Relevant bibliographies by topics / Spot price model calibration

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spot price model calibration'

Author: Grafiati
Published: 9 March 2023

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Journal articles on the topic "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 (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 (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 (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 (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 (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 (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 (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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Dissertations / Theses on the topic "Spot price model calibration"

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CALDANA, RUGGERO. "Spread and basket option pricing: an application to interconnected power markets." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2012. http://hdl.handle.net/10281/39422.

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An interconnector is an asset that gives the owner the right, but not the obligation, to transmit electricity between two locations each hour of the day over a prefixed time period. The financial value of the interconnector is given by a series of options that are written on the price differential between two electricity markets, that is, a strip of European options on an hourly spread. Since the hourly forward price is not directly observable on the market, Chapter 1 proposes a practical procedure to build an hourly forward price curve, fitting both base load and peak load forward quotations. One needs a stochastic model, a valuation formula, and a calibration method to evaluate interconnection capacity contracts. To capture the main features of the electricity price series, we model the energy price log-returns for each hour with a non-Gaussian mean-reverting stochastic process. Unfortunately no explicit solution to the spread option valuation problem is available. Chapter 2 develops a method for pricing the generic spread option in the non-Gaussian framework by extending the Bjerksund and Stensland (2011) approximation to a Fourier transform framework. We also obtain an upper bound on the estimation error. The method is applicable to models in which the joint characteristic function of the underlying assets is known analytically. Since an option on the difference of two prices is a particular case of a basket option, Chapter 3 extends our results to basket option pricing, obtaining a lower and an upper bound on the estimated price. We propose a general lower approximation to the basket option price and provide an upper bound on the estimation error. The method is applicable to models in which the joint characteristic function of the underlying assets and the geometric average is known. We test the performance of these new pricing algorithms, considering different stochastic dynamic models. Finally, in Chapter 4, we use the proposed spread option pricing method to price interconnectors. We show how to set up a calibration procedure: A market-coherent calibration is obtained, reproducing the hourly forward price curve. Finally, we present several examples of interconnector capacity contract valuation between European countries.
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Blöchlinger, Lea. "Power Prices - A Regime-Switching Spot/Forward Price Model with Kim Filter Estimation." kostenfrei, 2008. http://www.biblio.unisg.ch/www/edis.nsf/wwwDisplayIdentifier/3442.

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AMARAL, LUIZ FELIPE MOREIRA DO. "USING LINEAR AND NON-LINEAR APPROACHES TO MODEL THE BRAZILIAN ELECTRICITY SPOT PRICE SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2003. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3727@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Nesta dissertação, estratégias de modelagem são apresentadas envolvendo modelos de séries temporais lineares e não lineares para modelar a série do preço spot no mercado elétrico brasileiro. Foram usados, dentre os lineares, os modelos ARIMA(p,d,q) proposto por Box, Jenkins e Reinsel (1994) e os modelos de regressão dinâmica. Dentre os não lineares, o modelo escolhido foi o STAR desenvolvido, inicialmente, por Chan e Tong (1986) e, posteriormente, por Teräsvista (1994). Para este modelo, testes do tipo Multiplicador de Lagrange foram usados para testar linearidade, bem como para avaliar os modelos estimados. Além disso, foi também utilizada uma proposta para os valores iniciais do algoritmo de otimização, desenvolvido por Franses e Dijk (2000). Estimativas do filtro de Kalman suavizado foram usadas para substituir os valores da série de preço durante o racionamento de energia ocorrido no Brasil.<br>In this dissertation, modeling strategies are presented involving linear and non-linear time series models to model the spot price of Brazil s electrical energy market. It has been used, among the linear models, the modeling approach of Box, Jenkins and Reinsel (1994) i.e., ARIMA(p,d,q) models, and dynamic regression. Among the non-linear ones, the chosen model was the STAR developed, initially, by Chan and Tong (1986) and, later, by Teräsvirta (1994). For this model, the Lagrange Multipliers test, to measure the degree of non linearity of the series , as well as to evaluate the estimated model was used. Moreover, it was also used a proposal for the initial values of the optimization algorithm, developed by Franses and Dijk (2000). The smoothed Kalman filter estimates were used in order to provide values for the spot price series during the energy shortage period.
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Talasli, Irem. "Stochastic Modeling Of Electricity Markets." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614034/index.pdf.

