Journal articles: 'Spot price model calibration'

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BARLOW, MARTIN, YURI GUSEV, and MANPO LAI. "CALIBRATION OF MULTIFACTOR MODELS IN ELECTRICITY MARKETS." International Journal of Theoretical and Applied Finance 07, no. 02 (March 2004): 101–20. http://dx.doi.org/10.1142/s0219024904002396.

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Spot prices of electricity and other energy commodities are often modeled by multifactor stochastic processes. This poses a problem of estimating models' parameters based on historical data, i.e. calibrating them to markets. Here we show how a traditional tool of Kalman Filters can be successfuly applied to do this task. We study two mean-reverting log-spot price models and the Pilipovic model using correspondingly Kalman Filter the extended Kalman Filter. The results of applying this method to market data from several power exchanges are discussed.

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HIKSPOORS, SAMUEL, and SEBASTIAN JAIMUNGAL. "ENERGY SPOT PRICE MODELS AND SPREAD OPTIONS PRICING." International Journal of Theoretical and Applied Finance 10, no. 07 (November 2007): 1111–35. http://dx.doi.org/10.1142/s0219024907004573.

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In this article, we construct forward price curves and value a class of two asset exchange options for energy commodities. We model the spot prices using an affine two-factor mean-reverting process with and without jumps. Within this modeling framework, we obtain closed form results for the forward prices in terms of elementary functions. Through measure changes induced by the forward price process, we further obtain closed form pricing equations for spread options on the forward prices. For completeness, we address both an Actuarial and a risk-neutral approach to the valuation problem. Finally, we provide a calibration procedure and calibrate our model to the NYMEX Light Sweet Crude Oil spot and futures data, allowing us to extract the implied market prices of risk for this commodity.

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Aiube, Fernando Antonio Lucena, and Ariel Levy. "Recent movement of oil prices and future scenarios." Nova Economia 29, no. 1 (April 2019): 223–48. http://dx.doi.org/10.1590/0103-6351/4159.

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Abstract The recent movement of oil prices has brought many forecasts about what is coming in the near future. This is natural since the plunge in prices has been dramatic after 2014 and oil is an essential source of energy worldwide. This paper examines the probabilities of spot price scenarios. We model prices through stochastic processes focusing on the Schwartz-Smith model. The calibration is based on the term structure of future prices. Since the conditional distribution is log-normal we define the probability of a certain value of the spot price in a given time horizon. We found that the recovery of crude oil prices will be slow in the next four years. Moreover, the scenario of prices under US$ 20/barrel has the same probability as being greater than US$ 50/barrel. The methodology has many applications, mainly for government planning and for oil companies in their capital budget decisions.

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FOUQUE, JEAN-PIERRE, YURI F. SAPORITO, and JORGE P. ZUBELLI. "MULTISCALE STOCHASTIC VOLATILITY MODEL FOR DERIVATIVES ON FUTURES." International Journal of Theoretical and Applied Finance 17, no. 07 (November 2014): 1450043. http://dx.doi.org/10.1142/s0219024914500435.

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In this paper, we present a new method for computing the first-order approximation of the price of derivatives on futures in the context of multiscale stochastic volatility studied in Fouque et al. (2011). It provides an alternative method to the singular perturbation technique presented in Hikspoors & Jaimungal (2008). The main features of our method are twofold: firstly, it does not rely on any additional hypothesis on the regularity of the payoff function, and secondly, it allows an effective and straightforward calibration procedure of the group market parameters to implied volatilities. These features were not achieved in previous works. Moreover, the central argument of our method could be applied to interest rate derivatives and compound derivatives. The only pre-requisite of our approach is the first-order approximation of the underlying derivative. Furthermore, the model proposed here is well-suited for commodities since it incorporates mean reversion of the spot price and multiscale stochastic volatility. Indeed, the model was validated by calibrating the group market parameters to options on crude-oil futures, and it displays a very good fit of the implied volatility.

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Gonzalez, Jhonny, John Moriarty, and Jan Palczewski. "Bayesian calibration and number of jump components in electricity spot price models." Energy Economics 65 (June 2017): 375–88. http://dx.doi.org/10.1016/j.eneco.2017.04.022.

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Masala, Giovanni, Marco Micocci, and Andrea Rizk. "Hedging Wind Power Risk Exposure through Weather Derivatives." Energies 15, no. 4 (February 13, 2022): 1343. http://dx.doi.org/10.3390/en15041343.

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We introduce the industrial portfolio of a wind farm of a hypothetical company and its valuation consistent with the financial market. Next, we propose a static risk management policy originating from hedging against volumetric risk due to drops in wind intensity and we discuss the consequences. The hedging effectiveness firstly requires adequate modeling calibration and an extensive knowledge of these atypical financial (commodity) markets. In this hedging experiment, we find significant benefits for weather-sensitive companies, which can lead to new business opportunities. We provide a new financial econometrics approach to derive weather risk exposure in a typical wind farm. Our results show how accurate risk management can have a real benefit on corporate revenues. Specifically, we apply the spot market price simulation (SMaPS) model for the spot price of electricity. The parameters are calibrated using the prices of the French day-ahead market, and the historical series of the total hourly load is used as the final consumption. Next, we analyze wind speed and its relationship with electricity spot prices. As our main contribution, we demonstrate the effects of a hypothetical hedging strategy with collar options implemented against volumetric risk to satisfy demand at a specific time. Regarding the hedged portfolio, we observe that the “worst value” increases considerably while the earnings-at-risk (EaR) decreases. We consider only volumetric risk management, thus neglecting the market risk associated with electricity price volatility, allowing us to conclude that the hedging operation of our industrial portfolio provides substantial benefits in terms of the worst-case scenario.

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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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Jędrzejewski, Arkadiusz, Grzegorz Marcjasz, and Rafał Weron. "Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO." Energies 14, no. 11 (June 2, 2021): 3249. http://dx.doi.org/10.3390/en14113249.

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Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently, and then combining their forecasts can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based model is calibrated to the prices themselves. Here, we show that significant accuracy gains can also be achieved in the case of parameter-rich models estimated via the least absolute shrinkage and selection operator (LASSO). Moreover, we provide insights as to the order of applying seasonal decomposition and variance stabilizing transformations before model calibration, and propose two well-performing forecast averaging schemes that are based on different approaches for modeling the long-term seasonal component.

