to 2021:Q3. We find that oil demand is inelastic with respect to both income and prices at 0.117 and −0.179, respectively. The cointegration tests reveal instability in oil price elasticities over time. The time-varying panel data estimates support these findings, showing significant variations in elasticities influenced by oil market dynamics and global events. Income elasticities reached their highest levels during the COVID-19 pandemic, while price elasticities ranged from −0.396 to 0.275. Significantly, the sign of oil price elasticities shifted from negative to positive after 2015, contrary to the law of demand, probably because of declining oil prices during that period. The largest positive and statistically significant price elasticity occurred in early 2020, which can be attributed to the COVID-19-induced decline in oil prices. Overall, this analysis contributes to understanding oil demand dynamics and highlights the impact of economic and oil market factors on elasticities.
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Michaillat, Pascal, and Emmanuel Saez. "Aggregate Demand, Idle Time, and Unemployment *." Quarterly Journal of Economics 130, no. 2 (February 8, 2015): 507–69. http://dx.doi.org/10.1093/qje/qjv006.
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Abstract This article develops a model of unemployment fluctuations. The model keeps the architecture of the general-disequilibrium model of Barro and Grossman (1971) but takes a matching approach to the labor and product markets instead of a disequilibrium approach. On the product and labor markets, both price and tightness adjust to equalize supply and demand. Since there are two equilibrium variables but only one equilibrium condition on each market, a price mechanism is needed to select an equilibrium. We focus on two polar mechanisms: fixed prices and competitive prices. When prices are fixed, aggregate demand affects unemployment as follows. An increase in aggregate demand leads firms to find more customers. This reduces the idle time of their employees and thus increases their labor demand. This in turn reduces unemployment. We combine the predictions of the model and empirical measures of product market tightness, labor market tightness, output, and employment to assess the sources of labor market fluctuations in the United States. First, we find that product market tightness and labor market tightness fluctuate a lot, which implies that the fixed-price equilibrium describes the data better than the competitive-price equilibrium. Next, we find that labor market tightness and employment are positively correlated, which suggests that the labor market fluctuations are mostly due to labor demand shocks and not to labor supply or mismatch shocks. Last, we find that product market tightness and output are positively correlated, which suggests that the labor demand shocks mostly reflect aggregate demand shocks and not technology shocks.
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Guan, Xiaodong, Haishaerjiang Wushouer, Mingchun Yang, Sheng Han, Luwen Shi, Dennis Ross-Degnan, and Anita Katharina Wagner. "Influence of government price regulation and deregulation on the price of antineoplastic medications in China: a controlled interrupted time series study." BMJ Open 9, no. 11 (November 2019): e031658. http://dx.doi.org/10.1136/bmjopen-2019-031658.
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BackgroundIn October 2012, the Chinese government established maximum retail prices for specific products, including 30 antineoplastic medications. Three years later, in June 2015, the government abolished price regulation for most medications, including all antineoplastic medications. This study examined the impacts of regulation and subsequent deregulation of prices of antineoplastic medications in China.MethodsUsing hospital procurement data and an interrupted time series with comparison series design, we examined the impacts of the policy changes on relative purchase prices (Laspeyres price index) and volumes of and spending on 52 antineoplastic medications in 699 hospitals. We identified three policy periods: prior to the initial price regulation (October 2011 to September 2012); during price regulation (October 2012 to June 2015); and after price deregulation (July 2015 to June 2016).ResultsDuring government price regulation, compared with price-unregulated cancer medications (n=22, mostly newer targeted products), the relative price of price-regulated medications (n=30, mostly chemotherapeutic products) decreased significantly (β=−0.081, p<0.001). After the government price deregulation, no significant price change occurred. Neither government price regulation nor deregulation had a significant impact on average volumes of or average spending on all antineoplastic medications immediately after the policy changes or in the longer term (p>0.05).ConclusionCompared with unregulated antineoplastics, the prices of regulated antineoplastic medications decreased after setting price caps and did not increase after deregulation. To control the rapid growth of oncology medication expenditures, more effective measures than price regulation through price caps for traditional chemotherapy are needed.
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He, Chenghong. "Examining the Effect of Crude Oil Shock on the U.S. PPI Through Time Series Analysis." Advances in Economics, Management and Political Sciences 19, no. 1 (September 13, 2023): 291–97. http://dx.doi.org/10.54254/2754-1169/19/20230151.
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Nowadays, people are becoming more curious about how changes in oil prices affect inflation, particularly how they impact macroeconomic indicators like the PPI (Producer Price Index) and CPI (Consumer Price Index), due to the ongoing pandemic and war's impact on oil prices. The present study examines the relationship between oil price changes and the PPI in selected industries. Using data from the trucking, chemical manufacturing, semiconductor, and electronic device industries PPI from 2020, with WTI (West Texas Intermediate) as the reference of U.S. oil price. The Pearson correlation coefficient and linear regression model were employed to analyze the correlations and evaluate the impact of oil price changes. The results demonstrate that the PPI for the trucking industry had the strongest correlation with oil prices over the same period, while the PPI for the chemical manufacturing industry demonstrated the strongest relationship with oil prices a month earlier. The PPI for the semiconductor industry displayed a strong correlation with oil prices six months ago. Overall, the transportation industry appears to be highly sensitive to changes in contemporaneous oil prices, and oil prices one month ago have a significant concussion on the current PPI of the chemical lines, and the effect of oil prices on the electronic device industry is weaker and more delayed.
