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1
Fang, Xian Wen, Yan Ni Zou, and Qian Jin Zhao. "An Efficient Web Service Composition Method Based on the Price-Time Petri Net." Advanced Materials Research 268-270 (July 2011): 1421–26. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1421.
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At present, developers can rapidly generate applications through Web service composition, the quality of service (QoS) of web service composition is important, but most of the existing composition methods are difficult to balance the QoS indexes (Such as time and price). In this paper, a web service composition method based on the price-time Petri net is proposed, the minimum cost can be obtained by modeling based on price time Petri net, and presents a method of priced state class to analyze the cost of web service composition model. Theoretical analysis and case analysis show that the price-time Petri net method is feasible to study Web service composition with the minimum cost.
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Wang, Jun, Huopo Pan, and Fajiang Liu. "Forecasting Crude Oil Price and Stock Price by Jump Stochastic Time Effective Neural Network Model." Journal of Applied Mathematics 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/646475.
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The interacting impact between the crude oil prices and the stock market indices in China is investigated in the present paper, and the corresponding statistical behaviors are also analyzed. The database is based on the crude oil prices of Daqing and Shengli in the 7-year period from January 2003 to December 2009 and also on the indices of SHCI, SZCI, SZPI, and SINOPEC with the same time period. A jump stochastic time effective neural network model is introduced and applied to forecast the fluctuations of the time series for the crude oil prices and the stock indices, and we study the corresponding statistical properties by comparison. The experiment analysis shows that when the price fluctuation is small, the predictive values are close to the actual values, and when the price fluctuation is large, the predictive values deviate from the actual values to some degree. Moreover, the correlation properties are studied by the detrended fluctuation analysis, and the results illustrate that there are positive correlations both in the absolute returns of actual data and predictive data.
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Maçãs Nunes, P. "Effect of disutility on prices together with income changes in the context of a durable goods monopoly." Acta Oeconomica 59, no. 2 (June 1, 2009): 207–29. http://dx.doi.org/10.1556/aoecon.59.2009.2.4.
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In this article, we study the effect of disutility of consumers who do not buy certain durable goods at the present time with income variations on the behaviour over time of prices in the context of a Durable Goods Monopoly facing a continuous demand. If consumers do not foresee future changes in their income, price always decreases. However, greater disutility contributes to softening the price fall in the present, never reaching marginal cost at that moment. Consumers’ perspective for future changes to income has two major implications: 1) the durable goods monopoly, depending on disutility, can set a price equal to marginal cost at the present time if the perspective is for income growth in the future, something which never happens if reduced income is foreseen; and 2) it increases the tendency of a rise in price depending on disutility. We conclude that a price rise may be obtained for quite small disutility when the predicted increase in income is moderate and for moderate disutility, when the predicted increase in income is high.
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Josephy, N., L. Kimball, and V. Steblovskaya. "A Time-Series Approach to Non-Self-Financing Hedging in a Discrete-Time Incomplete Market." Journal of Applied Mathematics and Stochastic Analysis 2008 (September 2, 2008): 1–20. http://dx.doi.org/10.1155/2008/275217.
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We present an algorithm producing a dynamic non-self-financing hedging strategy in an incomplete market corresponding to investor-relevant risk criterion. The optimization is a two-stage process that first determines market calibrated model parameters that correspond to the market price of the option being hedged. In the second stage, an optimal set of model parameters is chosen from the market calibrated set. This choice is based on stock price simulations using a time-series model for stock price jump evolution. Results are presented for options traded on the New York Stock Exchange.
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Yen, Gili, and Eva C. Yen. "Price Limits, Price Expectations, and Price Movements: Empirical Findings from Spectrum Analysis." Review of Pacific Basin Financial Markets and Policies 04, no. 01 (March 2001): 1–7. http://dx.doi.org/10.1142/s0219091501000334.
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The present study applies spectrum analysis to examine the impact of price limits on stock price movements. Based on spectrum analysis, the authors find that the imposition of price limits does alter the pattern of stock price movements in Taiwan. Yet, its efficacy erodes over time.
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Webster, Michael, and Rory C. Tarnow-Mordi. "Decomposing Multilateral Price Indexes into the Contributions of Individual Commodities." Journal of Official Statistics 35, no. 2 (June 1, 2019): 461–86. http://dx.doi.org/10.2478/jos-2019-0020.
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Abstract This article describes methods for decomposing price indexes into contributions from individual commodities, to help understand the influence of each commodity on aggregate price index movements. Previous authors have addressed the decomposition of bilateral price indexes, which aggregate changes in commodity prices from one time period to another. Our focus is the decomposition of multilateral price indexes, which aggregate commodity prices across more than two time periods or countries at once. Multilateral indexes have historically been used for spatial comparisons, and have recently received attention from statistical agencies looking to produce temporal price indexes from large and high frequency price data sets, such as scanner data. Methods for decomposing these indexes are of practical relevance. We present decompositions of three multilateral price indexes. We also review methods proposed by other researchers for extending multilateral indexes without revising previously published index levels, and show how to decompose the extended indexes they produce. Finally, we use a data set of seasonal prices and quantities to illustrate how these decomposition methods can be used to understand the influence of individual commodities on multilateral price index movements, and to shed light on the relationships between various multilateral and extension methods.
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Anvari-Azar, Farhad, Dani Strickland, Neil Filkin, and Harry Townshend. "Net Present Value Analysis of a Hybrid Gas Engine-Energy Storage System in the Balancing Mechanism." Energies 13, no. 15 (July 24, 2020): 3816. http://dx.doi.org/10.3390/en13153816.
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There is the potential for hybridised gas engine-energy storage systems to participate in the Balancing Mechanism (BM) by offering a product that marries the advantages of both units. The higher price offerings are currently dominated by pumped storage (PS) assets. Given their high-flexibility, PS plants mostly offer at higher prices, but respond quicker and can run for a smaller minimum run time than a gas engine on its own. The operation of the hybrid system must match the operation of the pumped storage plants, to be able to claim a space in this part of the BM market including meeting a minimum run time and minimum start time. The business case is dependent on battery costs which in turn depend on size and operational strategy. This paper uses a case study approach to estimate Net Present Value of a hybrid system. The paper uses a mixture of publicly available data and industrially provided data within its analysis. The paper concludes that battery cost and lifespan are still issues and that battery-engine hybrids are not economic at present. There is indication in the modelling that under very favorable conditions such as low compound interest rates, an acceptance of offers above 7 times/day and low gas price, it is possible to see a return on investment of a lithium ion-based battery-gas engine hybrid.
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EKSTRÖM, ERIK, and JOHAN TYSK. "OPTIONS WRITTEN ON STOCKS WITH KNOWN DIVIDENDS." International Journal of Theoretical and Applied Finance 07, no. 07 (November 2004): 901–7. http://dx.doi.org/10.1142/s0219024904002694.