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Day-ahead spot electricity markets are the most transparent spot markets where one can find integrated supply and demand curves of the market players for each settlement period. Since it is an indicator for the market players and regulators, in this thesis we model the spot electricity prices. Logarithmic daily average spot electricity prices are modeled as a summation of a deterministic function and multi-factor stochastic process. Randomness in the spot prices is assumed to be governed by three jump processes and a Brownian motion where two of the jump processes are mean reverting. While the Brownian motion captures daily regular price movements, the pure jump process models price shocks which have long term effects and two Ornstein Uhlenbeck type jump processes with different mean reversion speeds capturing the price shocks that affect the price level for relatively shorter time periods. After removing the seasonality which is modeled as a deterministic function from price observations, an iterative threshold function is used to filter the jumps. The threshold function is constructed on volatility estimation generated by a GARCH(1,1) model. Not only the jumps but also the mean reverting returns following the jumps are filtered. Both of the filtered jump processes and residual Brownian components are estimated separately. The model is applied to Austrian, Italian, Spanish and Turkish electricity markets data and it is found that the weekly forecasts, which are generated by the estimated parameters, turn out to be able to capture the characteristics of the observations. After examining the future contracts written on electricity, we also suggest a decision technique which is built on risk premium theory. With the help of this methodology derivative market players can decide on taking whether a long or a short position for a given contract. After testing our technique, we conclude that the decision rule is promising but needs more empirical research.
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Štork, Zbyněk. "Term Structure of Interest Rates: Macro-Finance Approach." Doctoral thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-125158.

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Thesis focus on derivation of macro-finance model for analysis of yield curve and its dynamics using macroeconomic factors. Underlying model is based on basic Dynamic Stochastic General Equilibrium DSGE approach that stems from Real Business Cycle theory and New Keynesian Macroeconomics. The model includes four main building blocks: households, firms, government and central bank. Log-linearized solution of the model serves as an input for derivation of yield curve and its main determinants -- pricing kernel, price of risk and affine term structure of interest rates -- based on no-arbitrage assumption. The Thesis shows a possible way of consistent derivation of structural macro-finance model, with reasonable computational burden that allows for time varying term premia. A simple VAR model, widely used in macro-finance literature, serves as a benchmark. The paper also presents a brief comparison and shows an ability of both models to fit an average yield curve observed from the data. Lastly, the importance of term structure analysis is demonstrated using case of Central Bank deciding about policy rate and Government conducting debt management.
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Hu, Hsu-Ning, and 胡緒寧. "The Relative Price Between Index Spot And Index Futures Using MS-AR(1) Model." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/jsgk6v.

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碩士<br>淡江大學<br>財務金融學系碩士在職專班<br>95<br>Because of the high liquidity and lower fee, the index futures become the favorable tools for the purpose of hedging, arbitraging and speculating. In this paper, we use the weekly data of spot price and futures price from the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the MSCI Taiwan Index to investigate the behavior of the relative price between the spot and futures. We also check the relationship between the volatility of return of both spot and futures and the relative price. The empirical results indicated that : (1) Both relative price represent high and low volatility state of the Markov process. (2) At high volatility state, the relative price represent more negative effect in the Taiwan market and more positive effect in the Singapore market. (3) The probabilities of state persistence are very high in both markets. (4) The relationship between the volatility of return and the relative price are different in both markets.
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Chen, Hung-Chung, and 陳弘忠. "Using the Application of Grey Relational Analysis and Artifical Neural Network to Establish an International Spot Gold Price Forecasting Model." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/29456059936645785233.