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Zhang, Jian Hua, Fan Tao Kong, Jian Zhai Wu, Meng Shuai Zhu, Ke Xu, and Jia Jia Liu. "Tomato Prices Time Series Prediction Model Based on Wavelet Neural Network." Applied Mechanics and Materials 644-650 (September 2014): 2636–40. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2636.
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Accurate prediction of agricultural prices is beneficial to correctly guide the circulation of agricultural products and agricultural production and realize the equilibrium of supply and demand of agricultural area. On the basis of wavelet neural network, this paper, choosing tomato prices as study object, tomato retail price data from ten collection sites in Hebei province from January, 1st, 2013 to December, 30th, 2013 as samples, builds the tomato price time series prediction model to test price model. As the results show, model prediction error rate is less than 0.01, and the correlation (R2) of predicted value and actual value is 0.908, showing that the model could accurately predict tomatoes price movements. The establishment of the model will provide technical support for tomato market monitoring and early warning and references for related policies.
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Khoirudin, Rifki, and Mahrus Lutfi Adi Kurniawan. "A time-varying of property residential price in Indonesia: a VAR approach." Jurnal Ekonomi & Studi Pembangunan 24, no. 1 (May 16, 2023): 69–80. http://dx.doi.org/10.18196/jesp.v24i1.17750.
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The crisis of 2008 started with asset price bubbles which spread to other sectors, thus driving a recession. Turmoil in the housing sector can directly harm the domestic economy and financial stability. The research aims to analyze macroeconomic variables that can affect asset prices in Indonesia and how the inflation-targeting framework directly affects asset prices. This study contributes to the current research, such as the early warning system for the asset sector that the crisis of 2008 started with asset price bubbles. The Inflation Targeting Framework (ITF) policy used by the Central Bank has shown its effectiveness in the property sector. It can be seen that a negative response is shown from property prices when there are inflationary shocks. The response of interest rates to fluctuations in housing prices is stronger than the response of housing prices to fluctuations in interest rates. It indicates that the interest rate stimulus is more reactive to changes in housing prices as an accommodation of housing price volatility. GDP and money supply will respond negatively to property price fluctuations, which can lead to a crisis because GDP responds negatively. The strengthening of fiscal and monetary policy can soften the volatility of asset prices.
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SAHA, SUBRATA, and MANJUSRI BASU. "INTEGRATED DYNAMIC PRICING FOR SEASONAL PRODUCTS WITH PRICE AND TIME DEPENDENT DEMAND." Asia-Pacific Journal of Operational Research 27, no. 03 (June 2010): 393–410. http://dx.doi.org/10.1142/s0217595910002764.
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In this paper, an inventory model for deteriorating items with ramp-type time and price dependent consumption rate over a finite planning horizon is considered. In contrast to the traditional deterministic inventory model with static price over the entire planning horizon or fixed number of price changes over the finite time horizon, an alternative model is derived in which prices and the number of price change are to be decision variables. We show that the total profit function is concave. With the concavity, a solution procedure is presented to determine optimal prices, optimal number of pricing cycles and optimal lot size and optimal profit. We illustrate the model with numerical examples. Sensitivity analysis of the model is also carried out.
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Linda Sakinah, Rahma Anisa, and I Made Sumertajaya. "Energy Sector Stock Price Forecasting with Time Series Clustering Approach." Indonesian Journal of Statistics and Its Applications 8, no. 2 (December 31, 2024): 132–42. https://doi.org/10.29244/ijsa.v8i2p132-142.
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Stock investment promises higher returns but carries high risks because unpredictable price fluctuations. Energy sector shows potential due to its highest sectoral index growth in 2022. However, this doesn’t indicate that stock price increases occur evenly among all issuers. Therefore, it’s necessary to analyze clustering of issuers based on similarity of their stock price movements and used for forecasting stock prices at cluster level. This study aims to evaluate performance of clustering energy sector issuers using autocorrelation-based distance and dynamic time warping(DTW), and to forecast stock prices at cluster level. The data used consists weekly closing stock prices. The clustering used hierarchical average linkage method. Stock price forecast for each cluster used ARIMA model and its performance was evaluated using rolling-cross validation. The results showed that DTW distance had the best clustering performance. Energy sector issuers were grouped into four clusters with strong cluster category, indicated by silhouette coefficient >0.71. ARIMA models for each cluster produced MAPE values between 10-20%, categorizing them as good forecasting models. Clusters A and D were recommended for investors because have highest potential for capital gain based on forecasted stock prices. That clusters also consisted of companies with strong fundamentals and dividend policies.
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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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Iliychovski, Svetoslav, Teodora Filipova, and Mariana Petrova. "Applied aspects of time series models for predicting residential property prices in Bulgaria." Problems and Perspectives in Management 20, no. 3 (October 4, 2022): 588–603. http://dx.doi.org/10.21511/ppm.20(3).2022.46.