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Shao, Lingjie, and Kaili Xiang. "Valuation of Swing Options under a Regime-Switching Mean-Reverting Model." Mathematical Problems in Engineering 2019 (January 9, 2019): 1–14. http://dx.doi.org/10.1155/2019/5796921.

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In this paper, we study the valuation of swing options on electricity in a model where the underlying spot price is set to be the product of a deterministic seasonal pattern and Ornstein-Uhlenbeck process with Markov-modulated parameters. Under this setting, the difficulties of pricing swing options come from the various constraints embedded in contracts, e.g., the total number of rights constraint, the refraction time constraint, the local volume constraint, and the global volume constraint. Here we propose a framework for the valuation of the swing option on the condition that all the above constraints are nontrivial. To be specific, we formulate the pricing problem as an optimal stochastic control problem, which can be solved by the trinomial forest dynamic programming approach. Besides, empirical analysis is carried out on the model. We collect historical data in Nord Pool electricity market, extract the seasonal pattern, calibrate the Ornstein-Uhlenbeck process parameters in each regime, and also get market price of risk. Finally, on the basis of calibration results, a specific numerical example concerning all typical constraints is presented to demonstrate the valuation procedure.

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Di Francesco, Marco. "A General Gaussian Interest Rate Model Consistent with the Current Term Structure." ISRN Probability and Statistics 2012 (September 5, 2012): 1–16. http://dx.doi.org/10.5402/2012/673607.

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We describe an extension of Gaussian interest rate models studied in literature. In our model, the instantaneous spot rate is the sum of several correlated stochastic processes plus a deterministic function. We assume that each of these processes has a Gaussian distribution with time-dependent volatility. The deterministic function is given by an exact fitting to observed term structure. We test the model through various numeric experiments about the goodness of fit to European swaptions prices quoted in the market. We also show some critical issues on calibration of the model to the market data after the credit crisis of 2007.

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GLASSERMAN, PAUL, and QI WU. "FORWARD AND FUTURE IMPLIED VOLATILITY." International Journal of Theoretical and Applied Finance 14, no. 03 (May 2011): 407–32. http://dx.doi.org/10.1142/s0219024911006590.

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We address the problem of defining and calculating forward volatility implied by option prices when the underlying asset is driven by a stochastic volatility process. We examine alternative notions of forward implied volatility and the information required to extract these measures from the prices of European options at fixed maturities. We then specialize to the SABR model and show how the asymptotic expansion of the bivariate transition density in Wu (forthcoming) allows calibration of the SABR model with piecewise constant parameters and calculation of forward volatility. We then investigate empirically whether current option prices at multiple maturities contain useful information in predicting future option prices and future implied volatility. We undertake this investigation using data on options on the euro-dollar, sterling-dollar, and dollar-yen exchange rates. We find that prices across maturities do indeed have predictive value. Moreover, we find that model-based forward volatility extracts this predicative information better than a standard "model-free" measure of forward volatility and better than spot implied volatility. The enhancement to out-of-sample forecasting accuracy gained from model-based forward volatility is greatest at longer forecasting horizons.

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Guerini, Alice, Andrea Marziali, and Giuseppe De Nicolao. "MCMC calibration of spot‐prices models in electricity markets." Applied Stochastic Models in Business and Industry 36, no. 1 (July 2, 2019): 62–76. http://dx.doi.org/10.1002/asmb.2471.

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ANDRESEN, ARNE, FRED ESPEN BENTH, STEEN KOEKEBAKKER, and VALERIY ZAKAMULIN. "THE CARMA INTEREST RATE MODEL." International Journal of Theoretical and Applied Finance 17, no. 02 (March 2014): 1450008. http://dx.doi.org/10.1142/s0219024914500083.

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In this paper, we present a multi-factor continuous-time autoregressive moving-average (CARMA) model for the short and forward interest rates. This model is able to present an adequate statistical description of the short and forward rate dynamics. We show that this is a tractable term structure model and provides closed-form solutions to bond prices, yields, bond option prices, and the term structure of forward rate volatility. We demonstrate the capabilities of our model by calibrating it to a panel of spot rates and the empirical volatility of forward rates simultaneously, making the model consistent with both the spot rate dynamics and forward rate volatility structure.

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Gao, Xia, and Zhanxing Zhao. "A Minimum Variance Hedging Ratio Model Based on Nonlinear Grey Classification Model." Wireless Communications and Mobile Computing 2022 (February 28, 2022): 1–8. http://dx.doi.org/10.1155/2022/9848223.

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The risk transfer function of futures market is mainly realized by hedging strategy. Futures price yield and spot price yield tend to show different fluctuations before and during hedging, which leads to the distortion of hedging ratio, that is, the calculated hedging effect is weaker than the traditional hedging effect. On the basis of MV (minimum variance) hedging model, this paper introduces NGCM (nonlinear grey classification model) to solve the nonlinear correlation between futures and spot returns, which can improve the hedging effect. The results show that, due to the existence of basis, the price change model violates the linear assumption of OLS (ordinary least squares) parameters, and there is a problem of model missetting. It is estimated that HR (hedging ratio) should choose the price model, which can better depict the linkage between futures price and spot price. The effectiveness of HR in this study is higher than that of existing models. Applying this model to hedge can effectively avoid the spot price risk. Investors can reasonably choose the hedging model according to their own needs.

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Naipunya, J., I. Bhavani Devi, and D. Vishnusankar Rao. "Efficiency of chilli futures trading in terms of price discovery and price transmission." INTERNATIONAL RESEARCH JOURNAL OF AGRICULTURAL ECONOMICS AND STATISTICS 11, no. 2 (September 15, 2020): 137–43. http://dx.doi.org/10.15740/has/irjaes/11.2/137-143.