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There are two common methods for pricing European call options on a stock with known dividends. The market practice is to use the Black–Scholes formula with the stock price reduced by the present value of the dividends. An alternative approach is to increase the strike price with the dividends compounded to expiry at the risk-free rate. These methods correspond to different stock price models and thus in general give different option prices. In the present paper we generalize these methods to time- and level-dependent volatilities and to arbitrary contract functions. We show, for convex contract functions and under very general conditions on the volatility, that the method which is market practice gives the lower option price. For call options and some other common contracts we find bounds for the difference between the two prices in the case of constant volatility.
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Gürtler, Marc, and Thomas Paulsen. "Forecasting performance of time series models on electricity spot markets." International Journal of Energy Sector Management 12, no. 4 (November 5, 2018): 617–40. http://dx.doi.org/10.1108/ijesm-12-2017-0006.
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Purpose Study conditions of empirical publications on time series modeling and forecasting of electricity prices vary widely, making it difficult to generalize results. The key purpose of the present study is to offer a comparison of different model types and modeling conditions regarding their forecasting performance. Design/methodology/approach The authors analyze the forecasting performance of AR (autoregressive), MA (moving average), ARMA (autoregressive moving average) and GARCH (generalized autoregressive moving average) models with and without the explanatory variables, that is, power consumption and power generation from wind and solar. Additionally, the authors vary the detailed model specifications (choice of lag-terms) and transformations (using differenced time series or log-prices) of data and, thereby, obtain individual results from various perspectives. All analyses are conducted on rolling calibrating and testing time horizons between 2010 and 2014 on the German/Austrian electricity spot market. Findings The main result is that the best forecasts are generated by ARMAX models after spike preprocessing and differencing the data. Originality/value The present study extends the existing literature on electricity price forecasting by conducting a comprehensive analysis of the forecasting performance of different time series models under varying market conditions. The results of this study, in general, support the decision-making of electricity spot price modelers or forecasting tools regarding the choice of data transformation, segmentation and the specific model selection.
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Çevik, Emrah, Erdal Atukeren, and Turhan Korkmaz. "Oil Prices and Global Stock Markets: A Time-Varying Causality-In-Mean and Causality-in-Variance Analysis." Energies 11, no. 10 (October 21, 2018): 2848. http://dx.doi.org/10.3390/en11102848.
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This study examines the Granger-causal relationships between oil price movements and global stock returns by using time-varying Granger-causality tests in mean and in variance. We use the daily returns from Morgan Stanley Capital International (MSCI) G7 and the MSCI Emerging Stock Market Indexes to distinguish between the effects of daily oil price movements on G7 countries’ and emerging market countries’ stock markets. We further divide the emerging markets into two groups as oil-exporting and oil-importing countries. For the oil market, we use both the West Texas Intermediate (WTI) and Brent oil daily price movements. While the Granger-causality-in-mean tests indicate a causal link from WTI oil prices and G7 countries’ stock returns to MSCI emerging countries’ stock returns, the Granger-causality-in-variance tests suggest no causal link from global oil market prices to stock market returns. Nonetheless, a causal link from the G7 countries’ stock returns to the MSCI emerging countries’ stock returns is detected. In addition, G7 countries’ stock market volatility is found to Granger-cause Brent oil price volatility. The time-varying Granger-causality-in-mean and Granger-causality-in-variance tests present new and further insights. A causal relationship between oil price changes and G7 countries’ stock returns is found for some periods during and after the global financial crisis. Time-varying Granger-causality-in-variance test results indicate evidence of causal linkages among oil prices and global stock market returns that are specific only to certain time periods. We also find that there might be a difference between the movements in Brent and WTI oil prices with respect to their Granger-causal effects on oil-importing emerging markets’ stock returns—especially after the global financial crisis. Our results provide further evidence that the effects of oil price movements on stock returns might be different depending on the volatility in the stock markets.
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Mat, Burak, Mehmet Saltuk Arikan, Mustafa Bahadir Çevrimli, Ahmet Cumhur Akin, and Mustafa Agah Tekindal. "Causality Analysis of the Factors Affecting the Consumer Price of Veal: The Case of Turkey." Sustainability 12, no. 15 (August 3, 2020): 6257. http://dx.doi.org/10.3390/su12156257.
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It is interesting to identify the reasons and the direction of the correlation between the input/output prices and the macro/micro parameters in animal production processes. In the present study, the time series of the monthly data between the years 2014 and 2019 were analyzed to examine the factors that affected the consumer price of carcass meat in Turkey. An attempt was made to identify the relationship between the consumer price of carcass meat and the prices of cattle fattening feed, the exchange rate of the dollar, producer price index (PPI), and the agricultural PPI, which were anticipated to affect the consumer price of carcass meat as determined by the Granger causality analysis. According to econometric analysis results, when there is a change in carcass producer price, cattle fattening feed and PPI in the short term, the consumer price of carcass meat is affected by this. The producer price of carcass and PPI variables are determined to be the cause of each other’s Granger. At the same time, the PPI variable and the consumer price of carcass meat and dollar rate variables were found to be the cause of each other’s Granger. If Turkey is to prevent the excessive fluctuations in the consumer- and producer-prices of carcass meat caused by macro variables, an effective price control mechanism should be put into practice. It seems that this change would be possible only by developing and implementing policies to lower the input prices and production costs.
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Dahl, Mikkel. "A Discrete-Time Model for Reinvestment Risk in Bond Markets." ASTIN Bulletin 37, no. 02 (November 2007): 235–64. http://dx.doi.org/10.2143/ast.37.2.2024066.
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In this paper we propose a discrete-time model with fixed maximum time to maturity of traded bonds. At each trading time, a bond matures and a new bond is introduced in the market, such that the number of traded bonds is constant. The entry price of the newly issued bond depends on the prices of the bonds already traded and a stochastic term independent of the existing bond prices. Hence, we obtain a bond market model for the reinvestment risk, which is present in practice, when hedging long term contracts. In order to determine optimal hedging strategies we consider the criteria of super-replication and risk-minimization.
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Dahl, Mikkel. "A Discrete-Time Model for Reinvestment Risk in Bond Markets." ASTIN Bulletin 37, no. 2 (November 2007): 235–64. http://dx.doi.org/10.1017/s0515036100014859.
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In this paper we propose a discrete-time model with fixed maximum time to maturity of traded bonds. At each trading time, a bond matures and a new bond is introduced in the market, such that the number of traded bonds is constant. The entry price of the newly issued bond depends on the prices of the bonds already traded and a stochastic term independent of the existing bond prices. Hence, we obtain a bond market model for the reinvestment risk, which is present in practice, when hedging long term contracts. In order to determine optimal hedging strategies we consider the criteria of super-replication and risk-minimization.