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Accurate housing price forecasts play a critical role in balancing supply and demand in the residential real estate market, as well as in achieving the goals of various stakeholders – buyers, investors, construction contractors, public administration, real estate agencies, special investment purpose companies, etc. The present study aims to investigate the relationship between specific predictors and build a suitable model for forecasting housing prices in Bulgaria. In this regard, a study was conducted on transactions with residential real estate in the city of Sofia for the period from the first quarter of 2016 to the fourth quarter of 2021. The ARIMA model is used in the development to predict the values of the variables. Eight models are tested for the researched factors (24 in total). On this basis, the price per square meter of residential property was predicted, including estimated values from the ARIMA model for the parameters involved in the regression equation. The result showed that there is a strong relationship between the analyzed predictors and the studied variable – price per square meter of housing. The tested models are adequate and the statistical requirements for forecasting the prices of residential properties in Bulgaria are complied.
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Hill, Robert J. "Constructing Price Indexes across Space and Time: The Case of the European Union." American Economic Review 94, no. 5 (November 1, 2004): 1379–410. http://dx.doi.org/10.1257/0002828043052178.
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This paper considers the problem of how to construct and reconcile price indexes across space and time. A general taxonomy of panel price index methods, containing four broad classes, is proposed, along with five criteria for discriminating between them. Methods from each of the four classes are then used to compute spatial and temporal price indexes for the 15 countries of the European Union (EU) over the period 1995–2000. Using these panel price indexes, I test whether or not price levels and relative prices converged across the EU over this period.
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OSTROVSKY, DMITRY. "BLACK–SCHOLES–MERTON IN RANDOM TIME: A NEW STOCHASTIC VOLATILITY MODEL WITH PATH DEPENDENCE." International Journal of Theoretical and Applied Finance 10, no. 05 (August 2007): 847–72. http://dx.doi.org/10.1142/s0219024907004421.
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A generalized Black–Scholes–Merton economy is introduced. The economy is driven by Brownian motion in random time that is taken to be continuous and independent of Brownian motion. European options are priced by the no-arbitrage principle as conditional averages of their classical values over the random time to maturity. The prices are path dependent in general unless the time derivative of the random time is Markovian. An explicit self-financing hedging strategy is shown to replicate all European options by dynamically trading in stock, money market, and digital calls on realized variance. The notion of the average price is introduced, and the average price of the call option is shown to be greater than the corresponding Black–Scholes price for all deep in- and out-of-the-money options under appropriate sufficient conditions. The model is implemented in limit lognormal random time. The significance of its multiscaling law is explained theoretically and verified numerically to be a determining factor of the term structure of implied volatility.
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Du, Wenbin, You Wu, Yunliang Zhang, and Ya Gao. "The Impact Effect of Coal Price Fluctuations on China’s Agricultural Product Price." Sustainability 14, no. 15 (July 22, 2022): 8971. http://dx.doi.org/10.3390/su14158971.
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Few studies have used China’s latest economic data to verify the interaction between coal price fluctuation and vegetable price fluctuation. Therefore, the sharing of existing knowledge in the academic community is mainly reflected in this paper, which explores the influence between coal prices and agricultural product prices for the first time. Further, it supplements the verification of the effective parameters of vegetable price fluctuation in academia. The current study investigates the relationship between coal prices (thermal coal price) and agricultural product prices (vegetable prices) in China from 2016 to 2021. It uses separate time-series models to verify the effect of China’s coal price fluctuation on the price of agricultural products and explores the effect of the coal price on the vegetables’ price trend. The results confirm that the thermal coal price significantly impacts and positively affects vegetable prices. There is also a linkage between the price of coal and the security of agricultural products. It might mainly be due to coal usage in various stages of the growing, storage, transportation, and distribution of agricultural products. Higher coal prices may lead to higher agricultural prices, threatening China’s coal-dominant energy structure. These higher coal prices will endanger domestic energy security and agricultural security. Finally, this study also suggests ways to manage the effect of increased coal prices on agricultural product prices and then puts forward policy suggestions.
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Maia, Emanuella Gomes, Camila Mendes dos Passos, Renata Bertazzi Levy, Ana Paula Bortoletto Martins, Laís Amaral Mais, and Rafael Moreira Claro. "What to expect from the price of healthy and unhealthy foods over time? The case from Brazil." Public Health Nutrition 23, no. 4 (January 15, 2020): 579–88. http://dx.doi.org/10.1017/s1368980019003586.
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AbstractObjective:To measure change in price of food groups over time (1995–2030) in Brazil, considering the Brazilian Dietary Guidelines’ recommendations.Design:Data from the Household Budget Survey (2008–2009 HBS) and the National System of Consumer Price Indexes (NSCPI) were used to create a data set containing monthly prices for the foods and beverages most consumed in the country (n 102), from January 1995 to December 2017. Data on price of foods and beverages from 2008–2009 HBS (referring to January 2009) were used to calculate real price over time using the monthly variation in prices from NSCPI. All prices were deflated to December 2017. Foods and beverages were classified following the Brazilian Dietary Guidelines’ recommendations. The monthly price for each food group and subgroup was used to analyse changes in prices from 1995 to 2017 and to forecast prices up to 2030 using fractional polynomial models.Setting:Brazil.Participants:National estimates of foods and beverages purchased for Brazil.Results:In 1995, ultra-processed foods were the most expensive group (R$ 6·51/kg), followed by processed foods (R$ 6·44/kg), then unprocessed or minimally processed foods and culinary ingredients (R$ 3·45/kg). Since the early 2000s, the price of ultra-processed foods underwent successive reductions, becoming cheaper than processed foods and reducing the distance between it and the price of the other group. Forecasts indicate that unhealthy foods will become cheaper than healthy foods in 2026.Conclusions:Food prices in Brazil have changed unfavourably considering the Brazilian Dietary Guidelines’ recommendations. This may imply a decrease in the quality of the population’s diet.
