Optimal dynamic basis trading

Angoshtari, Bahman; Leung, Tim

doi:10.1007/s10436-019-00348-x

Cited by 15 publications

(7 citation statements)

References 31 publications

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“…In particular, under this assumption, an argument similar to the proof of Proposition 1 of Angoshtari and Leung (2019) shows that the market model has a risk-neutral measure and, thus, is arbitrage free.…”

Section: Market Settingmentioning

confidence: 92%

“…(3.1) and (3.2) directly follow from (2.1)-(2.3) by applying Itô's lemma. The rest of the proof is similar to the proof of Lemma 1 in Angoshtari and Leung (2019) and is, thus, omitted.…”

Section: Market Settingmentioning

confidence: 99%

“…We briefly discuss nirvana solutions in Figure 6 below. For a more complete treatment of the subject, we refer the reader to Angoshtari and Leung (2019) for a detailed analysis of nirvana solutions in a special case of our market model where there is only one pair of futures and underlying assets. To keep the discussion less technical, however, we have decided to focus on the well-posed case γ > 1.…”

Section: Optimal Trading Of a Basket Of Futuresmentioning

confidence: 99%

“…Among the authors' recent related studies, Leung and Yan (2018) and Leung and Yan (2019) applied utility maximization approach to derive dynamic pairs trading strategies for futures under two-factor spot models. Most recently and relevantly, Angoshtari and Leung (2019) analyzed the problem of dynamically trading a futures contract and its underlying asset. The associated basis is modeled by a Brownian bridge, but the process is stopped early to capture the non-convergence of prices at the end of trading horizon.…”

Section: Introductionmentioning

confidence: 99%

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Optimal trading of a basket of futures contracts

Angoshtari

Leung

2020

Ann Finance

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We study the problem of dynamically trading multiple futures contracts with different underlying assets. To capture the joint dynamics of stochastic bases for all traded futures, we propose a new model involving a multi-dimensional scaled Brownian bridge that is stopped before price convergence. This leads to the analysis of the corresponding Hamilton-Jacobi-Bellman (HJB) equations, whose solutions are derived in semi-explicit form. The resulting optimal trading strategy is a long-short policy that accounts for whether the futures are in contango or backwardation. Our model also allows us to quantify and compare the values of trading in the futures markets when the underlying assets are traded or not.Numerical examples are provided to illustrate the optimal strategies and the effects of model parameters.

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Section: Market Settingmentioning

confidence: 92%

“…(3.1) and (3.2) directly follow from (2.1)-(2.3) by applying Itô's lemma. The rest of the proof is similar to the proof of Lemma 1 in Angoshtari and Leung (2019) and is, thus, omitted.…”

Section: Market Settingmentioning

confidence: 99%

Section: Optimal Trading Of a Basket Of Futuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal trading of a basket of futures contracts

Angoshtari

Leung

2020

Ann Finance

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“…A general regime-switching framework for dynamic futures trading can be found in [7]. As an alternative approach for capturing futures and spot price dynamics, the stochastic basis model [8], [9] directly models the difference between the futures and underlying asset prices, and solve for the optimal trading strategies through utility maximization.…”

Section: Introductionmentioning

confidence: 99%

Optimal Dynamic Futures Portfolios Under a Multiscale Central Tendency Ornstein-Uhlenbeck Model

Leung

Zhou

2020

SSRN Journal

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We study the problem of dynamically trading multiple futures whose underlying asset price follows a multiscale central tendency Ornstein-Uhlenbeck (MCTOU) model. Under this model, we derive the closed-form no-arbitrage prices for the futures contracts. Applying a utility maximization approach, we solve for the optimal trading strategies under different portfolio configurations by examining the associated system of Hamilton-Jacobi-Bellman (HJB) equations. The optimal strategies depend on not only the parameters of the underlying asset price process, but also the risk premia embedded in the futures prices. Numerical examples are provided to illustrate the investor's optimal positions and optimal wealth over time.

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