Hedging size risk: Theory and application to the US gas market

Roncoroni, Andréa; Brik, Rachid Id

doi:10.1016/j.eneco.2016.10.020

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…Both effects will be considered and discussed in Sections 3 and 5. Roncoroni and Brik (2017) discuss risk hedging strategies in natural gas markets.…”

Section: Market Data and Stylized Factsmentioning

confidence: 99%

Distributional modeling and forecasting of natural gas prices

Berrisch

Ziel

2022

Journal of Forecasting

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We examine the problem of modeling and forecasting European day‐ahead and month‐ahead natural gas prices. For this, we propose two distinct probabilistic models that can be utilized in risk and portfolio management. We use daily pricing data ranging from 2011 to 2020. Extensive descriptive data analysis shows that both time series feature heavy tails and conditional heteroscedasticity and show asymmetric behavior in their differences. We propose state‐space time series models under skewed, heavy‐tailed distributions to capture all stylized facts of the data. They include the impact of autocorrelation, seasonality, risk premia, temperature, storage levels, the price of European Emission Allowances, and related fuel prices of oil, coal, and electricity. We provide rigorous model diagnostics and interpret all model components in detail. Additionally, we conduct a probabilistic forecasting study with significance tests and compare the predictive performance against literature benchmarks. The proposed day‐ahead (month‐ahead) model leads to a 13% (9%) reduction in out‐of‐sample continuous ranked probability score (CRPS) compared with the best performing benchmark model, mainly due to adequate modeling of the volatility and heavy tails.

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“…Both effects will be considered and discussed in Sections 3 and 5. Roncoroni and Brik (2017) discuss risk hedging strategies in natural gas markets.…”

Section: Market Data and Stylized Factsmentioning

confidence: 99%

Distributional modeling and forecasting of natural gas prices

Berrisch

Ziel

2022

Journal of Forecasting

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“…Here, sizes θ * X and θ * I are the optimal forward gas and index units defining the optimal combined forward hedge Roncoroni and Id Brik (2017).…”

Section: Ec24 Hedging Policies and Utility Profiles In The Newsvendor...mentioning

confidence: 99%

Combined Custom Hedging: Optimal Design, Noninsurable Exposure, and Operational Risk Management

Guiotto

Roncoroni

2022

Operations Research

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Optimal Design of Combined Contingent Claims: Theory and Applications. In “Combined Custom Hedging: Optimal Design, Noninsurable Exposure, and Operational Risk Management”, Paolo Guiotto and Andrea Roncoroni develop a normative framework for the optimal design, value assessment, and operations management integration of financial derivatives. Most business and operating revenues entail a mix of financially insurable and noninsurable risk. A risk-averse firm may face them by positioning in a pair of financial derivatives with optimal bespoke payoff functions; one claim is written on the insurable term, and the other claim is written on any observable index exhibiting correlation to the noninsurable term. On a theoretical ground, the authors 1) state the problem in a general setup and prove existence and uniqueness of the optimal pair of combined claims, 2) show that the optimal payoff functions satisfy a Fredholm integral equation, and 3) assess the incremental benefit the firm obtains by switching from the optimal single-claim custom hedge to the optimal combined custom hedge they propose. On an experimental ground, they show that 1) the optimal combined custom hedge would be empirically relevant for a highly risk-averse firm facing a market shock shown during the first period of the COVID-19 pandemic in 2020, 2) integration with the optimal procurement in a generalized newsvendor model leads to a significant improvement in both risk and return, and: 3) this gain can be traded off for a substantial enhancement in operational flexibility.

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“…In the deregulated electricity market, retailers can acquire electricity through various options, e.g., the day-ahead (DA) wholesale market, bilateral contracts and self-owned distributed generation (DG). Due to the high volatility of the DA market prices [1], retailers need to adopt risk management methods to mitigate the risk arising from market price uncertainty. In addition, with the rapid development of smart grid technologies around the world, customers' demand response (DR) has received massive attentions and applications.…”

Section: Introductionmentioning

confidence: 99%

Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand

Zhang

Wang

et al. 2020

Energies

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This paper investigates the problem of how to deploy customers’ shiftable load (SL) for electricity retailers’ risk management under uncertainty of the day-ahead (DA) wholesale market price. The robust profit (RP) and the conditional robust profit (CRP) are introduced for a risk-averse retailer’s risk-reward trade-off analysis in its decision-making of electricity procurement from various options. A CRP-based bi-level optimization model is proposed for the risk-averse retailer to determine its electricity procurement strategy taking into consideration customers’ shiftable load. In the upper problem, the retailer decides its electricity procurement from various options and the SL incentive prices to maximize its CRP under a given confidence level, and in the lower problem, the customers shift their load according to the SL incentive prices to minimize their comprehensive costs including the discomfort cost caused by rescheduling electricity consumption. Finally, a case study is used to verify the effectiveness of this model. It is shown that the retailer can achieve larger profit and less risk by utilizing customers’ SL and the retailer’s risk-aversion level has an important impact on its electricity procurement and SL incentive strategies.

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Hedging size risk: Theory and application to the US gas market

Cited by 13 publications

References 27 publications

Distributional modeling and forecasting of natural gas prices

Distributional modeling and forecasting of natural gas prices

Combined Custom Hedging: Optimal Design, Noninsurable Exposure, and Operational Risk Management

Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand

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