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Farzanegan, Elham. "Time-varying price discovery in Bahar-e-Azadi Gold Coin spot and futures contracts." Investment Management and Financial Innovations 19, no. 3 (August 18, 2022): 153–66. http://dx.doi.org/10.21511/imfi.19(3).2022.13.
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This paper aims to analyze the daily price discovery of Bahar-e-Azadi Gold Coin (GC) spot and futures contracts in Iran, using the fractionally cointegrated error-correction model (FCECM). The residuals of the FCECM are modeled by the BEKK-GARCH specification to calculate the time-varying conditional information share between GC spot and futures prices. Using data covering December 21, 2008 to April 14, 2018, the paper establishes the novel finding that the GC spot and futures price series are fractionally integrated of orders 0.98347 and 0.95169, respectively. This implies the long memory behavior in the price series. Further, the results show that the series are fractionally cointegrated of order 0.542. The empirical findings from the methodology indicate that in the price discovery process, the GC spot market dominates the GC futures market. This analysis is robust to alternative construction of futures price series and sub-samples decomposed based on structural breaks. One possible explanation could be the higher trading volume associated with the GC spot market compared to the GC futures market. Incompleteness and market frictions also can cause a delay in the process of information incorporation into the futures market and may discourage market players from trading in these markets.
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Failler, Pierre, Yuhang Zheng, Yue Liu, Negar Akbari, Helga Josupeit, Andy Forse, and Benjamin Drakeford. "Time-varying effects of crude oil price fluctuations on tuna fish prices." Sustainable Economies 2, no. 3 (April 18, 2024): 103. http://dx.doi.org/10.62617/se.v2i3.103.
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This research presents an investigation of the time varying effects of crude oil on the price of three Tuna species, namely skipjack, albacore and yellow fin. The investigation analyses the impact coefficient of oil price fluctuation on tuna species over time with specific phases related to time points when crude oil prices fall, including December 2008 (due to the impact of the Financial Crisis), February 2016 (due to the impact of the US shale oil and gas revolution) and April 2020 (due to the impact of the global COVID-19). The analysis shows that the price of yellow fin and skipjack show sensitivity to these phased oil price shocks however stay consistent after recovery. This research finds that the relationship between oil price and Tuna price depend on specific phases of the oil price fluctuations and that global crude oil price shocks could have immediate and short-term impacts on fish markets especially during a period of financial crisis.
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Yoshida, Naohiro. "A micro-foundation of a simple financial model with finite-time singularity bubble and its agent-based simulation." Economics and Business Letters 12, no. 4 (December 15, 2023): 277–83. http://dx.doi.org/10.17811/ebl.12.4.2023.277-283.
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This paper proposes a mathematical model of financial security prices in continuous time with bubbles in which prices may diverge and crash in finite time. Just before the bubbles burst, prices increase super-exponentially. In addition, a discrete-time excess demand model is proposed to provide a micro-foundation for the continuous-time model. The derived discrete-time security price model has the same characteristics as the continuous-time price model and expresses the finite-time singularity. Furthermore, based on the excess demand model, an agent-based simulation is performed to check the price behavior. As expected, we can confirm that prices can diverge in finite time and increase super-exponentially.
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Dewi, Syanti, and Ishak Ramli. "OPSI SAHAM PADA PASAR MODAL DI INDONESIA (STUDI PASAR OPSI SAAT PASAR OPSI MASIH BERLANGSUNG DI BURSA EFEK INDONESIA)." Jurnal Muara Ilmu Ekonomi dan Bisnis 2, no. 2 (March 28, 2019): 300. http://dx.doi.org/10.24912/jmieb.v2i2.1001.
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Stock option exchange market is not working anymore in the Indonesian Stock Exchange, using the data option exchange market for the running period 2007-2008, we analyzed the effect of stock price, strike price, time to maturity, volatility and risk- free interest rate on the stock option’s price of listed stock call or put option trading at the Indonesian Stock Exchange during 2007-2008. The results found that the stock price, strike price, time to maturity, volatility and risk-free interest rate are positive significantly affecting the stock option price either the buying option price or the selling option price in Indonesia Stock Exchange 2007-2008 period. While there were no variables that significantly affected the call option during the periode 2007-2008, furthermore stock prices and strike prices significantly affected the put option prices. Time to maturity, Volatility, and risk free interest rate did not significantly affect the put option prices.That is why the stock option exchange market stop since the investor were not sure to the stock option price versus the risk of the volatility, time to maturity, and riskfree rate.
