Spot Price Modelling | ScienceGate

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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Commodity Exchange-Traded Funds

10.1093/oso/9780190656010.003.0013 ◽

2018 ◽

Author(s):

Christopher Milliken

Keyword(s):

Portfolio Diversification ◽

Futures Contracts ◽

Spot Price ◽

Exchange Traded Funds ◽

Investment Strategies ◽

Regulatory Structure ◽

Commodity Exchange ◽

Asset Class ◽

Exchange Traded Fund

Commodity exchange-traded funds (ETCs), which debuted in 2004, enable investors to access an asset class previously difficult or expensive to access. Although a small segment of the overall exchange-traded fund (ETF) universe, ETCs have grown in popularity with both speculators and investors looking for long-term portfolio diversification. Examples of the types of commodities that are now accessible through ETCs include gold, oil, and agricultural. The literature on ETCs is limited, but academic and industry work has centered on using futures contracts to replicate the performance of the underlying commodities spot price as well as the effect additional capital has had on the integrity of the futures market. This chapter covers this topic by reviewing the growth, investment strategies, and regulatory structure of ETCs as well as the underlying effects these funds have had on the underlying markets with which they engage.

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An Application of Marshall-Edgeworth Spot Price Index in Wind-Thermal Generation Swap Option Pricing

8th Renewable Power Generation Conference (RPG 2019) ◽

10.1049/cp.2019.0462 ◽

2019 ◽

Author(s):

Zhixiong Hu ◽

Ming Zhou ◽

Zhiming Jiang ◽

Yan Zhan ◽

Xiaojuan Liu

Keyword(s):

Option Pricing ◽

Price Index ◽

Spot Price ◽

Thermal Generation

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A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm

Energies ◽

10.3390/en14020388 ◽

2021 ◽

Vol 14 (2) ◽

pp. 388

Author(s):

Riccardo De Blasis ◽

Giovanni Batista Masala ◽

Filippo Petroni

Keyword(s):

Markov Model ◽

Electricity Market ◽

Wind Farm ◽

Central Italy ◽

High Order ◽

Distribution Model ◽

Spot Price ◽

Model Parameters ◽

Price Series ◽

Cross Correlations

The energy produced by a wind farm in a given location and its associated income depends both on the wind characteristics in that location—i.e., speed and direction—and the dynamics of the electricity spot price. Because of the evidence of cross-correlations between wind speed, direction and price series and their lagged series, we aim to assess the income of a hypothetical wind farm located in central Italy when all interactions are considered. To model these cross and auto-correlations efficiently, we apply a high-order multivariate Markov model which includes dependencies from each time series and from a certain level of past values. Besides this, we used the Raftery Mixture Transition Distribution model (MTD) to reduce the number of parameters to get a more parsimonious model. Using data from the MERRA-2 project and from the electricity market in Italy, we estimate the model parameters and validate them through a Monte Carlo simulation. The results show that the simulated income faithfully reproduces the empirical income and that the multivariate model also closely reproduces the cross-correlations between the variables. Therefore, the model can be used to predict the income generated by a wind farm.

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