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Pejović, Igor. "A Comparative Analysis of the Price Index in Transition Countries in the Time of Globalisation." Journal of Central Banking Theory and Practice 3, no. 1 (January 1, 2014): 101–13. http://dx.doi.org/10.2478/jcbtp-2014-0007.
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Abstract Globalisation with all its features can be divided in two segments - good and bad. When we look at the good side of globalisation, it is obvious that it has erased boundaries between countries in terms of trade, education, knowledge sharing, and other new technologies, while on the other hand, the bad side is that it has created a considerable gap between developed and developing countries, then different types of commercial, political and other conditioning, and dependence on strong, developed states. A great contribution to the negative part of globalisation was of economic instability that occurred at the beginning of this century and which consequences are still present in the world. In this article, we presented the impact of economic instability on the price index trough a comparative analysis of transition countries such as Montenegro, Serbia and Croatia over a period of five years (Croatia has just recently become a member of the European Union and due to that fact it was included in this study). The survey covered price indices relating to the prices of industrial products for the domestic markets, consumer price indices, indices of the hospitality services and the prices of the agricultural products.
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Rammurthy, Shruthi Komarla, and Sagar B. Patil. "An LSTM-Based Approach to Predict Stock Price Movement for IT Sector Companies." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (October 2021): 1–12. http://dx.doi.org/10.4018/ijcini.20211001.oa3.
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A stock market is an aggregation of buyers and sellers where issuance, buying, and selling of stocks happen. Predicting stock price is a significant concern due to volatility. Historical stock price and historical price data reveal the effect of such factors. Since stock data is time series and prediction can be made accurately with time series forecasting model. LSTM (Long Short Term Memory) model, a particular kind of RNN (Recurrent Neural Network), based on time series forecasting used to predict stock price. LSTM doesn’t have long term dependencies because of its distinctive structure. The study focuses on major IT firms considering the company’s low and high prices. But, mid-price, which is a mean of the low and close price, is considered for the prediction. LSTM based methodology employing mid-price is effective in predicting values compared to other attributes and accuracy of prediction using the LSTM model. We conclude with the present model is more efficient in stock price prediction with a decrease in mean square error.
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NIELSEN, JAN NYGAARD, and MARTIN VESTERGAARD. "ESTIMATION IN CONTINUOUS-TIME STOCHASTIC VOLATILITY MODELS USING NONLINEAR FILTERS." International Journal of Theoretical and Applied Finance 03, no. 02 (April 2000): 279–308. http://dx.doi.org/10.1142/s0219024900000139.
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The stylized facts of stock prices, interest and exchange rates have led econometricians to propose stochastic volatility models in both discrete and continuous time. However, the volatility as a measure of economic uncertainty is not directly observable in the financial markets. The objective of the continuous-discrete filtering problem considered here is to obtain estimates of the stock price and, in particular, the volatility using discrete-time observations of the stock price. Furthermore, the nonlinear filter acts as an important part of a proposed method for maximum likelihood for estimating embedded parameters in stochastic differential equations. In general, only approximate solutions to the continuous-discrete filtering problem exist in the form of a set of ordinary differential equations for the mean and covariance of the state variables. In the present paper the small-sample properties of a second order filter is examined for some bivariate stochastic volatility models and the new combined parameter and state estimation method is applied to US stock market data.
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Bradáčová, K. "Official agricultural land price in the Slovak Republic." Agricultural Economics (Zemědělská ekonomika) 53, No. 4 (January 7, 2008): 184–88. http://dx.doi.org/10.17221/865-agricecon.
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As long as the land market in Slovakia is not completely developed and land market prices introduced, the officially assigned land prices are practically in use. At the present time, land prices should express the supply prices, which cover the income effect of the land site under the socially necessary costs. In this situation, for the temporary period, centrally assigned fixed land prices could represent the effective supply and demand prices in case they correspond to the mentioned conditions. At present, the official prices are used for fiscal purposes and the land property rights.
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Riley, John M. "Extension's Role in Commodity Marketing Education: Past, Present, and Future." Journal of Agricultural and Applied Economics 45, no. 3 (August 2013): 537–55. http://dx.doi.org/10.1017/s1074070800005058.
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Historically, market situation and outlook has often included some form of price forecast. Recent volatility in agricultural commodity markets is making price forecasts challenging and at times less reliable. In addressing this price volatility, changes in agricultural markets are highlighted along with price forecasts: pre- and postincreased market volatility. Given these recent challenges, the future of Extension agricultural commodity marketing is discussed.
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Gollust, Sarah E., Xuyang Tang, Carlisle Ford Runge, Simone A. French, and Alexander J. Rothman. "The effect of proportional v. value pricing on fountain drink purchases: results from a field experiment." Public Health Nutrition 21, no. 13 (May 15, 2018): 2518–22. http://dx.doi.org/10.1017/s1368980018001143.
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AbstractObjectiveReducing sugar-sweetened beverage consumption is a public health priority, yet finding an effective and acceptable policy intervention is challenging. One strategy is to use proportional pricing (a consistent price per fluid ounce) instead of the typical value-priced approach where large beverages offer better value. The purpose of the present study was to evaluate whether proportional pricing affects the purchasing of fountain beverages at a university cinema concession stand.DesignFour price strategies for beverages were evaluated over ten weekends of film screenings. We manipulated two factors: the price structure (value pricing v. proportional pricing) and the provision of information about the price per fluid ounce (labels v. no labels). The key outcomes were the number and size of beverages purchased. We analysed data using regression analyses, with standard errors clustered by film and controlling for the day and time of purchase.SettingA university cinema concession stand in Minnesota, USA, in spring 2015.SubjectsUniversity students.ResultsOver the study period (360 beverages purchased) there were no significant effects of the proportional pricing treatment. Pairing a label with the standard value pricing increased the likelihood of purchasing large drinks but the label did not affect purchasing when paired with proportional pricing.ConclusionsProportional prices did not significantly affect the size of beverages purchased by students at a university cinema, but adding a price-per-ounce label increased large drink purchases when drinks were value-priced. More work is needed to address whether pricing and labelling strategies might promote healthier beverage purchases.
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Bils, Mark, Peter J. Klenow, and Benjamin A. Malin. "Reset Price Inflation and the Impact of Monetary Policy Shocks." American Economic Review 102, no. 6 (October 1, 2012): 2798–825. http://dx.doi.org/10.1257/aer.102.6.2798.
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Many business cycle models use a flat short-run Phillips curve, due to time-dependent pricing and strategic complementarities, to explain fluctuations in real output. But, in doing so, these models predict unrealistically high persistence and stability of US inflation in recent decades. We calculate “reset price inflation”—based on new prices chosen by the subsample of price changers—to dissect this discrepancy. We find that the models generate too much persistence and stability both in reset price inflation and in the way reset price inflation is converted into actual inflation. Our findings present a challenge to existing explanations for business cycles. (JEL E31, E52)
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Hashemlou, Bahareh, Arashk Masaeli, Hossein Sadeghi, Alireza Nasseri, and Mohammadhadi Hajian. "Determination of Shadow Price of Iran Electricity Market Using the Fuzzy Electricity Generation Planning." Current World Environment 10, no. 1 (April 30, 2015): 76–83. http://dx.doi.org/10.12944/cwe.10.1.09.