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This paper has examined the efficiency of futures trading of chilli in terms of price discovery and transmission. Seemingly unrelated regression” (SUR) model, Johansen’s multiple cointegrationtest, granger causality test and vector error correction model was applied to draw the results. Chilli spot market (Guntur) was efficient in price discovery. The Silbers and Garbage value of futures market was 0.0403 being significant at 1 per cent level (0.0037) indicating that futures market of chilli was inefficient in price discovery. The findings of the ADF test suggested that futures and spot prices of chilli attained stationarity at first difference. The co-integration test revealed the presence of one co-integrating equation and confirmed the long-run equilibrium relationship among futures and spot prices of chilli and spot markets came to short-run equilibrium as indicated by level of significance at 5 per cent i.e. (0.022), any disturbances in price would get corrected within 3 hours in spot markets as indicated by co-efficient values.

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Ye, Minghua, Rongming Wang, Guozhu Tuo, and Tongjiang Wang. "Crop price insurance in China: pricing and hedging using futures market." China Agricultural Economic Review 9, no. 4 (November 6, 2017): 567–87. http://dx.doi.org/10.1108/caer-12-2015-0178.

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Purpose The purpose of this paper is to demonstrate how crop price insurance premium can be calculated using an option pricing model and how insurers can transfer underwriting risks in the futures market. Design/methodology/approach Based on data from spot and futures market in China, this paper develops an improved B-S model for the calculation of crop price insurance premium and tests the possibility of hedging underwriting risks by insurance firms in the futures market. Findings The authors find that spot price of crops in China can be estimated with agricultural commodity futures prices, and can be taken as the insured price for crop price insurance. The authors also find that improved B-S model yields better estimation of crop price insurance premium than traditional B-S model when spot price does not follow geometric Brownian motion. Finally, the authors find that hedging can be one good alternative for insurance firms to manage underwriting risks. Originality/value This paper develops an improved B-S model that is data-driven in nature. Insured price of the crop price insurance, or the exercise price used in the B-S model, is estimated from a co-integration model built on spot and futures market price series. Meanwhile, distributional patterns of spot price series, one important factor determining the applicability of B-S model, is factored into the improved B-S model so that the latter is more robust and friendly to data with varied distributions. This paper also verifies the possibility of hedging of underwriting risks by insurance firms in the futures market.

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Wang, Lei, Min Wei, Heng Yang, Jinxian Li, Sainan Li, and Yunzhi Fei. "Optimization Model of Electricity Spot Market Considering Pumped Storage in China." Journal of Physics: Conference Series 2401, no. 1 (December 1, 2022): 012041. http://dx.doi.org/10.1088/1742-6596/2401/1/012041.

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Abstract As China’s electricity market continues to evolve, pumped hydro storage will participate in electricity spot market transactions. According to the latest price policy of pumped storage, pumped storage units will not participate in the spot market bidding for a long time and will be settled at the spot price. Aiming at maximizing the welfare of the whole society, this paper proposes a full dispatch model for pumped storage units to participate in China’s electricity spot market under the new power system. By transforming the day-ahead spot market clearing results into the boundary conditions of the real-time spot market, the model proposed in this paper realizes the fine adjustment of pumped storage units’ operation curve in the real-time market and further improves social welfare.

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Green, Hilary, Nino Kordzakhia, and Ruben Thoplan. "A Bivariate Model for Deman and Spot Price of Electricity." Advanced Materials Research 433-440 (January 2012): 3910–17. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3910.

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In this paper bivariate modelling methodology, solely applied to the spot price of electricity or demand for electricity in earlier studies, is extended to a bivariate process of spot price of electricity and demand for electricity. The suggested model accommodates common idiosyncrasies observed in deregulated electricity markets such as cyclical trends in price and demand for electricity, occurrence of extreme spikes in prices, and mean-reversion effect seen in settling of prices from extreme values to the mean level over a short period of time. The paper presents detailed statistical analysis of historical data of daily averages of electricity spot prices and corresponding demand for electricity. The data is obtained from the NSW section of Australian Energy Markets.

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SEDANA, WIRYA, KOMANG DHARMAWAN, and NI MADE ASIH. "MENENTUKAN HARGA KONTRAK BERJANGKA KOMODITAS KEDELAI MENGGUNAKAN MODEL MEAN REVERSION." E-Jurnal Matematika 5, no. 4 (November 30, 2016): 170. http://dx.doi.org/10.24843/mtk.2016.v05.i04.p137.

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It has been discussed in many literatures that commodity prices tend to follow mean reversion model. This means that when there is a jump price in certain time, the price will revert to the mean price in the future. In this research, the method to determine the existence of mean-reversion of soybean price dynamics is discussed. Then, the future contract of soybeans is calculated using mean-reversion simulation and the spot-future parity theorem. Both methods are applied to the closing price of soybeans for the period of 19 September 2011 to 28 April 2016. The results show that the future contract price calculated by Model Mean-Reversion simulation under estimate the future contract price determined by the spot-future parity theorem.

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Vedran Uran. "MATHEMATICAL MODEL OF THE ELECTRICITY PRICES ON THE SPOT MARKET." Journal of Energy - Energija 55, no. 2 (January 18, 2023): 202–17. http://dx.doi.org/10.37798/2006552386.

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The price on the electricity market is constantly changing. Owing to the characteristics of electricity market the future movement of the price is relatively hard to predict. For this reason the following three processes are applied: the process of the future electricity price movement according to the geometric Brownian motion, the mean reversion process, and the price spikes process. The paper discusses all three processes, including their definitions, formulas and applications, as well as their upsides and downsides.

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Gyamerah, Samuel Asante, and Philip Ngare. "Regime-Switching Model on Hourly Electricity Spot Price Dynamics." Journal of Mathematical Finance 08, no. 01 (2018): 102–10. http://dx.doi.org/10.4236/jmf.2018.81008.

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FENG, JIANFEN, XIAOWEI HUANG, JUYUE HOU, CHUNXIA WANG, and YAN ZENG. "CARBON BOND PRICING AND MODEL SELECTION." Singapore Economic Review 63, no. 02 (March 2018): 465–81. http://dx.doi.org/10.1142/s0217590817400215.