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Mohd Azman, Nur Azrina, Md Pauzi Abdullah, Mohammad Yusri Hassan, Dalila Mat Said, Faridah Hussin, Norzanah Rosmin, and Siti Maherah Hussin. "Impact of Different Time of Use Electricity Pricing Structure on Residential Consumer." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 3 (June 1, 2018): 1053. http://dx.doi.org/10.11591/ijeecs.v10.i3.pp1053-1060.
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<span>Load profile for residential users is different from commercial users where peak load occurs outside of work hours compared to working hours. Consequently, the Time of Use-based electricity price must be different not only in terms of price, but also in terms of time block structure. This paper examines the impacts of different TOU structures on TOU prices and load profiles of residential consumer. Four TOU structures are tested on the real load profile for a selected residential consumer area in Malaysia. Two elasticity factors are used for each structure to represent two different groups of users, a group that responds highly to price changes and a group that does not. The TOU price set for each structure is determined optimally subject to the following constraints; the price difference between the TOU and a fixed price per hour should be minimized and the amount of difference between price increase and price drop should be equal. From the analysis, the TOU structure with 12-time blocks provides better price signals and peak load reduction.</span>
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Zhao, Lu-Tao, Shun-Gang Wang, and Zhi-Gang Zhang. "Oil Price Forecasting Using a Time-Varying Approach." Energies 13, no. 6 (March 17, 2020): 1403. http://dx.doi.org/10.3390/en13061403.
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The international crude oil market plays an important role in the global economy. This paper uses a variable time window and the polynomial decomposition method to define the trend term of time series and proposes a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices. First, to characterize the time-varying characteristics of crude oil price trends, the basic concepts of post-position intervals, pre-position intervals and time-varying windows are defined. Second, a crude oil price series is decomposed with a time-varying window to determine the best fitting results. The parameter vector is used as a time-varying trend. Then, to quantitatively describe the continuation of the time-varying trend, the concept of the trend threshold is defined, and a corresponding algorithm for selecting the trend threshold is given. Finally, through the predicted trend thresholds, the historical reference data are selected, and the time-varying trend is combined to complete the crude oil price forecast. Through empirical research, it is found that the time-varying trend prediction model proposed in this paper achieves a better prediction than several common models. These results can provide suggestions and references for investors in the international crude oil market to understand the trends of oil prices and improve their investment decisions.
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HULT, HENRIK, FILIP LINDSKOG, and JOHAN NYKVIST. "A SIMPLE TIME-CONSISTENT MODEL FOR THE FORWARD DENSITY PROCESS." International Journal of Theoretical and Applied Finance 16, no. 08 (December 2013): 1350048. http://dx.doi.org/10.1142/s0219024913500489.
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In this paper, a simple model for the evolution of the forward density of the future value of an asset is proposed. The model allows for a straightforward initial calibration to option prices and has dynamics that are consistent with empirical findings from option price data. The model is constructed with the aim of being both simple and realistic, and avoid the need for frequent re-calibration. The model prices of n options and a forward contract are expressed as time-varying functions of an (n + 1)-dimensional Brownian motion and it is investigated how the Brownian trajectory can be determined from the trajectories of the price processes. An approach based on particle filtering is presented for determining the location of the driving Brownian motion from option prices observed in discrete time. A simulation study and an empirical study of call options on the S&P 500 index illustrate that the model provides a good fit to option price data.
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AL-Moders, Ali Hussein, and Tasnim H. Kadhim. "Bayesian Structural Time Series for Forecasting Oil Prices." Ibn AL- Haitham Journal For Pure and Applied Sciences 34, no. 2 (April 20, 2021): 100–107. http://dx.doi.org/10.30526/34.2.2631.
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There are many methods of forecasting, and these methods take data only, analyze it, make a prediction by analyzing, neglect the prior information side and do not considering the fluctuations that occur overtime. The best way to forecast oil prices that takes the fluctuations that occur overtime and is updated by entering prior information is the Bayesian structural time series (BSTS) method. Oil prices fluctuations have an important role in economic so predictions of future oil prices that are crucial for many countries whose economies depend mainly on oil, such as Iraq. Oil prices directly affect the health of the economy. Thus, it is necessary to forecast future oil price with models adapted for emerging events. In this article, we study the Bayesian structural time series (BSTS) for forecasting oil prices. Results show that the price of oil will increase to 156.2$ by 2035.
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Shabanov, Timofey Yu. "Time deals: optimization of lag." Vegetable crops of Russia, no. 5 (November 7, 2019): 94–97. http://dx.doi.org/10.18619/2072-9146-2019-5-94-97.
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Relevance. In many ways, a successful purchase and sale transaction is determined by the time it takes to complete it. The search for the best deal time is linked to the optimization of the time lag. The purpose of the study is to optimize the time lag of the agricultural market.Methods. This article considered a role of time in business processes for Russian potato market of producer into period 1998-2018.Results. There was trend of annual reduction about 365 thousand tons (1.2...1.7% of total) per year supply of potatoes on Russian domestic market of producers. Some priority of quantity vs price of producer been revealed. There was inverse proportional function between producers` prices and quantity of potatoes. That is than worse harvest that higher producer`s price. Reducing a supply of potatoes 1 million tons from agricultural market perhaps increase in producer prices by $ 8.5 / t (4...5% on average). Offered concept and hypothesis about linearization of equilibrium of market processes into conditions of time shifts of dynamics their parameters were confirmed. There verified assumption about existence of inter-interval time optimum which this linearization maximal business processes. In course of correlation analysis of dynamics of annual indicators of potato market, it revealed month May of current period as optimal time of producer's offers with price restrictions and diminishing returns, and month September with quantity restrictions and increasing returns. If fixation of producer`s prices situation of deals May more likely, in case of crop failure (limited quantity) in September.