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The present economies are so dependent on the electricity that even the short electricity outages cannot be tolerated. Decisions in Iran’s Electricity Grid are made on basis of the effective security of the units, which is rather distant from the price and market based planning. The present research was conducted to forecast the shadow price in Iran’s electricity market, for providing efficient tools for operators and transmitters of the market. Linear programming is among the powerful instruments for making instant balance and revealing the shadow price of electricity industry. Considering the uncertainty of the parameters involved in electricity grid models, implementation of fuzzy logic can be effective in regulating such parameters. Toward this aim, the present research was conducted using the shadow price of Iran’s electricity market within a time period from 20-Mar-2013 to 20-Mar-2014 using the fuzzy linear programming model. The results showed the shadow prices as 343 IRR and 383 IRR for the days with maximum and minimum use, respectively.
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Whiting, Seth W., Rocco Giovanni Catrone, and Amrinder Babbra. "Episodic Chasing and Price of Scratch-off Lottery Tickets." Journal of Gambling Issues, no. 32 (May 1, 2016): 133. http://dx.doi.org/10.4309/jgi.2016.32.8.
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The present study examined the relationship between instant (scratch-off) lottery ticket price and "chasing losses" within a single gambling episode. Across several months, each time an instant lottery ticket was purchased (N = 1081), convenience store clerks recorded the gamblers' genders, the price of the tickets purchased, and whether the customer purchased another ticket before leaving the premises. Logistic regression analysis showed a significant association between ticket price and repurchasing (odds ratio = .842, p < .0001), suggesting within-session chasing is common with lower priced instant lottery tickets, and lower costs are not necessarily indicative of less risk.
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Selvam, M., M. Raja, and P. Yazh Mozhi. "Forecasting the Time Volatility of Emerging Asian Stock Market Index." Asia Pacific Business Review 3, no. 2 (July 2007): 38–51. http://dx.doi.org/10.1177/097324700700300205.
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Volatility is the measure of how far the current price of an asset deviates from its average past prices. Greater the deviation, greater the volatility. It indicates the strength or conviction behind a price movement. Stock market volatility is the function of the arrival of positive and negative market information. Pricing of securities is supposed to be dependent on the volatility of each asset. Matured / developed markets continue to provide over long period of time high returns with low volatility. Emerging markets, except India and China exhibit low returns. The exponential growth in the Asian derivatives markets necessitated the need to test whether the Asian market indices are more volatile or not. The study finds an evidence of time varying volatility, which exhibits clustering, high persistence and predictability for almost all the Asian market indices in the sample. With this background the present paper investigates the dynamic behavior of stock returns of ten market indices from Asian countries, using symmetric GARCH (1,1) model for a period of one year from January 2006 to December 2006.
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Reichel, Vlastimil, and Petr Zimčík. "Determinants of Real Estate Prices in the Statutory City of Brno." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 66, no. 4 (2018): 991–99. http://dx.doi.org/10.11118/actaun201866040991.
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The real estate market demand for both‑houses and flats has been growing recently. This trend is one of the factors, which are able to influence the price of real estate. Our paper introduces determinants of housing price and their influence on actual real estate prices in the statutory city of Brno. The aim of this paper is to present key determinants of house price and find the hedonic price model, which describes, how determinants affect house price best. Realized prices were analyzed for housing units located in different districts of Brno. Key determinants in this research are a year of sale, an area of flat in square meters, number of rooms, location in districts of Brno, type of masonry and reconstruction. The dataset covers the time period between years 2012 and 2015. Hedonic price model is estimated by the method of ordinary least square. Besides main aim, four assumptions were verified, which should determine the influence on the price of individual determinants in the statutory city of Brno.
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Beare, Brendan K., and Juwon Seo. "TIME IRREVERSIBLE COPULA-BASED MARKOV MODELS." Econometric Theory 30, no. 5 (April 16, 2014): 923–60. http://dx.doi.org/10.1017/s0266466614000115.
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Economic and financial time series frequently exhibit time irreversible dynamics. For instance, there is considerable evidence of asymmetric fluctuations in many macroeconomic and financial variables, and certain game theoretic models of price determination predict asymmetric cycles in price series. In this paper, we make two primary contributions to the econometric literature on time reversibility. First, we propose a new test of time reversibility, applicable to stationary Markov chains. Compared to existing tests, our test has the advantage of being consistent against arbitrary violations of reversibility. Second, we explain how a circulation density function may be used to characterize the nature of time irreversibility when it is present. We propose a copula-based estimator of the circulation density and verify that it is well behaved asymptotically under suitable regularity conditions. We illustrate the use of our time reversibility test and circulation density estimator by applying them to five years of Canadian gasoline price markup data.
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Bodnar, Olga, Julia Galchynska, and Mariusz Maciejczak. "PRICE INTERDEPENDENCE OF AGRICULTURAL COMMODITIES FROM UKRAINE AND WORLD MARKETS." Acta Scientiarum Polonorum. Oeconomia 19, no. 4 (January 3, 2021): 15–22. http://dx.doi.org/10.22630/aspe.2020.19.4.36.
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The objective of the paper is to present the price interdependencies between agricultural commodity products from Ukraine (both export and non-export oriented) and other commodities whose prices are shaped on world markets, with a special focus on the role of their volatility. The research demonstrates a tight connection between the global prices of crude oil and prices of Ukrainian corn and wheat. Additionally, the volatility of world prices of agricultural commodities influenced the Ukrainian national market and had significant impact on domestic price declines. At the same time, the mechanisms for pricing non-export related agricultural commodities are formed mostly under the influence of factors from the domestic market. It is argued that a low interdependency between non-export oriented agricultural commodities and world markets stipulates the social stability of Ukraine’s population.
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önalan, ömer. "Time-changed generalized mixed fractional Brownian motion and application to arithmetic average Asian option pricing." International Journal of Applied Mathematical Research 6, no. 3 (June 13, 2017): 85. http://dx.doi.org/10.14419/ijamr.v6i3.7688.