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碩士<br>義守大學<br>工業管理學系<br>102<br>The bankruptcy of Lehman Brothers in 2008 triggered the financial tsunami making the world’s central banks to increase the need for the reservation of gold. People are worried about the economic uncertainty caused by the financial tsunami, so they have more demand for capital preservation. As one of the international currency in circulation, gold is the first choice of investors among the general public. The international price of gold from $ 1,000 per ounce since 2008 has risen to nearly $ 2,000 writhin just 3 to 4 years, so the appreciation magnitude can not be ignored. Thus, how to accurately predict the price of gold is what the modern investors highly expect. In view of the methods of investor’s predictions on the international spot gold prices, which refer to mathematical-technical analysis and time series analysis, it inevitably has its limitations. This study attempts gray relational analysis and artificial neural network on the basis, analog to artificial intelligence, including the factors of the price of oil, the stock market, the dollar exchange rate and other factors and technical indicators for gray relational analysis and artificial neural networks prediction, to break through the limitations of traditional technical analysis and time series analysis, to improve forecast accuracy, and to help investors make beneficial and long-term investment reference.
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De, Beer Johannes Scheepers. "The impact of single stock futures on the South African equity market." Diss., 2008. http://hdl.handle.net/10500/1339.

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Text in English with summaries in English and Afrikaans<br>The introduction of single stock futures to a market presents the opportunity to assess an individual company's response to futures trading directly, in contrast to the market-wide impact obtained from index futures studies. Thirty-eight South African companies were evaluated in terms of a possible price, volume, and volatility effect due to the initial trading of their respective single stock futures contracts. An event study revealed that SSF trading had little impact on the underlying share prices. A normalised volume comparison pre to post SSF trading showed a general increase in spot market trading volumes. The volatility effect was the main focus of this study with a GARCH(1,1) model establishing a volatility structure (pattern of behaviour) per company. Results showed a reduction in the level and changes in the structure of spot market volatility. In addition, a dummy variable regression could find no evidence of an altered company-market relationship (systematic risk) post futures.<br>Business Management<br>M.Com. (Business Management)
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El-Khatib, Mayar. "Highway Development Decision-Making Under Uncertainty: Analysis, Critique and Advancement." Thesis, 2010. http://hdl.handle.net/10012/5741.

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While decision-making under uncertainty is a major universal problem, its implications in the field of transportation systems are especially enormous; where the benefits of right decisions are tremendous, the consequences of wrong ones are potentially disastrous. In the realm of highway systems, decisions related to the highway configuration (number of lanes, right of way, etc.) need to incorporate both the traffic demand and land price uncertainties. In the literature, these uncertainties have generally been modeled using the Geometric Brownian Motion (GBM) process, which has been used extensively in modeling many other real life phenomena. But few scholars, including those who used the GBM in highway configuration decisions, have offered any rigorous justification for the use of this model. This thesis attempts to offer a detailed analysis of various aspects of transportation systems in relation to decision-making. It reveals some general insights as well as a new concept that extends the notion of opportunity cost to situations where wrong decisions could be made. Claiming deficiency of the GBM model, it also introduces a new formulation that utilizes a large and flexible parametric family of jump models (i.e., Lévy processes). To validate this claim, data related to traffic demand and land prices were collected and analyzed to reveal that their distributions, heavy-tailed and asymmetric, do not match well with the GBM model. As a remedy, this research used the Merton, Kou, and negative inverse Gaussian Lévy processes as possible alternatives. Though the results show indifference in relation to final decisions among the models, mathematically, they improve the precision of uncertainty models and the decision-making process. This furthers the quest for optimality in highway projects and beyond.
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Books on the topic "Spot price model calibration"

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Power Prices: A Regime-Switching Spot/Forward Price Model with Kim Filter Estimation. Südwestdeutscher Verlag für Hochschulschriften AG & Company KG, 2009.

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Krause, Timothy A. Pricing of Futures Contracts. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656010.003.0015.

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This chapter examines the relation between futures prices relative to the spot price of the underlying asset. Basic futures pricing is characterized by the convergence of futures and spot prices during the delivery period just before contract expiration. However, “no arbitrage” arguments that dictate the fair value of futures contracts largely determine pricing relations before expiration. Although the cost of carry model in its various forms largely determines futures prices before expiration, the chapter presents alternative explanations. Related commodity futures complexes exhibit mean-reverting behavior, as seen in commodity spread markets and other interrelated commodities. Energy commodity futures prices can be somewhat accurately modeled as a generalized autoregressive conditional heteroskedastic (GARCH) process, although whether these models provide economically significant excess returns is uncertain.
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Back, Kerry E. Forwards, Futures, and More Option Pricing. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.003.0017.