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Niyimbanira, Ferdinand. "Fuel price and exchange rate dynamics in South Africa: A time series analysis." Corporate Ownership and Control 12, no. 4 (2015): 185–93. http://dx.doi.org/10.22495/cocv12i4c1p2.
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This paper empirically examined the relationship between fuel price and exchange rate in South Africa. Monthly data spanning over the period of January 2001 to December 2013 was used while adopting the cointegration method. The Augumented Dickey Fuller (ADF) test showed that all variables (Fuel Price, Exchange rate and New Vehicle sales) became stationary after the first difference. The results from Johansen cointegration test indicated no cointegrating equation, indicating that series were not cointegrated.The findings show that fuel price is affected by at least its two previous month prices. Both explanatory variable coefficients (0.541228 and -0.368649), show that fuel price will be increased by 20 cents Rand due to its previous two month prices. The results from impulsive test confirmed VAR test results. This paper provided evidence that there was a causal link from the exchange rates to petrol price during last one sub-period. The implication therefore is that in South Africa an increase of the fuel price is a response to the Rand value fluctuations ceteris paribus. Based on the findings of the study, policy implications and suggestion for future research are made.
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Shapiro, Stephen L., and Joris Drayer. "A New Age of Demand-Based Pricing: An Examination of Dynamic Ticket Pricing and Secondary Market Prices in Major League Baseball." Journal of Sport Management 26, no. 6 (November 2012): 532–46. http://dx.doi.org/10.1123/jsm.26.6.532.
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In 2010, the San Francisco Giants became the first professional team to implement a comprehensive demand-based ticket pricing strategy called dynamic ticket pricing (DTP). In an effort to understand DTP as a price setting strategy, the current investigation explored Giants’ ticket prices during the 2010 season. First, the relationship between fixed ticket prices, dynamic ticket prices, and secondary market ticket prices for comparable seats were examined. In addition, seat location and price changes over time were examined to identify potential effects on ticket price in the primary and secondary market. Giants’ ticket price data were collected for various games throughout the 2010 season. A purposive selection of 12 games, which included (N= 1,316) ticket price observations, were chosen in an effort to include a multitude of game settings. Two ANOVA models were developed to examine price differences based on pricing structure, market, section, and time. Findings showed significant differences between fixed ticket prices, dynamic ticket prices, and secondary market ticket prices, with fixed ticket prices on the low end and secondary market ticket prices on the high end of the pricing spectrum. Furthermore, time was found to have a significant influence on ticket price; however, the influence of time varied by market and seat location. These findings are discussed and both theoretical and practical implications are considered.
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Melching, Konstantin, and Tristan Nguyen. "On the Impact of Dividend Payments on Stock Prices - an Empirical Analysis of the German Stock Market." Studies in Business and Economics 16, no. 1 (April 1, 2021): 255–69. http://dx.doi.org/10.2478/sbe-2021-0020.
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Abstract This paper examines the relation between dividend payments and stock prices of all firms in the German prime standard DAX 30 in the time period from 2012 to 2019. The irrelevance theory introduced by Miller and Modigliani states that dividend payments must not have an impact on stock prices in a perfect market. In contrast, the signaling theory and the dividend puzzle indicate that dividend payments are likely to have a profound impact on the stock price. According to our findings the ex-dividend decrease of stock prices was significantly smaller than the dividend payment. Nevertheless, the results support the impact of the dividend payment on the share price. Firstly, the existence of the ex-dividend markdown is a proof that dividend payments cause share price losses. Secondly, the study explains in particular that high dividend payments result in high share prices over the examined period. Thirdly, our analysis demonstrates a positive correlation between the dividend and the stock price development according to the signaling theory. Considering the above- mentioned results, we can conclude that the share price of a company is highly affected by the decision making of the company regarding the dividend policy.
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Al-adawiyah, Syavira Habib, Evawati Alisah, and Abdul Aziz. "Perbandingan Tingkat Akurasi Metode Average Based Fuzzy Time Series Markov Chain dan Algoritma Novel Fuzzy Time Series." Jurnal Riset Mahasiswa Matematika 1, no. 3 (February 28, 2022): 129–42. http://dx.doi.org/10.18860/jrmm.v1i3.14332.
