Estimating a Lévy multifactor market model for electricity futures markets by using independent component analysis

Poto, Giuseppe Di; Fanone, Enzo

doi:10.21314/jem.2012.081

Cited by 5 publications

(5 citation statements)

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“…The sample period spans from 6/1/2004 to 12/31/2012. The sample size is 2178 observations and Figueroa ( 2005) jump-diffusion model and (iv) Di Poto and Fanone (2012) Lévy multifactor market model based on Independent Component Analysis. Details are in Online Appendix A, B, C and D respectively.…”

Section: Comparison Against Alternative Modelsmentioning

confidence: 99%

“…We prefer to concentrate on actual market prices and include the effect of the changing time to maturity in the SFP component better in sample fit. Third, SFP model outperforms Cartea and Figueroa (2005) jumpdiffusion model and Di Poto and Fanone (2012) Lévy multifactor model in an out-ofsample pricing exercise in the period 2013-2015 and in fitting the term structure of volatilities by market segments. Fourth, we show that models who do not account for the impact of non-normality are not able to replicate market prices, and, in particular, are unable of taking into account tail risk.…”

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confidence: 93%

“…Appendix D: Di Poto and Fanone (2012) ModelIn order to provide comparison against a more recent market model competitor, we develop an adapted version of the model proposed byDi Poto and Fanone (2012), DF from now on. Specifically, with deviate from DF in several aspects.…”

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confidence: 99%

“…Exceptions areAndresen, Koekebakker and Westgaard (2010) who present a discrete random-field model based on the multivariate NIG distribution, andDi Poto, and Fanone, (2012) who apply a Lévy multifactor market model by using Independent Component Analysis.…”

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confidence: 99%

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Modelling Electricity Swaps with Stochastic Forward Premium Models

Blanco¹,

Peña²,

Rodríguez³

2018

The Energy Journal

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We present a new model for pricing electricity swaps. Two general factors affect contracts but unique risk elements affect each contract. General factors are average swap prices and deterministic trend-seasonal components, and unique elements are forward premiums. Innovations follow MNIG distributions. We estimate the model with data from the European Energy Exchange. The model outperforms four competitors, both in in-sample valuation and in out-of-sample forecasting, and in fitting the term structure of volatilities by market segments. Competitor models are (i) diffusion spot prices, (ii) jump-diffusion spot prices with time dependent volatility, (iii) HJM-based and (iv) Lévy multifactor model with NIG distributions. Value-at-Risk measures based on normality strongly underestimate tail risk but our model gives estimates that are more exact.

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Section: Comparison Against Alternative Modelsmentioning

confidence: 99%

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confidence: 93%

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confidence: 99%

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confidence: 99%

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Modelling Electricity Swaps with Stochastic Forward Premium Models

Blanco¹,

Peña²,

Rodríguez³

2018

The Energy Journal

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“…Exceptions areAndresen, Koekebakker, and Westgaard (2010a) who present a discrete random-field model based on the multivariate NIG distribution, and DiPoto and Fanone (2012) who apply a Lévy multifactor market model by using Independent Component Analysis.…”

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confidence: 99%

Three essays on financial markets

Cheng¹

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[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This dissertation consists of three essays. The first essay extends Kyle (1985)'s informed trading model to allow for multiple informed traders, noise in informed traders' private signals and the probability of information events. The first essay's main theoretical findings are: (a) Other than the intuitive effect of incurring loss, noise in private signals may preserve informed traders' profit by dampening competition. When the noise level is relatively low, the competition dampening effect dominates. What's more important, there is an optimal noise level at which informed traders' profit is maximized. (b) If the optimal noise level can be implemented, then strong form informational efficiency is not achievable in days with information events, and the size of potential bias depends on both the probability and magnitude of information events. The theoretical section also includes simulation results which support theoretical finding (b). The empirical part of this essay extends Odders-White and Ready (2008) and finds that: (1) Estimated noise in private signal is higher for small firm stocks, which is intuitive since information leaks are less likely for small firms and small firms are more secretive. (2) Estimated noise level is larger for stock/year combinations with larger estimated number of informed traders and larger estimated magnitude of information events. Result (2) confirms theoretical finding (a), which states that optimal noise level is increasing in the number of informed traders and magnitude of information events. The second essay explains how an airline can hedge more effectively employing dynamic copula models. Since there is no corresponding futures contract available, airlines may have to cross hedge jet fuel with either crude oil or other refined products. Because the dependence between jet fuel and its hedging instrument is time-varying, airlines' cross hedging problem needs a dynamic solution. This essay estimates timevarying optimal cross hedge ratios using dynamic copula based GARCH models. Estimation results show that copula models outperform static hedging models by achieving a smaller hedged portfolio variance in both in-sample (by about 5%) and out-of-sample tests (by about 9%). This essay also finds that heating oil futures is a more effective cross hedging instrument for jet fuel than WTI crude oil futures. The third essay seeks to investigate the impact of corporate hedging. First of all, this essay examines whether oil/jet fuel price increases impact Southwest stock/Dow Jones Transportation index returns and return volatilities significantly. Assuming Southwest airlines' hedging effort is effective, this essay tries to find supportive evidence showing that Southwest stock return and return volatility are less sensitive to positive oil/jet fuel price shocks than the DJT counterparts. While intuition suggests oil/jet fuel price increases, especially the large ones, may reduce transportation firms' profitability substantially, the link between positive oil shocks and stock returns as well as the link between positive oil shocks and stock return volatilities are found to be weak. This essay shows that positive oil shocks' impacts on Southwest/DJT returns and volatilities are very limited, though there is a tail dependence exception: extreme positive oil price increases do make large negative stock returns more likely. Although intuition suggests Southwest stock return and return volatility may be less sensitive to oil/jet fuel price shocks due its hedging effort, the analysis of this essay does not find any profound evidence supporting this conjecture.

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Modeling and Estimating Electricity Futures: A Non-Gaussian Market Model Approach

Fanone¹

2014

Energy Pricing Models

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Estimating a Lévy multifactor market model for electricity futures markets by using independent component analysis

Cited by 5 publications

References 0 publications

Modelling Electricity Swaps with Stochastic Forward Premium Models

Modelling Electricity Swaps with Stochastic Forward Premium Models

Three essays on financial markets

Modeling and Estimating Electricity Futures: A Non-Gaussian Market Model Approach

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