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Forward measures are defined. Forward and futures contracts are explained. The spot‐forward parity formula is derived. A forward price is a martingale under the forward measure. A futures price is a martingale under a risk neutral probability. Forward prices equal futures prices when interest rates are nonrandom. The expectations hypothesis is explained. The option pricing formulas of Margabe (exchange options), Black (options on forwards), and Merton (random interest rates) are derived. Implied volatilities and local volatility models are explained. Heston’s stochastic volatility model is derived.
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Book chapters on the topic "Spot price model calibration"

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Helland, Eivind, Timur Aka, and Eric Winnington. "Stochastic Spot Price Multi-Period Model and Option Valuation for Electrical Markets." In Commodities, 2nd ed. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003265399-29.

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"- Stochastic Spot Price Multi-Period Model and Option Valuation for Electrical Markets." In Commodities. Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b19020-34.

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Lin, Yaojia, and Junyi Su. "The Tour Spot Attraction Evaluation and Analysis of National Forest Parks in Guangdong Province on Basis of the AHP Model." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde221109.

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This article establishes the evaluation model on Analytic Hierarchy Process (AHP) basis and applies this model to evaluate and analyse the tour spot attraction of national forest parks. The model has 4 criteria hierarchies: attraction of tour resource environment factors, attraction of administration and services factors, attraction of social environment factors and attraction of economic factors. These factors are assorted into the 20 benchmark indexes such as aesthetics of the scenery spot, territory and location, the condition of service facilities, propaganda, marketing and ticket price, etc. An analysis matrix is introduced to established the tour spot attraction evaluation index system of national forest parks in Guangdong Province so as to solve the problem of the insufficiency in scientific and professional evaluation for such tour sports. This article discusses the combination of the above-mentioned innovative method and the college major development, and its value in the course of rural community prosperity and the development of moral and political education in college lessons.
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Zhu, Heliang, Xi Zhang, and Patricia Ordenaz de Pablos. "The Role of Gold Market as Stabilizer of Service Industry." In Advances in Logistics, Operations, and Management Science. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9758-4.ch014.

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China's gold futures market has been in market for more than four years, is the risk transfer function fully realized? How the performance of hedging? Based on the data of futures prices and spot prices from January 9th of 2008 to December 31st of 2010, we use the following four statistical models such as traditional regression model (OLS), two-variable vector auto regression model (B-VAR), error correction hedging model (ECM), and error correction GARCH model (EC-GARCH) to perform stationarity and cointegration test On the basis of minimum risk hedge ratio estimated, the following conclusions are made based on the study: (1) As China's gold futures market has run for more than three years, hedge is effective through the gold futures market, which can significantly reduce the participants ‘ risk of price fluctuation; (2)In practice, hedging ratio should be rationally determined by different models according to different hedging length and different expectations. Based on these conclusions, this paper also made corresponding policy recommendations.
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Conference papers on the topic "Spot price model calibration"

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Liu, Duan, Zhicheng Cai, and Xiaoping Li. "Hidden Markov Model Based Spot Price Prediction for Cloud Computing." In 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC). IEEE, 2017. http://dx.doi.org/10.1109/ispa/iucc.2017.00152.

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Li, Zheng, William Tärneberg, Maria Kihl, and Anders Robertsson. "Using a Predator-Prey Model to Explain Variations of Cloud Spot Price." In 6th International Conference on Cloud Computing and Services Science. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005808600510058.

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Zheng Yanan, Gengyin Li, Ming Zhou, Shan Lin, and K. L. Lo. "An improved grey model for forecasting spot and long term electricity price." In 2010 International Conference on Power System Technology - (POWERCON 2010). IEEE, 2010. http://dx.doi.org/10.1109/powercon.2010.5666371.

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Peng, Chun-Cheng, Chia-Wei Yeh, Jun-Gong Wang, Shih-Hao Wang, and Chung-Wei Huang. "Prediction of LME lead spot price by neural network and NARX model." In 2020 IEEE 2nd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS). IEEE, 2020. http://dx.doi.org/10.1109/ecbios50299.2020.9203577.

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Amekraz, Zohra, and Moulay Youssef. "Prediction of Amazon spot price based on chaos theory using ANFIS model." In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE, 2016. http://dx.doi.org/10.1109/aiccsa.2016.7945632.