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Fuzzy time series method can be applied in predicting the situation in food price development data such as rice. The position of rice as a staple food has resulted in this commodity being one of the indicators of economic growth. The importance of suppressing rice prices so that they are stable can be done by forecasting rice prices in Indonesia in the future. The research method used for forecasting is average based fuzzy time series Markov chain and novel algorithms fuzzy time series. Researchers will compare the two methods in the case of rice prices by looking at the level of accuracy that is better. The data used in this study is the average monthly rice price at the wholesale trade level from January 2015 to March 2021 in units of Rp/Kg as much as 75 data. The results of the comparison of the level of accuracy using the value of Mean Absolute Percentage Error (MAPE), obtained the forecast of the average price of rice at the Indonesian wholesale trade level for average based fuzzy time series Markov chain which is 0.36%, while the MAPE value for novel algorithm fuzzy time series is 0.19%. Based on the MAPE results, it can be concluded that the novel algorithm method fuzzy time series produces a better level of accuracy compared to the method average based fuzzy time series Markov chain.
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Zhou, Yaping, and Dayong Lv. "Aggregate Investor Sentiment and Time-Varying Price Discovery: Evidence from the Options Market." Economic Analysis Letters 2, no. 2 (May 13, 2023): 1–6. http://dx.doi.org/10.58567/eal02020001.
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Previous literature shows that the price-discovery ability of options market varies substantially over time. Using data of Shanghai Stock Exchange 50 exchange-traded fund options, this paper shows that options prices contribute relatively less to price discovery during low-sentiment periods, but the price-discovery ability of options market remains unchanged during high-sentiment periods. These results suggest that change in aggregate investor sentiment is an important source of the time variation in options’ price discovery ability. Moreover, the options market experiences greater bid-ask spreads when investor sentiment is lower, supporting a “transaction costs mechanism.” This paper fulfills related literature on the time variation in options’ price-discovery ability.
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Björklund, Kicki, John Alex Dadzie, and Mats Wilhelmsson. "Offer price, transaction price and time‐on‐market." Property Management 24, no. 4 (August 2006): 415–26. http://dx.doi.org/10.1108/02637470610671631.
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Avino, Davide, Emese Lazar, and Simone Varotto. "Time varying price discovery." Economics Letters 126 (January 2015): 18–21. http://dx.doi.org/10.1016/j.econlet.2014.09.030.
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Tian, Yuhe. "Changes in Bitcoin Prices under the Uncertain Market: An Analysis Based on Time Series Model." Highlights in Business, Economics and Management 7 (April 5, 2023): 208–15. http://dx.doi.org/10.54097/hbem.v7i.6950.
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Today's society is full of uncertainty, especially during the outbreak of the Russian Ukrainian Conflict, oil prices have changed. This paper uses VAR model and ARMA-GARCHX model to figure out if the high price of oil due to the conflicts between Russia and Ukraine will lead to changes in the price of Bitcoin. Through the use of STATA, it is concluded that in an uncertain market, the price of Bitcoin will be affected by the rise in oil prices. This paper combines the Russian Ukrainian conflict, oil price and Bitcoin price, and uses relevant data to fill in the relevant theories of the impact of the Russian Ukrainian conflict. Therefore, in the process of consumer investment, investors should not only pay attention to the benefits of Bitcoin, but also pay attention to many shortcomings of this emerging virtual currency. Besides, supervisors should improve the stability in the Bitcoin market.
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Jiang, Chun, Yi-Fan Wu, Xiao-Lin Li, and Xin Li. "Time-frequency Connectedness between Coal Market Prices, New Energy Stock Prices and CO2 Emissions Trading Prices in China." Sustainability 12, no. 7 (April 2, 2020): 2823. http://dx.doi.org/10.3390/su12072823.
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This paper aims to examine whether there is inherent dynamic connectedness among coal market prices, new energy stock prices and carbon emission trading (CET) prices in China under time- and frequency-varying perspectives. For this purpose, we apply a novel wavelet method proposed by Aguiar-Conraria et al. (2018). Specifically, utilizing the single wavelet power spectrum, the multiple wavelet coherency, the partial wavelet coherency, also combined with the partial phase difference and the partial wavelet gains, this paper discovers the time-frequency interaction between three markets. The empirical results show that the connectedness between the CET market price and the coal price is frequency-varying and mainly occur in the lower and higher frequency bands, while the connectedness between the CET market price and the new energy stock price mainly happen in the middle and lower frequency bands. In the high-frequency domain, the CET market price is mainly affected by the coal price, while the CET market price is dominated by the new energy stock price in the middle frequency. These uncovered frequency-varying characteristics among these markets in this study could provide several implications. Main participants in these markets, such as polluting industries, governments and financial actors, should pay close attention to the connectedness under different frequencies, in order to realize their goal of the production, the policymaking, and the investment.
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Afrimayani, Afrimayani, and Darvi Mailisa Putri. "Analisis Pergerakan Harga Emas Berjangka Menggunakan Model Fuzzy Time Series Markov Chain." JOSTECH Journal of Science and Technology 3, no. 2 (October 10, 2023): 144–55. http://dx.doi.org/10.15548/jostech.v3i2.6994.
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Gold is one type of precious metal that can be an investment instrument to protect the value of wealth. Gold price movements need to be known in investing, this can be observed usinga time series model that can predict gold prices in the next period. Gold price movement models can be used as investor guidelines in planning and decision making to increase profits and prevent losses. Gold price movements modeled with a numerical approach can be done through the Fuzzy Time Series Markov Chain (FTSMC) model. The modeling results show that the FTSMC can model gold prices and has good accuracy values with small MAPE, RMSE, and MAE values. This indicates an excellent goodness of fit for the FTSMC model. Long-term stability for gold price movements provides investment benefits because gold has value as an asset that tends to be stable, easy to liquidate in cash, free from interest, has a role as an emergency fund and can protect the value of wealth.