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In this paper we present a novel model to analyze the behavior of random asset price process under the assumption that the stock price pro-cess is governed by time-changed generalized mixed fractional Brownian motion with an inverse gamma subordinator. This model is con-structed by introducing random time changes into generalized mixed fractional Brownian motion process. In practice it has been observed that many different time series have long-range dependence property and constant time periods. Fractional Brownian motion provides a very general model for long-term dependent and anomalous diffusion regimes. Motivated by this facts in this paper we investigated the long-range dependence structure and trapping events (periods of prices stay motionless) of CSCO stock price return series. The constant time periods phenomena are modeled using an inverse gamma process as a subordinator. Proposed model include the jump behavior of price process because the gamma process is a pure jump Levy process and hence the subordinated process also has jumps so our model can be capture the random variations in volatility. To show the effectiveness of proposed model, we applied the model to calculate the price of an average arithmetic Asian call option that is written on Cisco stock. In this empirical study first the statistical properties of real financial time series is investigated and then the estimated model parameters from an observed data. The results of empirical study which is performed based on the real data indicated that the results of our model are more accuracy than the results based on traditional models.
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Dittmann, Iwona. "Primary and Secondary Residential Real Estate Markets in Poland – Analogies in Offer and Transaction Price Development." Real Estate Management and Valuation 21, no. 1 (May 1, 2013): 39–48. http://dx.doi.org/10.2478/remav-2013-0006.
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Abstract The paper presents the results of a comparison of the development of mean offer and transaction prices per 1m2 in primary and secondary residential real estate markets in 16 provincial capital cities in Poland, during the time period from the 3rd quarter of 2006 to the 3rd quarter of 2012. The quarterly data came from the residential real estate price database of the National Bank of Poland (NBP). The research concerns the dependencies in the residential real estate market between the following four price categories: offer prices in the primary market, transaction prices in the primary market, offer prices in the secondary market and transaction prices in the secondary market. For each city, the dynamics of each price category per 1m2, the existence of a linear correlation between the individual mean price categories in chosen subintervals and their convergence and variability in time were studied and compared.
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Miao, Chun-Hui. "Why Don’t Prices Rise during Periods of Peak Demand? Synchronize Demand to Relax Competition." B.E. Journal of Economic Analysis & Policy 16, no. 3 (July 1, 2016): 1585–97. http://dx.doi.org/10.1515/bejeap-2015-0238.
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Abstract During periods of peak demand, frequent markdowns present an empirical puzzle. Based on the idea that stores face capacity constraints in times of high shopping volume, we show that stores keep their off-season prices high in order to lure all consumers to shop around the same time. This relaxes competition and allows stores to raise prices. Due to binding capacity constraints, stores randomize their prices. Thus, our model offers a unified explanation for both the countercyclicality and the high frequency of price changes during periods of peak demand.
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Andriawan, Rio, and Utik Bidayati. "PENGARUH KENAIKAN HARGA BAHAN BAKAR MINYAK TERHADAP INDEKS HARGA SAHAM GABUNGAN DI BURSA EFEK INDONESIA." Jurnal Fokus Manajemen Bisnis 4, no. 2 (September 30, 2014): 75. http://dx.doi.org/10.12928/fokus.v4i2.1352.
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This study entitled “The Effect of Fuel Price Increase Against Composite Stock Price Index In Indonesia Stock Exchange”, with populations used in this study is a data sample JCI JCI to 8 working days before and 8 days of work after the fuel price hike june 22 2013, researchers assume that the time span of the study will occur the impact of rising fuel prices which impact on the presence of differences in returns and significant abnormal return 8 working days 8 working days before and after the increase in fuel prices, this is evidenced by the value of α <sig. Ie 0.05 <0.064. While the subsequent twsting using a one sample t-test to get the result there is no significant abnormal return in 8 working days before and 8 days after the work is evidenced by the increase in fuel prices α < sig. Significance before and after values for 0.689 and 0.605. In this study it can be concluded that in th present impact of an event does not occur at yhe time of approaching the event.
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Haight, Robert G. "Optimal management of loblolly pine plantations with stochastic price trends." Canadian Journal of Forest Research 23, no. 1 (January 1, 1993): 41–48. http://dx.doi.org/10.1139/x93-007.
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An economic analysis of loblolly pine (Pinustaeda L.) plantation management options with stochastic sawtimber and pulpwood stumpage price trends is conducted using the North Carolina State University Plantation Management Simulator, a widely used model in the southeastern United States. Results for stands with a range of site indices suggest that regimes with high planting densities combined with commercial thinning options have higher expected present values than do regimes without thinning options, especially in plantations with hardwood competition. Such regimes are superior because high planting densities increase the returns from pulpwood thinnings without compromising sawtimber volume at rotation age. Further, high planting densities maintain the option to produce either sawtimber or pulpwood depending on the stumpage prices at midrotation. Optimal regimes are conditional on the sawtimber and pulpwood prices at the time of planting. A comparison of results for plantations with and without hardwood competition suggests that when the hardwood stumpage price is likely to increase over time, removing hardwoods with commercial thinning is superior to removing hardwoods immediately after planting. Finally, planting and thinning regimes that are optimal for deterministic price trends provide near-optimal expected returns when employed in an environment where price trends are stochastic.
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Mircea, Gabriela, Mihaela Neamţu, and Laura Mariana Cismaş. "Dynamical Models For Prices With Distributed Delays." Timisoara Journal of Economics and Business 8, no. 1 (June 1, 2015): 91–102. http://dx.doi.org/10.1515/tjeb-2015-0010.
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Abstract In the present paper we study some models for the price dynamics of a single commodity market. The quantities of supplied and demanded are regarded as a function of time. Nonlinearities in both supply and demand functions are considered. The inventory and the level of inventory are taken into consideration. Due to the fact that the consumer behavior affects commodity demand, and the behavior is influenced not only by the instantaneous price, but also by the weighted past prices, the distributed time delay is introduced. The following kernels are taken into consideration: demand price weak kernel and demand price Dirac kernel. Only one positive equilibrium point is found and its stability analysis is presented. When the demand price kernel is weak, under some conditions of the parameters, the equilibrium point is locally asymptotically stable. When the demand price kernel is Dirac, the existence of the local oscillations is investigated. A change in local stability of the equilibrium point, from stable to unstable, implies a Hopf bifurcation. A family of periodic orbits bifurcates from the positive equilibrium point when the time delay passes through a critical value. The last part contains some numerical simulations to illustrate the effectiveness of our results and conclusions.
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Larionova, E. I., T. I. Chinaeva, and E. P. Shpakovskaya. "Analysis of the development of Oil and Gas Industry in present conditions." Statistics and Economics 16, no. 6 (December 26, 2019): 29–36. http://dx.doi.org/10.21686/2500-3925-2019-6-29-36.