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Krecar, Nikola, Andrej F. Gubina, and Gregor Bozic. "A method for calibration of a fundamental model of electricity price." In 2011 European Energy Market (EEM). IEEE, 2011. http://dx.doi.org/10.1109/eem.2011.5952990.

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Ali, Aliyuda. "Ensemble Learning Model for Prediction of Natural Gas Spot Price Based on Least Squares Boosting Algorithm." In 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI). IEEE, 2020. http://dx.doi.org/10.1109/icdabi51230.2020.9325615.

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Ogwu, Jessica, Emmanuel Ikpesu, and Kingsley Ogbonna. "Natural Gas Spot Price Prediction Using a Machine Learning Datacentric Approach." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/211979-ms.

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Abstract The ability to accurately predict natural gas prices asides being beneficial to stakeholders of the natural gas market also have positive economic impacts on energy management and environmental sustainability. This paper explores the application of machine learning algorithms for the purpose of accurately predicting monthly natural gas spot prices. Henry Hub natural gas spot price data from January 2001 to November 2021 were utilized alongside four machine learning algorithms namely; Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest Regressor and Gradient Boosting Machine (GBM). The models were trained with 11 variables with 80% of the dataset utilized for training and 20% for testing purposes. A 10-fold cross validation technique was implemented for model validation purposes. The accuracy of each model was evaluated using the Root Mean Square error metric. After model evaluation, all four models generated distinct results, with the Artificial Neural Network model having the most accurate prediction of all four models.
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Silva, Lucas Barth, Roberto Zanetti Freire, and Osíris Canciglieri Junior. "Spot Energy Price Forecasting Using Wavelet Transform and Extreme Learning Machine." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-62.

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Given the social importance of energy, there is a concern to promote the sustainable development of the sector. Aiming at this evolution, from the 90s onwards, a wave of liberalization in the sector began to emerge in various parts of the world. These measures promoted an increase in the dynamism of commercial transactions and the transformation of electricity into a commodity. Consequently, futures, short-term, and spot markets were created. In this context, and due to the volatility of energy prices, the forecast of monetary values has become strategic for traders. This work aims to apply a computational intelligence model using Wavelet Transform on input values and the Extreme Machine Learning algorithm for training and prediction (W-ELM). The macro parameters were optimized using the Particle Swarm Optimization algorithm and for the selection of the input variables, a model based on Mutual Information (MI) was used. In the end, the methodology was compared with the traditional methods: Autoregressive Moving Averages (ARIMA) and General Autoregressive Conditional Heteroskedasticity (GARCH) models. Results showed that the W-ELM had better performance for forecasting 1 to 4 weeks of when compared to ARIMA. When the GARCH model results were considered, the proposed method provided worse performance only for 1 step ahead forecasting.
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Ozozen, Avni, Gulgun Kayakutlu, Marcel Ketterer, and Ozgur Kayalica. "A combined seasonal ARIMA and ANN model for improved results in electricity spot price forecasting: Case study in Turkey." In 2016 Portland International Conference on Management of Engineering and Technology (PICMET). IEEE, 2016. http://dx.doi.org/10.1109/picmet.2016.7806831.

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Reports on the topic "Spot price model calibration"

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Balat, Jorge, Juan Esteban Carranza, Juan David Martin, and Álvaro Riascos. El efecto de cambios en la regulación del mercado mayorista de electricidad en Colombia en un modelo estructural de subastas complejas. Banco de la República, 2022. http://dx.doi.org/10.32468/be.1211.

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We investigate the effects of a change in the regulation of the spot market for electricity in Colombia that took place in 2009. Specifically, the regulation switched from an auction mechanism with simple bids to one with complex bids to allow generators to separately bid on variable and quasi-fixed components. This greater flexibility was introduced to reduce production inefficiencies that arise from non-convexities in the cost structures of thermal generators. In this paper, we estimate and compute a structural model to quantify the effects of this change on allocation efficiency along with the effects on the wholesale price of electricity in Colombia. Consistently with previous reduced form evidence, we show that the production efficiency increased under the new dispatch mechanism, but prices increased.
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