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This paper discusses how to value a carbon bond and how to choose models for it like CGNPC wind additional carbon benefits of medium-term notes, the first Carbon-bond in China. First, the fractional process with jumps is used to model CERs spot price in BlueNext market and to price a numerical carbon bond. Then, through sensitive analysis for model parameters, the fractional process without jumps is suggested to model CERs spot price finally, but the fractional process with jumps may be more suitable for EA in China. Furthermore, there also discusses how to choose discount rate for those derivatives which are paid off by one currency but its underlying is quoted by another.

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Helbawanti, Octaviana, and Masyhuri -. "Volatility and Market Integration of Spot-Forward Corn Price in Indonesia." Media Trend 14, no. 1 (April 2, 2019): 1–9. http://dx.doi.org/10.21107/mediatrend.v14i1.4379.

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This study aims to determine the volatility and market integration between the price of corn in the Indonesian spot market and futures market in the international market. The data used in this research is secondary data consisting of Indonesian corn spot price and corn forward price referring commodity exchange, Chicago. Data in the form of monthly time series in 2007 until 2016. ARCH / GARCH method is used to measure the volatility at spot and forward price, whereas the market integration of spot and forward corn is used Johansen Cointegration and Engel-Granger Causality method. The results show that spot and forward prices of corn occur high volatility. The best ARCH/GARCH model for spot price is GARCH (2,0) with the volatility value of 0,91 and for forward price is GARCH (2,0) with the volatility value of 1.12. It means that volatility of spot and forward influenced by the increase and fluctuations of spot and forward price two previous periods. Between the spot and forward market, there is market integration and a one-way causal relationship. The market integration indicates there is long-run relationship, while one way indicates the spot price effect on the forward price, not vice versa.

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Xin, He, and Zhang Guofu. "Dynamic Nonlinear Correlation Studies on Stock and Oil Market Based on Copula." Open Petroleum Engineering Journal 8, no. 1 (September 15, 2015): 405–9. http://dx.doi.org/10.2174/1874834101508010405.

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Employing the dataset of WTI oil spot price and stock price index in China，Brazil, India, US, German, France, UK and Japan, this paper obtains five subintervals of whole sample range through a nonparametric multiple change point algorithms. Furthermore, it analyzes dependence between oil spot price and stock price index through copula model and computes the value of VaR and ES based on simulation for every subinterval. It reveals that dependence between oil spot price and stock price index during financial crisis is an asymmetric tail dependence. The value of VaR and ES of the oil spot price and stock price index shows irregular fluctuation.

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Kumar Mahalik, Mantu, Debashis Acharya, and M. Suresh Babu. "Price discovery and volatility spillovers in futures and spot commodity markets." Journal of Advances in Management Research 11, no. 2 (July 29, 2014): 211–26. http://dx.doi.org/10.1108/jamr-09-2012-0039.

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Purpose – The purpose of this paper is to investigate empirically the price discovery and volatility spillovers in Indian spot-futures commodity markets. Design/methodology/approach – The study has used four futures and spot indices of Multi-Commodity Exchange, Mumbai. The study also employs vector error correction model (VECM) and bivariate exponential Garch model (EGARCH) to analyze the price discovery and volatility spillovers in Indian spot-futures commodity market. Findings – The VECM shows that agriculture future price index (LAGRIFP), energy future price index (LENERGYFP) and aggregate commodity index (LCOMDEXFP) effectively serve the price discovery function in the spot market implying that there is a flow of information from future to spot commodity markets but the reverse causality does not exist. There is no cointegrating relationship between metal future price index (LMETALFP) and metal spot price index (LMETALSP). Besides the bivariate EGARCH model indicates that although the innovations in one market can predict the volatility in another market, the volatility spillovers from future to the spot market are dominant in the case of LENERGY and LCOMDEX index while LAGRISP acts as a source of volatility toward the agri-futures market. Research limitations/implications – The results are aggregate in nature. Further study at disaggregated level will provide further insights on behavior of specific commodity prices and the price discovery process. Originality/value – The paper provides useful information about the evolution and structures of futures commodity trading in India, related literature and relevant methodology concerning the hypotheses.

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Benth, F. E., and L. Vos. "Pricing of Forwards and Options in a Multivariate Non-Gaussian Stochastic Volatility Model for Energy Markets." Advances in Applied Probability 45, no. 2 (June 2013): 572–94. http://dx.doi.org/10.1239/aap/1370870130.

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In Benth and Vos (2013) we introduced a multivariate spot price model with stochastic volatility for energy markets which captures characteristic features, such as price spikes, mean reversion, stochastic volatility, and inverse leverage effect as well as dependencies between commodities. In this paper we derive the forward price dynamics based on our multivariate spot price model, providing a very flexible structure for the forward curves, including contango, backwardation, and hump shape. Moreover, a Fourier transform-based method to price options on the forward is described.

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Benth, F. E., and L. Vos. "Pricing of Forwards and Options in a Multivariate Non-Gaussian Stochastic Volatility Model for Energy Markets." Advances in Applied Probability 45, no. 02 (June 2013): 572–94. http://dx.doi.org/10.1017/s0001867800006443.

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In Benth and Vos (2013) we introduced a multivariate spot price model with stochastic volatility for energy markets which captures characteristic features, such as price spikes, mean reversion, stochastic volatility, and inverse leverage effect as well as dependencies between commodities. In this paper we derive the forward price dynamics based on our multivariate spot price model, providing a very flexible structure for the forward curves, including contango, backwardation, and hump shape. Moreover, a Fourier transform-based method to price options on the forward is described.

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Yan, Yunxian, Lu Tian, and Yuejie Zhang. "Is Chinese or American maize price effective for trading and policy-making reference?" China Agricultural Economic Review 6, no. 3 (August 26, 2014): 470–84. http://dx.doi.org/10.1108/caer-05-2013-0080.

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Purpose – The purpose of this paper is to discover an effective maize price for trading and policy-making reference by assessing the price transmission of the US spot and futures maize prices to Chinese counterparts. Design/methodology/approach – The authors apply a systematic, quantitative method to analyze the integration between US and Chinese maize markets. Based on the residuals of the variables through error correction model, the directed acyclic graph (DAG) among six price variables is conducted. With consideration of the dependence on and direction of six price variables, the variance decomposition of each variable is calculated. Findings – This paper shows that the vertical price transmission passes from wholesale price to farm-gate price. The horizontal price transmission ranges from spot price to futures price at the domestic market and from the American spot price to domestic spot price, from the American spot price to domestic futures price and from the American futures price to domestic futures price. The American maize spot and futures prices, and in particular the spot price, have greater effects on domestic maize prices contemporaneously. It also indicates that the American spot price is the leader price in the long run at both maize markets. Originality/value – This paper contributes by using the DAG method in this paper. It also contributes by helping policy makers and market participants find the leading prices and offers insight into ways of gaining power of price setting in the maize market.

