Commodity Price Models

Roncoroni, Andréa

doi:10.1002/9780470061602.eqf17013

Cited by 5 publications

(2 citation statements)

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“…In the case we described here, funding costs are represented by the short rate of interest, collateral benefits, and side costs through the convenience yield. Specific examples are provided in the following table (Roncoroni, 2010) It could be interesting to exploit parity (3.4) and pricing formula (6.1) in these other circumstances. Following the discussion made on forward price and existing parities, we may guess that a virtue of our approach is in allowing general random volatility.…”

Section: Discussionmentioning

confidence: 99%

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A note on the spot-forward parity under stochastic cost of carry

Guiotto

2022

Energy Economics

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Section: Discussionmentioning

confidence: 99%

“…This strategy consists of borrowing cash, buying the commodity, storing and delivering it at maturity. If 𝐶 (𝑡, 𝑇) is predictable at time 𝑡, this replication argument entails the celebrated spot-forward parity (Roncoroni, 2010): 𝐹 (𝑡, 𝑇) = 𝑆(𝑡) × 𝐶 (𝑡, 𝑇).…”

Section: Introductionmentioning

confidence: 99%

A note on the spot-forward parity under stochastic cost of carry

Guiotto

2022

Energy Economics

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A Survey on Big Data Market: Pricing, Trading and Protection

et al. 2018

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TIDES: A Trust-Aware IoT Data Economic System With Blockchain-Enabled Multi-Access Edge Computing

et al. 2020

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To make good use of valuable Internet of Things (IoT) data assets, this paper proposes a trust-aware IoT data economic system (TIDES) with complete IoT data pricing, trading and protection functions. To ensure reliable and automatic data trading, the entire trading process is automatically performed by smart contracts on a hierarchical blockchain. Moreover, we develop several sophisticated methods to ensure the efficiency and service quality of TIDES. First, a complete evaluation model that takes the data trading profile and reputation into consideration is proposed for both suppliers and demanders to assess the trustworthiness of their trading partners. Second, a client-centric data value evaluation model and a game-theory-based pricing model are used to promote win-win transactions in which the demanders obtain higher quality data at an acceptable price and the suppliers receive higher profits. Third, a dispute arbitration model is invoked to detect suspicious trading and refund these payments automatically. TIDES further utilizes a multi-access edge computing (MEC) architecture to alleviate the huge burdens of IoT devices from blockchain operations, reduce the trading latency, and help mobile devices to trade IoT data. The simulation results have shown the advantages of TIDES in terms of trading time, storage overhead, data trading profit, quality data trading, pricing efficiency, and reliability on data asset management and trading. INDEX TERMS Blockchain, data economy, data trading, edge computing, Internet of Things (IoT).

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Commodity Price Models

Abstract: We propose a classification of existing arbitrage‐free stochastic models of commodity price dynamics. Focus is put on model primitives, structural elements and driving sources of noise.

Cited by 5 publications

References 25 publications

A note on the spot-forward parity under stochastic cost of carry

A note on the spot-forward parity under stochastic cost of carry

A Survey on Big Data Market: Pricing, Trading and Protection

TIDES: A Trust-Aware IoT Data Economic System With Blockchain-Enabled Multi-Access Edge Computing

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