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Purpose of the study. This study examines the state of companies of oil sector based on the analysis of dynamics and relationship between basic financial indicators, characterizing the activities of oil companies; it identifies factors affecting the companies’ efficiency, such as return on sales (ROS) and productivity. The work is based on dynamic, structural, correlation analysis of analytical and statistical information on processes occurring in this area of economic activity.Materials and methods. Statistical data and analytical information on oil sector companies serve as the information base of this study. Statistical methods of information analysis (comparative analysis, analysis of time series, correlation, and regression analysis) represent the methodological base of research.Results. The authors analyzed the development trends of the global and Russian oil and gas sectors. The last two decades have been marked by changes in the global oil market that were caused by fluctuations in the price of oil and oil products and with the rise and fall in the price of Brent crude oil per barrel.The paper considers dynamics of financial indicators of Russian oil companies. An analysis of the data on the revenue of the largest Russian companies in ruble and dollar terms over the last 10 years has revealed a significant difference in the dynamics of these indicators. The authors performed ROS and oil price profitability correlation as well as correlation between the price of oil, the exchange rate and the profitability of oil companies.Conclusion. The oil and gas industry is an essential sector of the economy that heavily promotes to the socio-economic development of our country. Revenues of the oil and gas sector contribute to the Russian GDP and are a major component of the budget. There are two ways to calculate revenue of oil companies – in ruble (dollar terms) and impact of RUB/USD exchange rate. The sharp changes in the exchange rate of the last decade have advanced significant changes in the revenue of Russian oil companies.In this study, the total revenue (in dollar terms) was calculated as the ratio of revenue in rubles to the average annual exchange rate of the corresponding period. In general, the disastrous results of 2015 and 2016 led to a decrease in the average growth rates of dollar and ruble revenue, as well as profit and profitability.The authors performed a correlation analysis of return on sales and oil prices, which revealed an almost total absence of correlation between these indicators. Oil prices and exchange rates have a negligible effect on the profitability of oil companies. An inverse correlation is observed between the RUB/USD pair and the oil price per barrel. It is concluded that the cost of oil and the exchange rate have little effect on the profitability of oil companies.Since the oil and gas complex makes a very significant contribution to the development of the country’s economy, it is advisable to analyze its development trends on a regular basis. Based on the results of the economic and statistical analysis of financial indicators, it is possible to identify the main development directions of the oil and gas industry, evaluate positive and negative processes, and determine further prospects.
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Afsar, Hasan Murat, Oussama Ben-Ammar, Alexandre Dolgui, and Faicel Hnaien. "Supplier Replacement Model in a One-Level Assembly System under Lead-Time Uncertainty." Applied Sciences 10, no. 10 (May 13, 2020): 3366. http://dx.doi.org/10.3390/app10103366.
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Supplier selection/replacement strategies, purchasing price negotiation and optimized replenishment policies play a key role in efficient supply chain management in today’s dynamic market. Their importance increases even more in Industry 4.0. In this paper, we propose a joint model of replenishment planning and purchasing price negotiation in the context of supplier replacement in a one-level assembly system (OLAS) producing one type of finished product. The real component lead times are stochastic. There is consequently a non-negligible risk that the assembly process may be stopped if all components for assembly are not delivered on the due date. This incurs inventory-related costs, holding and backlogging, which should be minimized. We consider a set of suppliers characterized by their prices and the probability distributions of their lead-times, and we present a model and an approach that optimize not only replenishment policy, but also purchasing prices. For a given unit, it is possible to model several alternative suppliers with alternative pricing and lead-time uncertainties, and evaluate their impacts on the total cost: composed of holding, backlogging and purchasing costs for the assembly system. The findings of this study indicate that it can be beneficial to pay suppliers an additional purchase cost in order to reduce the holding and backlogging costs related to uncertainty. In consequence, decision makers can use the proposed approach to negotiate prices and delivery delays or to select suppliers.
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Fonseca, Vitor M. A. da, and Manuel A. R. da Fonseca. "A Simple Approach to Assess if a Financial “Bubble” is Present: The Case of Bitcoin." Applied Economics and Finance 6, no. 4 (May 14, 2019): 1. http://dx.doi.org/10.11114/aef.v6i4.4266.
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This article’s goal is to evaluate if the recent price behavior of Bitcoin can be characterized as a financial market “bubble”. To deal with this assessment, we adopt a statistical definition of a “bubble” derived from the efficient market hypothesis and we propose a simple method to test this proposition, based on the time-series model known as random walk. We analyze the data available for Bitcoin prices, together with an asset selected as benchmark, and perform statistical tests derived from simple regression equations. The main conclusion is that there is consistent evidence that that Bitcoin follows the pattern of a financial “bubble” – at least, such pattern is more evident in the case of Bitcoin than in the stock index used as benchmark.
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Tomal, Mateusz, and Agata Gumieniak. "Agricultural Land Price Convergence: Evidence from Polish Provinces." Agriculture 10, no. 5 (May 21, 2020): 183. http://dx.doi.org/10.3390/agriculture10050183.
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This research deals with the problem of agricultural land market efficiency using the spatial market integration concept as well as the present value (PV) model. Empirically, it aims to test the convergence of agricultural land prices across Polish provinces. In order to check the law of one price (LOP), good-quality, medium-quality and bad-quality land sales markets are examined separately. Furthermore, this study is complemented by an analysis of the drivers behind agricultural land price convergence. The main method of testing price convergence is the log t regression. The latter was performed in two configurations, i.e., based on trend components of time series extracted using the Hodrick–Prescott filter and the Hamilton filter. Additionally, traditional β- and σ-convergence tests were applied. The obtained results indicated that agricultural land prices tend to converge in relative terms, which means that the provinces share a common long-run growth path. This finding and estimates of traditional convergence tests prove the increasing integration in the agricultural land market in Poland. There is no evidence, however, to support the conclusion that the absolute version of the long-run LOP holds. Moreover, using dynamic fixed effects models, it was identified that for good-, medium- and bad-quality land prices almost the same drivers of convergence apply. The only differences concern the strength of the influence of independent variables on prices of farmland of various types. Additionally, bad-quality land prices are the only ones which are affected by livestock density. Furthermore, estimates of the present value model finally confirmed that the agricultural land sales market in Poland cannot be considered as efficient.
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Agarwalla, Megha, Tarak Nath Sahu, and Shib Sankar Jana. "Dynamics of oil price shocks and emerging stock market volatility: a generalized VAR approach." Vilakshan - XIMB Journal of Management 18, no. 2 (February 10, 2021): 106–21. http://dx.doi.org/10.1108/xjm-07-2020-0018.
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Purpose This study aims to establish the dynamic relationship between international crude oil prices and Indian stock prices represented by the Bombay Stock Exchange (BSE) energy index. Design/methodology/approach Using Johansen’s cointegration test, vector error correction (VEC) model, impulse response function and variance decomposition test the study tries to ascertain the short-term and long-term dynamic association between the oil price shock and the movement of stock price and Granger causality test is applied to find out the nature of causality. Findings Considering vector autoregression estimation, the present study analyzes the relationship between the variables and tries to make a valid conclusion. The result of the co-integration test exhibits the presence of a long-term association between these two macro-economic variables during the period under study. Also, in the short-run VEC Granger causality result reveals that the movement of international crude oil price significantly influences the Indian stock price. Research limitations/implications To get a more robust result the study can be further extended by taking a longer time period with data of shorter time-frequency such as daily or weekly and further by using more sophisticated econometric and statistical tools. Further, the study can be extended to firm-level investigation considering the forward trading concentration with the Indian oil basket. Social implications In today’s globalized era, forecasting of share price movement helps investors in predicting the market and invest accordingly. Through this liquidity of the markets enhance and markets become more active in the global arena. Originality/value This study represents fresh findings in the changing time period the linkage between crude oil prices and stock prices which are of value to the academicians, researchers, policymakers, investors, market regulators, etc.
