Modelling the general dependence between commodity forward curves

Zolotko, Mikhail; Okhrin, Ostap

doi:10.1016/j.eneco.2014.02.019

Cited by 14 publications

(6 citation statements)

References 49 publications

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“…Secondly, at present the copula-GARCH methodology is widely used in the analysis of financial time series (see, e.g. Patton, 2006;Serban et al, 2007;Lee and Long, 2009;Doman, 2011;Wu et al, 2012;Aloui et al, 2013;Li and Yang, 2013;Zolotko and Okhrin, 2014; and for a review Patton, 2012).…”

Section: Methodsmentioning

confidence: 99%

In Search of Hedges and Safe Havens in Global Financial Markets

Wanat

Śmiech

Papież

2016

Statistics in Transition New Series

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The aim of the paper is to search for hedges and safe havens within three instrument classes: assets (represented by the S&P500 index), gold and oil prices, and dollar exchange rates. Weekly series of returns of all the instruments from the period January 1995 -June 2015 are analysed. The study is based on conditional correlations between the instruments in different market regimes obtained with the use of copula-DCC GARCH models. It is assumed that different market regimes will be identified by statistical clustering techniques; however, only conditional variances (without conditional covariances) will be taken into account. The reason for this assumption is connected with the fact that variances can be understood as market risk, and, as such, are a good indicator of market conditions. A considerable advantage of such an approach is the lack of need to determine the number of market regimes, as it is established by clustering quality measures. What is more, the methodology used in the paper makes it possible to treat the relations between instruments symmetrically. The results obtained in the study reveal that only dollar exchange rates can be treated as a (strong) hedge and a (strong) safe haven for other instruments, while gold and oil are a hedge for assets.

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Section: Methodsmentioning

confidence: 99%

In Search of Hedges and Safe Havens in Global Financial Markets

Wanat

Śmiech

Papież

2016

Statistics in Transition New Series

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“…Alternative methodologies to capture downside risks for crude oil prices are also used (e.g., [21][22][23][24]). For multivariate analysis cases, recent papers propose copula approaches to model dependence between different crude oil markets or between crude oil and other energy markets (e.g., [25][26][27][28][29][30]).…”

Section: Literature Reviewmentioning

confidence: 99%

Quantifying Risk in Traditional Energy and Sustainable Investments

2019

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These days we are witnessing a deep change in the characteristics of the type of energy that our economies are supplied with. A clear trend is that sustainable and green energies are decisively replacing traditional fossil fuel-based sources of energy. For various reasons, this fundamental change implies an increasing risk in investments on portfolios heavily based on traditional energy industries. What is less known, is that these industries have returns that show a very low correlation with sustainable fossil fuel-free stock portfolios making them an appealing tool for portfolio managers to design properly diversified investments. In this study we examine this and related phenomena proposing statistical methods to implement the expected shortfall (ES), the challenging risk measure recently adopted by the financial regulator. We obtain evidence that a newly proposed backtesting procedure for the ES based on multinomial tests is an adequate and simple method to validate these risk measures when applied to a highly volatile stock index. Backtesting results of the ES show that flexible heavy-tailed distribution α–stable performs well for modelling the loss distribution. These results are even improved when the variances of fossil fuel price returns are included as external regressors in the GARCH model variance equation. In this case, the ES computed from the four considered loss distributions perform properly.

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“…Models for commodity price evolution typically fall into two categories: models of the spot price and convenience yield Q (for example [1] and [2]) and models of the forward prices themselves (for example [3] and [4]). Since commodity markets usually trade in forward price terms, through futures markets and overthe-counter forward transactions, the latter category of model -called "forward curve" models -is often more intuitive for derivatives traders.…”

Section: The Model Dynamicsmentioning

confidence: 99%

“…Values of α ranged from 0 to 3, which gave ATMF implied volatilities ranging from 57. 4 Figure 3 shows the approximation error in basis points (one basis point is 10 −4 of the forward) for these extreme parameters as a function of α, the volatility of volatility, running from 0 to 3. Figure 4 shows the standard error on the simulated forward.…”

Section: Validating the Drift Approximationmentioning

confidence: 99%

A Two Factor Forward Curve Model with Stochastic Volatility for Commodity Prices

Higgins¹

2017

Preprint

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We describe a model for evolving commodity forward prices that incorporates three important dynamics which appear in many commodity markets: mean reversion in spot prices and the resulting Samuelson effect on volatility term structure, decorrelation of moves in different points on the forward curve, and implied volatility skew and smile.This model is a "forward curve model" -it describes the stochastic evolution of forward prices -rather than a "spot model" that models the evolution of the spot commodity price. Two Brownian motions drive moves across the forward curve, with a third Heston-like stochastic volatility process scaling instantaneous volatilities of all forward prices.In addition to an efficient numerical scheme for calculating European vanilla and early-exercise option prices, we describe an algorithm for Monte Carlo-based pricing of more generic derivative payoffs which involves an efficient approximation for the risk neutral drift that avoids having to simulate drifts for every forward settlement date required for pricing.

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Modelling the general dependence between commodity forward curves

Cited by 14 publications

References 49 publications

In Search of Hedges and Safe Havens in Global Financial Markets

In Search of Hedges and Safe Havens in Global Financial Markets

Quantifying Risk in Traditional Energy and Sustainable Investments

A Two Factor Forward Curve Model with Stochastic Volatility for Commodity Prices

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