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Dang-Nguyen, S., and Y. Rakotondratsimba. "Control of price acceptability under the univariate Vasicek model." International Journal of Financial Engineering 03, no. 03 (September 2016): 1650014. http://dx.doi.org/10.1142/s2424786316500146.

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The valuation of the probability of a financial contract to be lower or higher than a given price under the univariate Vasicek model is discussed in this paper. This price restriction can be justified by consistency reasons, since some prices may not be coherent on a financial point of view, e.g. they imply negative yields, or thought as unreachable by the asset manager. At first, assuming that the pricing functions is monotone, the price constraints are formulated in terms of a threshold on the value of the spot rate process. Since this process is Gaussian, these limits are reformulated in terms of a barrier of the Gaussian increments. Next, once the thresholds are identified, the probability to satisfy the price restriction after the generation of the spot rate at one future date can be computed. Then, assuming that the bounds on the spot rate are constant during a Monte-Carlo simulation, the probability of generating a path of this process that does not satisfy the constraint is valued using some results related to the hitting times. Lastly, the proposed approach is applied to various interest rates sensitive contracts and is illustrated by some numerical examples.

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BJÖRK, TOMAS, MAGNUS BLIX, and CAMILLA LANDÉN. "ON FINITE DIMENSIONAL REALIZATIONS FOR THE TERM STRUCTURE OF FUTURES PRICES." International Journal of Theoretical and Applied Finance 09, no. 03 (May 2006): 281–314. http://dx.doi.org/10.1142/s0219024906003639.

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We consider HJM type models for the term structure of futures prices, where the volatility is allowed to be an arbitrary smooth functional of the present futures price curve. Using a Lie algebraic approach we investigate when the infinite dimensional futures price process can be realized by a finite dimensional Markovian state space model, and we give general necessary and sufficient conditions, in terms of the volatility structure, for the existence of a finite dimensional realization. We study a number of concrete applications including a recently developed model for gas futures. In particular we provide necessary and sufficient conditions for when the induced spot price is a Markov process. In particular we can prove that the only HJM type futures price models with spot price dependent volatility structures which generically possess a spot price realization are the affine ones. These models are thus the only generic spot price models from a futures price term structure point of view.

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Crespi, John M., and Tian Xia. "A Note on First-Price Sealed-Bid Cattle Auctions in the Presence of Captive Supplies." Agricultural and Resource Economics Review 44, no. 3 (December 2015): 340–45. http://dx.doi.org/10.1017/s1068280500005098.

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The authors present an analytical model of a first-price sealed-bid cattle auction in which a spot and coordinated markets are interconnected. The model reveals that the conventional wisdom that market coordination negatively affects the bid price in the spot market is an oversimplification. The relationships between key market variables impact bids and bid shading in complex ways. While captive supplies can lead to lower spot prices, the price reductions do not necessarily stem from an increase in market power due to contracting. The model emphasizes the importance of several variables for future empirical studies.

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BENTH, FRED ESPEN, and JŪRATĖ ŠALTYTĖ-BENTH. "THE NORMAL INVERSE GAUSSIAN DISTRIBUTION AND SPOT PRICE MODELLING IN ENERGY MARKETS." International Journal of Theoretical and Applied Finance 07, no. 02 (March 2004): 177–92. http://dx.doi.org/10.1142/s0219024904002360.

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We model spot prices in energy markets with exponential non-Gaussian Ornstein–Uhlenbeck processes. We generalize the classical geometric Brownian motion and Schwartz' mean-reversion model by introducing Lévy processes as the driving noise rather than Brownian motion. Instead of modelling the spot price dynamics as the solution of a stochastic differential equation with jumps, it is advantageous from a statistical point of view to model the price process directly. Imposing the normal inverse Gaussian distribution as the statistical model for the Lévy increments, we obtain a superior fit compared to the Gaussian model when applied to spot price data from the oil and gas markets. We also discuss the problem of pricing forwards and options and outline how to find the market price of risk in an incomplete market.

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Shao, Li-Peng, Jia-Jia Chen, Lu-Wen Pan, and Zi-Juan Yang. "A Credibility Theory-Based Robust Optimization Model to Hedge Price Uncertainty of DSO with Multiple Transactions." Mathematics 10, no. 23 (November 23, 2022): 4420. http://dx.doi.org/10.3390/math10234420.

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This paper addresses the deregulated electricity market arising in a distribution system with an electricity transaction. Under such an environment, the distribution system operator (DSO) with a distributed generator faces the challenge of electricity price uncertainty in a spot market. In this context, a credibility theory-based robust optimization model with multiple transactions is established to hedge the uncertain spot price of the DSO. Firstly, on the basis of credibility theory, the spot price is taken as a fuzzy variable and a risk aversion-based fuzzy opportunity constraint is proposed. Then, to exploit the resiliency of multiple transactions on hedging against uncertain spot price, the spot market, option contract and bilateral contract integrating power flow constraints are studied, because it is imperative for DSO to consider the operational constraints of the local network in the electricity market. Finally, the clear equivalence class is adopted to transform the risk aversion constraint into a deterministic robust optimization one. Under the premise of considering the expected cost of the DSO, the optimal electricity transaction strategy that maximizes resistance to uncertain spot price is pursued. The rationality and effectiveness of the model are verified with a modified 15-node network. The results show that the introduction of option contracts and bilateral contracts reduces the electricity transaction cost of DSO by USD 28.5. In addition, under the same risk aversion factor, the cost of the proposed model is reduced by USD 195.18 compared with robust optimization, which avoids the over-conservatism of traditional robust optimization.

