Jumps and stochastic volatility in crude oil prices and advances in average option pricing

Kyriakou, Ioannis; Pouliasis, Panos K.; Papapostolou, Nikos C.

doi:10.1080/14697688.2016.1211798

Cited by 24 publications

(8 citation statements)

References 65 publications

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“…Fusai, Marena and Roncoroni [34] advocated Fourier-based pricing methods for discretely-monitored Asian options in corn and gas markets using a square-root (Cox-Ingersoll-Ross) stochastic process for the spot dynamics. Discrete and continuous-time Asian options in the context of crude oil were priced by an iterative numer-ical method by Kyriakou, Pouliasis and Papapostolou [35] based on the Heston stochastic volatility model and its Bates' extension where the log-price dynamics have jumps (see also [29]). The Heston along with the SABR stochastic volatility models were proposed for oil futures price dynamics by Shiraya and Takahashi [36], who derived approximation formulas by asymptotic expansions for Asian options.…”

Section: Options In Energy and Commoditiesmentioning

confidence: 99%

Pricing of Commodity and Energy Derivatives for Polynomial Processes

Benth

2021

Mathematics

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Operating in energy and commodity markets require a management of risk using derivative products such as forward and futures, as well as options on these. Many of the popular stochastic models for spot dynamics and weather variables developed from empirical studies in commodity and energy markets belong to the class of polynomial jump diffusion processes. We derive a tailor-made framework for efficient polynomial approximation of the main derivatives encountered in commodity and energy markets, encompassing a wide range of arithmetic and geometric models. Our analysis accounts for seasonality effects, delivery periods of forwards and exotic temperature forwards where the underlying “spot” is a nonlinear function of the temperature. We also include in our derivations risk management products such as spread, Asian and quanto options.

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Section: Options In Energy and Commoditiesmentioning

confidence: 99%

Pricing of Commodity and Energy Derivatives for Polynomial Processes

Benth

2021

Mathematics

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“…See also Oyuna and Yaobin (2021), Kyriakou et al (2016), andVo (2011) and references therein for a comparason between a classical Heston SV model and a GARCH-type volatility model, SV models with jumps and asian option, and SV models to extreact information interwined in stock and oil futures markets for risk prediction, respectively. However, all three papers build and report the SV models quite different from this paper's multifactor model.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities

Solibakke

2021

JRFM

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This paper builds and implements multifactor stochastic volatility models for the international oil/energy markets (Brent oil and WTI oil) for the period 2011–2021. The main objective is to make step ahead volatility predictions for the front month contracts followed by an implication discussion for the market (differences) and observed data dependence important for market participants, implying predictability. The paper estimates multifactor stochastic volatility models for both contracts giving access to a long-simulated realization of the state vector with associated contract movements. The realization establishes a functional form of the conditional distributions, which are evaluated on observed data giving the conditional mean function for the volatility factors at the data points (nonlinear Kalman filter). For both Brent and WTI oil contracts, the first factor is a slow-moving persistent factor while the second factor is a fast-moving immediate mean reverting factor. The negative correlation between the mean and volatility suggests higher volatilities from negative price movements. The results indicate that holding volatility as an asset of its own is insurance against market crashes as well as being an excellent diversification instrument. Furthermore, the volatility data dependence is strong, indicating predictability. Hence, using the Kalman filter from a realization of an optimal multifactor SV model visualizes the latent step ahead volatility paths, and the data dependence gives access to accurate static forecasts. The results extend market transparency and make it easier to implement risk management including derivative trading (including swaps).

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“…These popular instruments are less susceptible to market manipulation and offering simpler hedging strategies than regular interest rate options (see, e.g., Chacko & Das, 2002). Specially, Asian options are popular and commonly used for the price risk management (see, e.g., Kyriakou, Pouliasis, & Papapostolou, 2016). Specially, Asian options are popular and commonly used for the price risk management (see, e.g., Kyriakou, Pouliasis, & Papapostolou, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Another example is commodity options, and a variety of commodity options with average underlying prices are traded OTC (see, e.g., Alexander & Venkatramanan, 2008). Specially, Asian options are popular and commonly used for the price risk management (see, e.g., Kyriakou, Pouliasis, & Papapostolou, 2016). With the significant trading volume increases of Asian options, and the harrowing experience of the subprime mortgage crisis in 2007, the counterparty risk should by no means be ignored when pricing Asian options traded in the OTC market.…”

Section: Introductionmentioning

confidence: 99%

Analytical valuation of Asian options with counterparty risk under stochastic volatility models

Wang

2019

Journal of Futures Markets

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In this paper, we consider Asian options with counterparty risk under stochastic volatility models. We propose a simple way to construct stochastic volatility models through the market factor channel. In the proposed framework, we obtain an explicit pricing formula of Asian options with counterparty risk and illustrate the effects of systematic risk on Asian option prices. Specially, the U-shaped and inverted U-shaped curves appear when we keep the total risk of the underlying asset and the issuer's assets unchanged, respectively.

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Jumps and stochastic volatility in crude oil prices and advances in average option pricing

Cited by 24 publications

References 65 publications

Pricing of Commodity and Energy Derivatives for Polynomial Processes

Pricing of Commodity and Energy Derivatives for Polynomial Processes

Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities

Analytical valuation of Asian options with counterparty risk under stochastic volatility models

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