Static mitigation of volumetric risk

Brik, Rachid Id; Roncoroni, Andréa

doi:10.21314/jem.2016.146

Cited by 8 publications

(7 citation statements)

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“…The holder of such a contract will receive a payment (K −S(T ))×(P V (T )− L) as a compensation for the price and production loss if P V (T ) > L (high supply from PV) and S(T ) < K (low prices). This option contract is an example of an energy quanto option, extensively discussed in Caporin et al [14], Benth et al [9] and Brik and Roncoroni [12]. To understand the distribution of the payoff from such options, their price and the optimal design of strikes, a joint model for the power price and PV production is required.…”

Section: Quanto Optionmentioning

confidence: 99%

“…Options on power price exist in many power markets, while derivatives on weather-variables are traded to some extent at the Chicago Mercantile Exchange (see Benth andŠaltytė Benth [8] for an overview). Benth et al [9] present an approach to pricing of such options including a pricing measure, while Brik and Roncoroni [12] study the optimal design of such options.…”

Section: Quanto Optionmentioning

confidence: 99%

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Stochastic modeling of photovoltaic power generation and electricity prices

Benth¹,

Ibrahim²

2017

JEM

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In recent years, renewable energy has gained importance in producing power in many markets. The aim of this article is to model photovoltaic (PV) production for three transmission operators in Germany. PV power can only be generated during sun hours and the cloud cover will determine its overall production. Therefore, we propose a model that takes into account the sun intensity as a seasonal function. We model the deseasonalized data by an autoregressive process to capture the stochastic dynamics in the data. We present two applications based on our suggested model. First, we build a relationship between electricity spot prices and PV production where the higher the volume of PV production, the lower the power prices. As a further application, we discuss virtual power plant derivatives and energy quanto options.

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Section: Quanto Optionmentioning

confidence: 99%

Section: Quanto Optionmentioning

confidence: 99%

Stochastic modeling of photovoltaic power generation and electricity prices

Benth¹,

Ibrahim²

2017

JEM

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“…For example, Oum et al [21] and Oum and Oren [22] constructed optimal nonlinear payoff functions on electricity prices for volume (demand) risk hedging in electric utilities. Brick and Roncoroni [23] also derived nonlinear payoffs for temperature using a similar approach. These results share analogous ideas with ours that optimal (and effective) payoff functions are searched based on smooth nonlinear shapes and then are replicated using standardized instruments for hedging volume risks.…”

Section: Introductionmentioning

confidence: 99%

Improving the Efficiency of Hedge Trading Using Higher-Order Standardized Weather Derivatives for Wind Power

Matsumoto

Yamada

2023

Energies

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Since the future output of wind power generation is uncertain due to weather conditions, there is an increasing need to manage the risks associated with wind power businesses, which have been increasingly implemented in recent years. This study introduces multiple weather derivatives of wind speed and temperature and examines their effectiveness in reducing (hedging) the fluctuation risk of future cash flows attributed to wind power generation. Given the diversification of hedgers and hedging needs, we propose new standardized derivatives with higher-order monomial payoff functions, such as “wind speed cubic derivatives” and “wind speed and temperature cross-derivatives,” to minimize the cash flow variance and develop a market-trading scheme to practically use these derivatives in wind power businesses. In particular, while demonstrating the importance of standardizing weather derivatives regarding market liquidity and efficiency, we propose a strategy to narrow down the required number (or volume) of traded instruments and improve trading efficiency by utilizing the least absolute shrinkage and selection operator (LASSO) regression. Empirical analysis reveals that higher-order, multivariate standardized derivatives can not only enhance the out-of-sample hedge effect but also help reduce trading volume. The results suggest that diversification of hedging instruments increases transaction flexibility and helps wind power generators find more efficient portfolios, which can be generalized to risk management practices in other businesses.

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“…It is shown that revenue uncertainty causes the firm to export less. The quantity risk hedging problem has been tackled in a couple of recent papers (see Brik & Roncoroni, ; Roncoroni & Brik, ). The present paper has two distinct differences with those recent works.…”

Section: Introductionmentioning

confidence: 99%

“…The present paper has two distinct differences with those recent works. First, Brik and Roncoroni (2016) and Roncoroni and Brik (2017) assumes a lognormal model with mean-reverting dynamics for prices, while the present paper assumes a much more general process. From a practical perspective, this is a substantial improvement.…”

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confidence: 99%

Barndorff‐Nielsen and Shephard model for hedging energy with quantity risk

et al. 2019

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In this paper, the Barndorff‐Nielsen and Shephard (BN‐S) model is implemented to find an optimal hedging strategy in the presence of quantity risk for oil produced in the Bakken, a new region of oil extraction that is benefiting from fracking technology. Hedging and price risk management become much more involved with the inclusion of quantity risk. Explorers and drillers have uncertainty on the quantity of oil that would be extracted, and governments have uncertainty of the quantity of oil that will be extracted, sold, and available for imposing tax regimes. One of the main assumptions typically made in a portfolio model of hedging is that the quantity of inventory or demand is known. This is inappropriate in many hedging situations. Quantity risk compounds the difficulty of determining the optimal size of the position under both price and production risk. In this paper, we provide a novel way of handling the quantity risk in connection with the BN‐S model. The model is analyzed as a quadratic hedging problem and related analytical results are developed. The results indicate that oil can be optimally hedged with a combination of variance swaps and options. For various quantity risks, the model is implemented to analyze hedging decisions numerically for managing price risk in the Bakken oil commodities.

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Static mitigation of volumetric risk

Cited by 8 publications

References 0 publications

Stochastic modeling of photovoltaic power generation and electricity prices

Stochastic modeling of photovoltaic power generation and electricity prices

Improving the Efficiency of Hedge Trading Using Higher-Order Standardized Weather Derivatives for Wind Power

Barndorff‐Nielsen and Shephard model for hedging energy with quantity risk

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