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Kang, Seok-Kyu. "A Study on the Price Discovery in Korea Stock Index Markets: KODEX200, KOSPI200, and KOSPI200 Futures." Journal of Derivatives and Quantitative Studies 17, no. 3 (August 31, 2009): 67–97. http://dx.doi.org/10.1108/jdqs-03-2009-b0003.

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This paper examines the price discovery process among the Korea stock index markets using the vector error correction model (VECM) and the multivariate generalized auto regressive conditional heteroskedasticity (M-GARCH) model. The minute-by-minute price series of the KOSPI200 index, KOSPI200 futures, and KODEX200 are cointegrated. The empirical results are summarized as follows: First, VECM estimation results indicate that when the cointegrating relationship is perturbed by the arrival of ntis, the KODEX200(ETF) does not adjusted to restore equilibrium. This is the task of the KOSPI200 futures and spot. These two index securities use the KODEX200 to represent the ntioequilibrium price, with the KOSPI200 futures responding faster than the KOSPI200 spot. When the cointegrating relationship betweeiesOSPI200 spot and futues is perturbed by the arrival of ntis, the KOSPI200 spot does adjusted to restore equilibrium. Next, the results from the multivariate GARCH modes indicate that the volatilities of esOSPI200 spot and futures markets suggest unidirectiona1volatility spillover from KOSPI200 futures to KOSPI200 spot. KODEX200(ETF) volatilities spill over bothesOSPI200 spot and futures markets. and this happen in the reverse direction with a strong effect from the KODEX200 to KOSP200 futures and spot. The overall findings indicate that the KODEX200(ETF) market dominates KOSPI200 futures and spot in the price discovery process. The regulation of Instutional traders on trading on futures markets explains its superior price discovery function.

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DIMITROFF, GEORGI, STEFAN LORENZ, and ALEXANDER SZIMAYER. "A PARSIMONIOUS MULTI-ASSET HESTON MODEL: CALIBRATION AND DERIVATIVE PRICING." International Journal of Theoretical and Applied Finance 14, no. 08 (December 2011): 1299–333. http://dx.doi.org/10.1142/s021902491100653x.

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We propose a parsimonious multi-asset Heston model and provide an easy-to-implement calibration algorithm. The model is customized to pricing multi-asset options in markets with liquidly traded single-asset options but no liquidly traded cross-asset options. In this situation, single-asset model parameters can be calibrated from option price data, however, cross-asset parameters cannot. We formulate a parsimonious model specification such that all single-asset models are Heston models, which are affine allowing for efficient calibration of the respective parameters. The single-asset models are correlated using cross-asset correlations only. Cross-asset correlations are observable, in contrast to correlations of latent variables such as volatilities, and serve as basis for calibration. A hybrid calibration approach for identifying the model parameters consistent with option price data and asset price data is outlined and illustrated by a case study. In banking practice the approach is referred to as correlation adjustment.

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Dong, Jun, Xihao Dou, Aruhan Bao, Yaoyu Zhang, and Dongran Liu. "Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China." Sustainability 14, no. 13 (June 25, 2022): 7767. http://dx.doi.org/10.3390/su14137767.

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With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology.

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Pani, Upananda, Ştefan Cristian Gherghina, Mário Nuno Mata, Joaquim António Ferrão, and Pedro Neves Mata. "Does Indian Commodity Futures Markets Exhibit Price Discovery? An Empirical Analysis." Discrete Dynamics in Nature and Society 2022 (March 8, 2022): 1–14. http://dx.doi.org/10.1155/2022/6431403.

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Price discovery function analyses the dynamics of futures and spot price behavior in an asset’s intertemporal dimensions. The present study examines the price discovery function of the bullion, metal, and energy commodity futures and spot prices through the Granger causality and Johansen–Juselius cointegration tests. The Granger causality test results show bidirectional causality between the spot and futures returns for gold, silver, aluminum, lead, nickel, and zinc. The Johansen cointegration test shows that spot and futures prices are in the long-run equilibrium path for silver, aluminum, lead, nickel, zinc, crude oil, and natural gas. The vector error correction model results suggest that both the spot and futures markets are equally efficient in price discovery for the nickel. The spot market leads the futures market in price discovery for copper and zinc. However, the futures market leads the spot market in price discovery for silver, aluminum, and lead. The findings of the study suggest the market participants for implementing hedging and arbitrage strategies. It also helps the market regulators to examine the stability of these rapidly growing commodity futures markets in India.

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Monteiro, Claudio, Ignacio J. Ramirez-Rosado, and L. Alfredo Fernandez-Jimenez. "A strategy for electricity buyers in futures markets." E3S Web of Conferences 152 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202015203007.

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This paper presents an original trading strategy for electricity buyers in futures markets. The strategy applies a medium-term electricity price forecasting model to predict the monthly average spot price which is used to evaluate the Risk Premium for a physical delivery under a monthly electricity futures contract. The proposed trading strategy aims to provide an advantage relatively to the traditional strategy of electricity buyers (used as benchmark), anticipating the good/wrong decision of buying electricity in the futures market instead in the day-ahead market. The mid-term monthly average spot price forecasting model, which supports the trading strategy, uses only information available from futures and spot markets at the decision moment. Both the new trading strategy and the monthly average spot price forecasting model, proposed in this paper, have been successfully tested with historical data of the Iberian Electricity Market (MIBEL), although they could be applied to other electricity markets.

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BENTH, FRED ESPEN, and SALVADOR ORTIZ-LATORRE. "A CHANGE OF MEASURE PRESERVING THE AFFINE STRUCTURE IN THE BARNDORFF-NIELSEN AND SHEPHARD MODEL FOR COMMODITY MARKETS." International Journal of Theoretical and Applied Finance 18, no. 06 (September 2015): 1550038. http://dx.doi.org/10.1142/s0219024915500387.

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For a commodity spot price dynamics given by an Ornstein–Uhlenbeck (OU) process with Barndorff-Nielsen and Shephard stochastic volatility, we price forwards using a class of pricing measures that simultaneously allow for change of level and speed in the mean reversion of both the price and the volatility. The risk premium is derived in the case of arithmetic and geometric spot price processes, and it is demonstrated that we can provide flexible shapes that are typically observed in energy markets. In particular, our pricing measure preserves the affine model structure and decomposes into a price and volatility risk premium. In the geometric spot price model, we need to resort to a detailed analysis of a system of Riccati equations, for which we show existence and uniqueness of solution and asymptotic properties that explain the possible risk premium profiles. Among the typical shapes, the risk premium allows for a stochastic change of sign, and can attain positive values in the short end of the forward market and negative in the long end.