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Fiedor, Paweł, and Artur Hołda. "The Effects of Bankruptcy on the Predictability of Price Formation Processes on Warsaw’s Stock Market." e-Finanse 12, no. 1 (March 1, 2016): 32–42. http://dx.doi.org/10.1515/fiqf-2016-0134.
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AbstractIn this study we investigate how bankruptcy affects the market behaviour of prices of stocks on Warsaw’s Stock Exchange. As the behaviour of prices can be seen in a myriad of ways, we investigate a particular aspect of this behaviour, namely the predictability of these price formation processes. We approximate their predictability as the structural complexity of logarithmic returns. This method of analysing predictability of price formation processes using information theory follows closely the mathematical definition of predictability, and is equal to the degree to which redundancy is present in the time series describing stock returns. We use Shannon’s entropy rate (approximating Kolmogorov-Sinai entropy) to measure this redundancy, and estimate it using the Lempel-Ziv algorithm, computing it with a running window approach over the entire price history of 50 companies listed on the Warsaw market which have gone bankrupt in the last few years. This enables us not only to compare the differences between predictability of price formation processes before and after their filing for bankruptcy, but also to compare the changes in predictability over time, as well as divided into different categories of companies and bankruptcies. There exists a large body of research analysing the efficiency of the whole market and the predictability of price changes en large, but only a few detailed studies analysing the influence of external stimulion the efficiency of price formation processes. This study fills this gap in the knowledge of financial markets, and their response to extreme external events.
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Campos, Lídio Mauro Lima, Jherson Haryson Almeida Pereira, Danilo Souza Duarte, and Roberto Célio Limão Oliveira. "Evolving deep neural networks for Time Series Forecasting." Learning and Nonlinear Models 18, no. 2 (June 30, 2021): 40–55. http://dx.doi.org/10.21528/lnlm-vol18-no2-art4.
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The aim of this paper is to introduce a biologically inspired approach that can automatically generate Deep Neural networks with good prediction capacity, smaller error and large tolerance to noises. In order to do this, three biological paradigms were used: Genetic Algorithm (GA), Lindenmayer System and Neural Networks (DNNs). The final sections of the paper present some experiments aimed at investigating the possibilities of the method in the forecast the price of energy in the Brazilian market. The proposed model considers a multi-step ahead price prediction (12, 24, and 36 weeks ahead). The results for MLP and LSTM networks show a good ability to predict peaks and satisfactory accuracy according to error measures comparing with other methods.
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Liu, Hong Yong, and Li Deng. "The Influence of "Limit House Price, Compete Land Price" Policy to Land Price under Auction." Advanced Materials Research 243-249 (May 2011): 6385–88. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.6385.
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The article mainly introduced how the land auction in our country impacted the sell price of land, and through analysising the influence of the policy in 2006 and 2010 which called “limit house price , compete land price” to land auction, got that this policy may control house price and land price in a short time. But in the long run, selling the land through auction had some problems, and this policy had difficulty in setting the price and the quality of the house couldn’t be guaranteed. According to the above questions, it proposed that it should through transforming government’s function, reasonably determining house price and strengthening the quality supervision of “limited price house” to solve the present situation of overpriced.
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Gabrielli, Laura, Paloma Taltavull de La Paz, and Armando Ortuño Padilla. "Long-term regional house prices cycles. A city-based index for Italy." Journal of European Real Estate Research 10, no. 3 (November 6, 2017): 303–30. http://dx.doi.org/10.1108/jerer-05-2017-0019.
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Purpose This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data. Design/methodology/approach This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals. Findings Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level. Research limitations/implications Data are measured as the average price in squared meters, and the resulting index is not quality controlled. Practical implications The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices. Originality/value This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.
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Piao, Ri, Deok-Joo Lee, and Taegu Kim. "Real-Time Pricing Scheme in Smart Grid Considering Time Preference: Game Theoretic Approach." Energies 13, no. 22 (November 23, 2020): 6138. http://dx.doi.org/10.3390/en13226138.
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Unbalanced power demand across time slots causes overload in a specific time zone. Various studies have proved that this can be mitigated through smart grid and price policy, but research on time preference is insufficient. This study proposed a real-time pricing model on a smart grid through a two-stage Stackelberg game model based on a utility function that reflects the user’s time preference. In the first step, the suppliers determine the profit-maximizing price, and then, the users decide the electricity usage schedule according to the given price. Nash equilibrium and comparative analysis of the proposed game explain the relationship between time preference, price, and usage. Additionally, a Monte Carlo simulation demonstrated the effect of the change in time preference distribution. The experimental results confirmed that the proposed real-time pricing method lowers peak-to-average ratio (PAR) and increases overall social welfare. This study is meaningful in that it presents a pricing method that considers both users’ and suppliers’ strategies with time preference. It is expected that the proposed method would contribute to a reduction in the need for additional power generation facilities through efficient operation of the smart grid.
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Berk, Niyazi, Sabriye Biçen, and Nadire Seyidova. "Study on Measuring of Real Estate Speculative Bubble: Evidence from Turkey." European Journal of Multidisciplinary Studies 5, no. 1 (May 19, 2017): 334. http://dx.doi.org/10.26417/ejms.v5i1.p334-338.
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The investor's expectation of future price increases on real estate is causing to further rise of prices. In the 1990s, Turkey’s real estate price / rental income ratio was around 10, now is between 17-20 years. On the other hand, as a result of insufficient innovation and incentive application of industry, some companies have left their core activity and moved to the consruction industry. Studies using time series analysis with Turkey’s data show that GDP growth and interest rates have a great impact on investment decisions of consruction companies. Using Turkstat, Bloomberg and Eurostat data, the empirical part of this study present the relationship between interest rate and GDP growth and consruction investments. The analysis will continue with cross-city-time-series analysis for a sample of 4 well-developed cities of turkey, in terms of construction investments. Finally, measuring the price-to-earnings ratio, the home price-to-rent ratio, the gross rental yield and the house ownership ratio will be compared to those of the metropolitan cities in Europe, whether there is a real estate bubble in Turkey or not.
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Thompson Chaudhry, Theresa, and Azam Amjad Chaudhry. "The Effects of Rising Food and Fuel Costs on Poverty in Pakistan." LAHORE JOURNAL OF ECONOMICS 13, Special Edition (September 1, 2008): 117–38. http://dx.doi.org/10.35536/lje.2008.v13.isp.a8.
