Dynamic Portfolio Selection in Arbitrage

Jurek, Jakub W.; Yang, Halla

doi:10.2139/ssrn.882536

Cited by 67 publications

(58 citation statements)

References 34 publications

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“…For simplicity, we assume that κ, θ, and σ are constant. Equation (7) was used by Jurek and Yang (2007) as a general formulation for an investor facing a mean-reverting arbitrage opportunity and by Dempster, Medova, and Tang (2008) for valuing and hedging spread options on two commodity prices that are assumed to be cointegrated in the long run. Since we define price spreads as the subtraction of longer maturity product price from shorter maturity product price, positive and negative spreads (S's) demonstrate backwardation and contango, respectively.…”

Section: [Insert Figure 1 About Here]mentioning

confidence: 99%

“…Gatev, Goetzmann, and Rouwenhorst (2006) conduct empirical tests of pairs trading applied to common stock and show that a pairs trading strategy is profitable even after taking into account transaction costs. Jurek and Yang (2007) compare the performance of their optimal mean-reversion strategy with the performance of Gatev, Goetzmann, and Rouwenhorst using simulated data and report that their strategy provides even better performance.…”

Section: Introductionmentioning

confidence: 99%

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A Profit Model for Spread Trading with an Application to Energy Futures

Kanamura¹,

Rachev²,

Fabozzi³

2009

JOT

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This paper proposes a profit model for spread trading by focusing on the stochastic movement of the price spread and its first hitting time probability density. The model is general in that it can be used for any financial instrument. The advantage of the model is that the profit from the trades can be easily calculated if the first hitting time probability density of the stochastic process is given. We then modify the profit model for a particular market, the energy futures market. It is shown that energy futures spreads are modeled by using a meanreverting process. Since the first hitting time probability density of a mean-reverting process is approximately known, the profit model for energy futures price spreads is given in a computable way by using the parameters of the process. Finally, we provide empirical evidence for spread trades of energy futures by employing historical prices of energy futures (WTI crude oil, heating oil, and natural gas futures) traded on the New York Mercantile Exchange. The results suggest that natural gas futures trading may be more profitable than WTI crude oil and heating oil due to its high volatility in addition to its long-term mean reversion, which offers supportive evidence of the model prediction.

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Section: [Insert Figure 1 About Here]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Profit Model for Spread Trading with an Application to Energy Futures

Kanamura¹,

Rachev²,

Fabozzi³

2009

JOT

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“…Maximizing in N t yields the following expression for the number of shares in the optimal portfolio: N t = ∂V /∂W ∂ 2 V /∂W 2 σ 2 (λ(P − P t ) − rP t ) − ∂ 2 V /∂P ∂W ∂ 2 V /∂W 2 (19) Jurek & Yang (2006) solve this equation explicitly for U(x) = log x and U(x) = x 1−γ /(1 − γ) and we recover in particular the classic expression:…”

Section: Utility Maximization In Frictionless Marketsmentioning

confidence: 99%

“…Jurek & Yang (2006) show that the optimal portfolio allocation can also be computed explicitly in the presence of fund flows, with:…”

Section: Leverage Constraintsmentioning

confidence: 99%

“…Liu & Longstaff (2004) also study the optimal investment problem in the presence of a "textbook" finite horizon arbitrage opportunity, modeled as a Brownian bridge, while Jurek & Yang (2006) study this same problem when the arbitrage horizon is indeterminate. Gatev, Goetzmann & Rouwenhorst (2006) studied the performance of pairs trading, using pairs of assets as classic examples of structurally mean-reverting portfolios.…”

Section: Introductionmentioning

confidence: 99%

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Identifying small mean-reverting portfolios

d’Aspremont

2011

Quantitative Finance

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Given multivariate time series, we study the problem of forming portfolios with maximum mean reversion while constraining the number of assets in these portfolios. We show that it can be formulated as a sparse canonical correlation analysis and study various algorithms to solve the corresponding sparse generalized eigenvalue problems. After discussing penalized parameter estimation procedures, we study the sparsity versus predictability tradeoff and the impact of predictability in various markets.

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References

2011

Financial Markets and Trading

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Dynamic Portfolio Selection in Arbitrage

Cited by 67 publications

References 34 publications

A Profit Model for Spread Trading with an Application to Energy Futures

A Profit Model for Spread Trading with an Application to Energy Futures

Identifying small mean-reverting portfolios

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