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Kang, Seok Kyu. "The Unbiasedness and Hedging Effectiveness in KOSPI200 Futures Market." Journal of Derivatives and Quantitative Studies 15, no. 1 (May 31, 2007): 73–100. http://dx.doi.org/10.1108/jdqs-01-2007-b0003.

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This study is to examine the unblasedness hypothesis and hedging effectiveness in KOSPI20() futures market. The unbiasedness and efficiency hypothesis is carried out using a cointegration methodology. And hedging effectiveness is measured by comparing hedging performance of the naive hedge model, OLS hedge model. and constant correlation bivariate GARCH (1. 1) hedge model based on rolling windows. The sample period covers from May. 3. 1996 to December. 8, 2005. The empirical results are summarized as follows: First, there exists the cOintegrating relationship between realized spot prices and futures prices of the 10 day. 22 day. 44 day. and 59 day prior to maturity. Second. futures prices of backward the 10 day. 22 day. 44 day from maturity provide unbiased forecasts of the realized spot prices. The KOSPI200 futures price is likely to predict accurately future KOSPI200 spot prices without the trader having to pay a risk premium for the privilege of trading the contract. Third. for shorter maturity. the futures price appears to be the best forecaster of spot price. Forth, bivariate GARCH hedging effectiveness outperforms the naive and OLS hedging effectiveness. The implications of these findings show that KOSPI200 futures market behaves as unbiased predictor of future spot price and risk management instrument of KOSPI200 spot portfolio.

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Raffensperger, J. F., and M. Milke. "A design for a fresh water spot market." Water Supply 5, no. 6 (December 1, 2005): 217–24. http://dx.doi.org/10.2166/ws.2005.0067.

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This paper proposes a design for a fresh water spot market. The design has the groundwater of the New Zealand Canterbury Plains in mind, but would be appropriate for almost any region. In our market design, a market manager will operate a hydrology model and a Linear Program (LP), based on widely available software. The hydrogeology model calculates the effects of users' proposed abstraction rates. The LP finds allocations that maximise the sum of buyer and seller surplus, while satisfying environmental constraints expressed in terms of hydraulic heads and gradients in specific locations. The model produces a price for every location of interest. The price at a given location is the shadow price on the associated user's supply of water. Users are then charged their allocation times the market price at their location, and the market clears. We have written the software and are beginning testing.

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Popovska, Ekaterina, and Galya Georgieva-Tsaneva. "ARIMA Model for Day-Ahead Electricity Market Price Forecasting." Innovative STEM Education 4, no. 1 (June 10, 2022): 149–61. http://dx.doi.org/10.55630/stem.2022.0418.

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Electricity price forecasting becomes a significant challenge on a day-to-day basis and price variations are even more volatile on an hourly basis. Therefore, this paper is used several approaches to analyze the Bulgarian hourly electricity price dynamics in the day-ahead market. Proper analysis crucially depends on the choice of an adequate model. Reviewed are the factors which may influence the electricity spot prices and characteristics of the time series of prices. Methods include and variety of modeling approaches that are applied and evaluated for forecasting electricity prices such as time-series models and regression models. The forecasting technique is to model day-ahead spot prices using well known ARIMA/SARIMA model including stationarity checks, seasonal decompose, differencing, autoregressive modeling, and autocorrelation to analyze and forecast time series hourly data. For each approach, model estimates and forecasts are developed using hourly price data, reshaped, and aggregated data on a daily and monthly basis for the Bulgarian day-ahead market.

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Lin, Ching-Chung, Shen-Yuan Chen, Dar-Yeh Hwang, and Chien-Fu Lin. "Does Index Futures Dominate Index Spot? Evidence from Taiwan Market." Review of Pacific Basin Financial Markets and Policies 05, no. 02 (June 2002): 255–75. http://dx.doi.org/10.1142/s021909150200078x.

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By utilizing vector error correction model (VECM) and EGARCH model, this article uses 5-minute intraday data to examine the interaction of return and volatility between Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the newly introduced TAIEX futures. VECM model shows that there exists bi-directional Granger causality between index spot and index futures markets, but spot market plays a more important role in price discovery. The results of impulse response function and information share indicate that most of the price discovery happens in index spot market. The evidence of EGRACH shows that the impacts of spot and futures innovations are asymmetrical, and the volatility spillovers between spot and futures markets are bi-directional. However, the information flow from spot to futures is stronger. These results suggest that the TAIEX spot market dominates the TAIEX futures market in terms of return and volatility.

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Wu, Congxin, Xinyu Wang, Shan Luo, Jing Shan, and Feng Wang. "Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data." Applied Sciences 10, no. 23 (November 25, 2020): 8393. http://dx.doi.org/10.3390/app10238393.

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This article takes into account the form of mixed data as well as the peak and thick tail characteristics contained in the data characteristics, expands the GARCH-MIDAS (Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling) model, establishes a new GARCH-MIDAS model with the residual term of the skewed-t distribution, and analyzes the influence factors of crude oil futures price volatility, which can better explain the changing laws of crude oil price volatility. The results show the following: First, the low-frequency factors include crude oil production, consumption, inventory, and natural gas spot price, and the high-frequency factors include on-market trading volume and off-market spot price, which can significantly explain the volatility of oil price. Second, low-frequency factors include crude oil inventory, consumption, crude oil production, and speculative factors, and high-frequency factors include crude oil spot price and substitute prices. The increase in the volatility of trading volume is significantly positively correlated with oil price volatility, and the overall volatility model outperforms the horizontal effect model. Third, from the perspective of the combined effect of a single factor level and volatility, we find that supply and demand are the low-frequency factors; the trading volume of on-market factors, natural gas price, and crude oil spot price of off-market factors, among the high-frequency factors, are the most important factors affecting oil price volatility. Fourth, from the perspective of high-frequency and low-frequency effects combined, there is no significant difference between the various factor models, which shows that the mixed effect model of high and low frequency models has advantages in terms of the stability of the estimation results.