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Hafiyya, Nida, Fitria Virgantari, and Maya Widyastiti. "IMPLEMENTASI METODE FUZZY TIME SERIES PADA PERAMALAN HARGA EMAS DI INDONESIA." Interval : Jurnal Ilmiah Matematika 2, no. 2 (December 9, 2022): 94–103. http://dx.doi.org/10.33751/interval.v2i2.6517.
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Changes in gold prices in Indonesia continue to occur from time to time with uncertain changes and can be said to be volatile. With the condition of the volatile data on changes in gold prices, predictions are needed that are appropriate or close to the existing data. Chen's fuzzy time series method can be used as a solution to this problem because it has a low error rate. The purpose of this study was to predict the price of gold in Indonesia in the coming period by implementing the Chen fuzzy time series method, these steps can be solved using R software. The data used in this study were daily gold price data in Indonesia for the period January 1, 2022, to June 30, 2022. The results obtained are gold price forecasts for the period July 1, 2022, amounting to Rp. 988,313 and the MAPE value of 0.60515%. The MAPE value indicates that Chen's fuzzy time series method was very well used to forecast Indonesias gold price data.
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Jang, Han Ik, and Byeng Kuk Kim. "Analysis of the Ripple Effect Between Price Changes by Housing Type." Korean Association for Housing Policy Studies 31, no. 1 (February 28, 2023): 101–42. http://dx.doi.org/10.24957/hsr.2023.31.1.101.
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This study analyzed the interrelationship and lead-lag relationship between price changes by housing type with the TVP-VARX model and Wavelet Phase Difference. As a result of the empirical analysis, the price of all housing types has a strong tendency to decrease after 2020 due to rising interest rates. The interrelationship between price changes by housing type is time-varying, and the impact of increase in apartment price on the price of detached and row houses increases over time. On the other hand, the impact of increase in detached house prices on prices of other housing types increases and then decreases sharply after August 2020. The impact of increase in row house price on prices of other types of housing generally remains constant, but temporarily increases around August 2020. At this time, in the interrelationship between prices by housing type, the impact of increase in apartment price on the price of other housing types is the largest on average. Next, it is confirmed that the impact of increase in the price of detached and row houses has a large impact on other housing types. The lead-lag relationship between price changes by housing type, which is detached → row → apartment, clearly has made after 2019. However, the interrelationship, influence, and lead-lag relationship between price changes by housing type by region are different. Accordingly, this study suggests that it is necessary to discriminately select housing types and policies to be supplied for price stabilization in the housing market according to the time and region.
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Nilsen, Øivind A., Per Marius Pettersen, and Joakim Bratlie. "Time-Dependency in Producers’ Price Adjustments: Evidence from Micro Panel Data." Review of Economics 69, no. 2 (August 28, 2018): 147–68. http://dx.doi.org/10.1515/roe-2018-0012.
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Abstract Existing micro evidence of firms’ price changes tends to show a downward sloping hazard rate – the longer the price of a product has remained the same, the less likely it is that the price will change. Using a panel of Norwegian plant- and product-specific prices, we also find a downward sloping hazard when applying a Kaplan–Meier model. After having controlled for both observed and unobserved characteristics, we find flat hazards with spikes in the first and twelfth months. This suggests time-dependent price-setting by at least some of the producers. The spike after 12 months might be explained by seasonal demand effects, but also by the pricing season effect related to information acquisition and processing, negotiation and signing of price contracts. The revealed price adjustment pattern is at odds with the predictions of the Calvo model, a central element in many dynamic stochastic general equilibrium models, as this assumes constant frequencies of price adjustments over time. Our empirical findings instead point to a modified Calvo model where firms in some periods experience lower menu costs. Finally, the empirical findings may have implications for the effectiveness of monetary policy interventions.
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NGUYỄN MINH, ĐỨC. "Price Transmission in the Value Chain of Hard Clam in Vietnam." Journal of Asian Business and Economic Studies 219 (January 1, 2014): 127–43. http://dx.doi.org/10.24311/jabes/2014.219.1.06.
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Using data collected from 2007-2010, this study identifies price linkages and then forecasts vertical price transmission elasticities between markets (farm, wholesale, retail and export) in the value chain of hard clam (Meratrix lyrata) in Vietnam. After doing necessary tests to make sure that all price data are stationary, Seemingly Unrelated Regression (SUR) and Error Correction Model (ECM) are employed to examine short-time and long-time effects of hard clam price in one market on the other market in its value chain. The seemingly unrelated regression results show that hard clam prices seem not depend on seasons. Farm price of hard clam is transmitted completely to wholesale price while the price in retail market causes negative effect on farm price in the short run. Wholesale price of hard clam is transmitted to both prices in farm and retail markets. The export price of hard clam is estimated not to be affected in the short run by prices in other markets except retail price in domestic markets. Error correction models confirm the independence of hard clam price on annual seasons. The transmission elasticities of prices between the markets are also identified based on model estimation.