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The dramatic increase in international food and fuel prices in recent times is a crucial issue for developing countries and the most vulnerable to these price shocks are the poorest segments of society. In countries like Pakistan, the discussion has focused on the impact of substantially higher food and fuel prices on poverty. This paper used PSLM and MICS household level data to analyze the impact of higher food and energy prices on the poverty head count and the poverty gap ratio in Pakistan. Simulated food and energy price shocks present some important results: First, the impact of food price increases on Pakistani poverty levels is substantially greater than the impact of energy price increases. Second, the impact of food price inflation on Pakistani poverty levels is significantly higher for rural populations as compared to urban populations. Finally, food price inflation can lead to significant increases in Pakistani poverty levels: For Pakistan as a whole, a 20% increase in food prices would lead to an 8% increase in the poverty head count.
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You, Peng Sheng, and Chung Ming Su. "A Deterministic Model for a Manufacture System with an Advance Purchase Discount." Materials Science Forum 505-507 (January 2006): 919–24. http://dx.doi.org/10.4028/www.scientific.net/msf.505-507.919.
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Most production planning models assume that the backorder or advance purchase price is the same as the regular purchase price. In practice, since most advance purchase customers may expect to purchase items at discount prices, the purchasing behavior of these customers may differ from the regular purchase customers. In the present paper, we developed a production planning model for dealing with a manufacturing system with an advance purchase discount. The objective of the paper is to maximize the unit time profit by jointly determining the advance purchase discount rate and the size of a production run.
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KITA, Eisuke, Masaaki Harada, and Takao Mizuno. "Application of Bayesian Network to stock price prediction." Artificial Intelligence Research 1, no. 2 (September 26, 2012): 171. http://dx.doi.org/10.5430/air.v1n2p171.
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Authors present the stock price prediction algorithm by using Bayesian network. The present algorithm uses the networktwice. First, the network is determined from the daily stock price and then, it is applied for predicting the daily stock pricewhich was already observed. The prediction error is evaluated from the daily stock price and its prediction. Second, thenetwork is determined again from both the daily stock price and the daily prediction error and then, it is applied for thefuture stock price prediction. The present algorithm is applied for predicting NIKKEI stock average and Toyota motorcorporation stock price. Numerical results show that the maximum prediction error of the present algorithm is 30% inNIKKEI stock average and 20% in Toyota Motor Corporation below that of the time-series prediction algorithms such asAR, MA, ARMA and ARCH models.
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Gerlach, J. C., G. Demos, and D. Sornette. "Dissection of Bitcoin’s multiscale bubble history from January 2012 to February 2018." Royal Society Open Science 6, no. 7 (July 2019): 180643. http://dx.doi.org/10.1098/rsos.180643.
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We present a detailed bubble analysis of the Bitcoin to US Dollar price dynamics from January 2012 to February 2018. We introduce a robust automatic peak detection method that classifies price time series into periods of uninterrupted market growth (drawups) and regimes of uninterrupted market decrease (drawdowns). In combination with the Lagrange Regularization Method for detecting the beginning of a new market regime, we identify three major peaks and 10 additional smaller peaks, that have punctuated the dynamics of Bitcoin price during the analysed time period. We explain this classification of long and short bubbles by a number of quantitative metrics and graphs to understand the main socio-economic drivers behind the ascent of Bitcoin over this period. Then, a detailed analysis of the growing risks associated with the three long bubbles using the Log-Periodic Power-Law Singularity (LPPLS) model is based on the LPPLS Confidence Indicators , defined as the fraction of qualified fits of the LPPLS model over multiple time windows. Furthermore, for various fictitious ‘present’ times t 2 before the crashes, we employ a clustering method to group the predicted critical times t c of the LPPLS fits over different time scales, where t c is the most probable time for the ending of the bubble. Each cluster is proposed as a plausible scenario for the subsequent Bitcoin price evolution. We present these predictions for the three long bubbles and the four short bubbles that our time scale of analysis was able to resolve. Overall, our predictive scheme provides useful information to warn of an imminent crash risk.
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PISTORIUS, MARTIJN, and JOHANNES STOLTE. "FAST COMPUTATION OF VANILLA PRICES IN TIME-CHANGED MODELS AND IMPLIED VOLATILITIES USING RATIONAL APPROXIMATIONS." International Journal of Theoretical and Applied Finance 15, no. 04 (June 2012): 1250031. http://dx.doi.org/10.1142/s0219024912500318.
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We present a new numerical method to price vanilla options quickly in time-changed Brownian motion models. The method is based on rational function approximations of the Black-Scholes formula. Detailed numerical results are given for a number of widely used models. In particular, we use the variance-gamma model, the CGMY model and the Heston model without correlation to illustrate our results. Comparison to the standard fast Fourier transform method with respect to accuracy and speed appears to favour the newly developed method in the cases considered. We present error estimates for the option prices. Additionally, we use this method to derive a procedure to compute, for a given set of arbitrage-free European call option prices, the corresponding Black-Scholes implied volatility surface. To achieve this, rational function approximations of the inverse of the Black-Scholes formula are used. We are thus able to work out implied volatilities more efficiently than one can by the use of other common methods. Error estimates are presented for a wide range of parameters.
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Mandal, S., B. C. Giri, and K. S. Chaudhuri. "Optimal batch production strategies under continuous price decrease and time discounting." Yugoslav Journal of Operations Research 17, no. 2 (2007): 165–75. http://dx.doi.org/10.2298/yjor0702165m.
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Single price discount in unit cost for bulk purchasing is quite common in reality as well as in inventory literature. However, in today's high-tech industries such as personal computers and mobile industries, continuous decrease in unit cost is a regular phenomenon. In the present paper, an attempt has been made to investigate the effects of continuous price decrease and time-value of money on optimal decisions for inventoried goods having time-dependent demand and production rates. The proposed models are developed over a finite time horizon considering both shortages and without shortages in inventory. Numerical examples are taken to illustrate the developed models and to examine the sensitivity of model parameters.
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Nestorenko, Tetyana, Mangirdas Morkunas, Jana Peliova, Artiom Volkov, Tomas Balezentis, and Dalia Streimkiene. "A New Model for Determining the EOQ under Changing Price Parameters and Reordering Time." Symmetry 12, no. 9 (September 14, 2020): 1512. http://dx.doi.org/10.3390/sym12091512.
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The present study deals with the modification of Wilson’s formulation by taking into account changes in the supply chain represented by the parameters of the model, namely varying delivery costs and price of goods stored. The four different models are presented. The proposed models avoid the main drawbacks of Wilson’s formulation—the constant price and reordering time—and discuss the case where varying parameters are used alongside discounting. The proposed models render lower costs under particular settings.