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Xiang, Ying, and Xiao Hong Zhuang. "Application of ARIMA Model in Short-Term Prediction of International Crude Oil Price." Advanced Materials Research 798-799 (September 2013): 979–82. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.979.

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International crude oil price is the referential scale of spot crude oil price and refined oil price. This paper made an analysis and prediction of Brent crude oil price by ARIMA model based on its price data from November 2012 to April 2013. It indicated that model ARIMA (1,1,1) possessed good prediction effect and can be used as short-term prediction of International crude oil price.

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Kang, Sang Hoon, and Seong-Min Yoon. "Volatility Spillover between the KOSPI 200 Spot and Futures Markets Using the VECM-DCC-GARCH Model." Journal of Derivatives and Quantitative Studies 19, no. 3 (August 31, 2011): 233–49. http://dx.doi.org/10.1108/jdqs-03-2011-b0001.

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This paper investigates the price discovery, volatility spillover, and asymmetric volatility spillover effects between the KOSPI 200 market and its futures contracts market. The investigation was performed using the VECM-DCC-GARCH approach. In the case of returns, we found a significant unidirectional information flow from the futures market to the spot market; this implies that the KOSPI 200 futures market plays an important role on the price discovery in the spot market. In addition, we found a strong bi-directional casualty involving the volatility interaction between the spot and futures markets; this implies that market volatility originating in the spot market will influence the volatility of the futures market and vice versa. We also found strong asymmetric volatility spillover effects between the two markets.

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Qin, Jieye, Christopher J. Green, and Kavita Sirichand. "Spot–Futures Price Adjustments in the Nikkei 225: Linear or Smooth Transition? Financial Centre Leadership or Home Bias?" Journal of Risk and Financial Management 16, no. 2 (February 12, 2023): 117. http://dx.doi.org/10.3390/jrfm16020117.

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This paper studies price discovery in Nikkei 225 markets through the nonlinear smooth transition price adjustments between spot and future prices and across all three futures markets. We test for smooth transition nonlinearity and employ an exponential smooth transition error correction model (ESTECM) with exponential generalised autoregressive conditional heteroscedasticity (EGARCH), allowing for the effects of transaction costs, heterogeneity, and asymmetry in Nikkei price adjustments. We show that the ESTECM-EGARCH is the appropriate model as it offers new insights into Nikkei price dynamics and information transmission across international markets. For spot–futures price dynamics, we find that futures led spot prices before the crisis, but spot prices led afterwards. This can be explained by the lower level of heterogeneity in the underlying spot transaction costs after the crisis. For cross-border futures prices, the foreign exchanges (Chicago and Singapore) lead in price discovery, which can be attributed to their roles as global information centres and their flexible trading conditions, such as a more heterogeneous structure of transaction costs. The foreign leadership is robust to the use of linear or nonlinear models, the time differences between Chicago and the other markets, and the long-run liquidity conditions of the Nikkei futures markets, and strongly supports the international centre hypothesis.

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Mallika, Mathew, and M. M. Sulphey. "Gold Exchange Traded Fund - Price Discovery and Performance Analysis." Scientific Annals of Economics and Business 65, no. 4 (December 1, 2018): 477–95. http://dx.doi.org/10.2478/saeb-2018-0024.

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Abstract The paper aims to examine the price discovery process and the performance of Gold Exchange Traded Funds especially with respect to two Gold ETFs, namely, Goldman Sachs Gold Exchange Traded Scheme (GoldBeEs) and SBI Gold Exchange Traded Scheme (SBIGETS), for the period 2009 – 2016. The study has employed Johansen cointegration and Johansen’s Vector Error Correction Model (VECM) for the price discovery analysis. The results of VECM reveal that the spot prices lead the Gold ETFs price during the study period. Tracking Error analysis shows that Gold ETFs have neither outperformed nor underperformed the spot price. Price Deviation analysis indicates that Gold ETFs are trading on an average lower than the spot price of gold. The entire analysis reveals that although the price discovery takes place in the spot market, Gold ETFs have performed as well as physical gold and the slight difference in price with that of Gold is only because of certain fees, which are applicable in the management of Gold ETFs.

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Growitsch, Christian, Rabindra Nepal, and Marcus Stronzik. "Price Convergence and Information Efficiency in German Natural Gas Markets." German Economic Review 16, no. 1 (February 1, 2015): 87–103. http://dx.doi.org/10.1111/geer.12034.

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Abstract In 2007, Germany changed network access regulation in the natural gas sector and introduced a so-called entry-exit system. The spot market effects of the reregulation remain to be examined. We use cointegration analysis and a state space model with time-varying coefficients to study the development of natural gas spot prices in the two major trading hubs in Germany and the interlinked spot market in the Netherlands. To analyse information efficiency in more detail, the state space model is extended to an error correction model. Overall, our results suggest a reasonable degree of price convergence between the corresponding hubs. Market efficiency in terms of information processing has increased considerably among Germany and the Netherlands.

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Heng, Wang, and Xu Qi. "Procurement Strategies Using Portfolio Approach Based on Options and Spot Markets Procurement." International Journal of Business and Management 12, no. 10 (September 17, 2017): 212. http://dx.doi.org/10.5539/ijbm.v12n10p212.

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The paper explored the conditions for retailers to implement option contract and the strategies to make joint purchase in spot market. Under the condition of uncertain market demands, a joint purchase model integrating batch ordering, option contract and spot market has been developed. Considering price fluctuation, the conditions for implementing option contract-based ordering have been studied; the impacts of price fluctuation and option execution price on retailers optimal ordering of joint purchase have been analyzed as well. The result shows that if a retailer adopts a joint purchase strategy, certain constraints need to be met. Otherwise, it is more conductive for the retailer to maximize revenues by adopting a single purchase order. When the spot market is involved, the total order quantity and the order quantity of option contract are negatively correlated with the option execution price and are positively correlated with the spot price fluctuation; and, the order quantity of bulk order contract is positively correlated with the option execution price and is negatively correlated with the spot price fluctuation.

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Journal articles on the topic 'Spot price model calibration'

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