A Simple Linear Programming Approach to Gain, Loss and Asset Pricing

Longarela, Iñaki R.

doi:10.2202/1534-598x.1064

Cited by 7 publications

(1 citation statement)

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“…The market trading data of the S&P 500 index options for both the call and put options listed in the Chicago Board Options Exchange are collected from 2 January 2008 to 30 December 2014. Most of previous literature concerning gain-loss ratio used simulated data [35,36]. Few researchers adopt the real trading data which are low frequency and single variety [37].…”

Section: Data and Estimated Risk-neutral Distributionmentioning

confidence: 99%

An Improvement of Gain-Loss Price Bounds on Options Based on Binomial Tree and Market-Implied Risk-Neutral Distribution

Jiang

Lei

Chung³

2018

Sustainability

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This paper investigates the approximated arbitrage bounds of option prices in an incomplete market setting and draws implications for option pricing and risk management. It gives consideration to periods of global financial crisis and European sovereign debt crisis. To this end, we employ the gain-loss ratio method combined with the market-implied risk-neutral distribution calculated by binomial tree to investigate the options price bounds. Our implied gain-loss bounds of option prices are preference-free and parametric-free to avoid the misspecification error of subjective choice on the benchmark model of gain-loss ratio, and consequently, greatly reduce model risk and market risk. The empirical results show that there are option prices breaking the gain-loss bounds, even after taking into account the market information. This means that a good risk management technique and good-deal investment opportunities exist if the implied binomial tree is used as a benchmark model in the gain-loss bounds.

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Section: Data and Estimated Risk-neutral Distributionmentioning

confidence: 99%

An Improvement of Gain-Loss Price Bounds on Options Based on Binomial Tree and Market-Implied Risk-Neutral Distribution

Jiang

Lei

Chung³

2018

Sustainability

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Gain–loss based convex risk limits in discrete-time trading

Pınar

2010

Comput Manag Sci

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Expected gain–loss pricing and hedging of contingent claims in incomplete markets by linear programming

Pınar

Altay-Salih

Camcι

2010

European Journal of Operational Research

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We analyze the problem of pricing and hedging contingent claims in the multi-period, discrete time, discrete state case using the concept of a ''k gain-loss ratio opportunity". Pricing results somewhat different from, but reminiscent of, the arbitrage pricing theorems of mathematical finance are obtained. Our analysis provides tighter price bounds on the contingent claim in an incomplete market, which may converge to a unique price for a specific value of a gain-loss preference parameter imposed by the market while the hedging policies may be different for different sides of the same trade. The results are obtained in the simpler framework of stochastic linear programming in a multi-period setting, and have the appealing feature of being very simple to derive and to articulate even for the non-specialist. They also extend to markets with transaction costs.

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A Simple Linear Programming Approach to Gain, Loss and Asset Pricing

Cited by 7 publications

References 0 publications

An Improvement of Gain-Loss Price Bounds on Options Based on Binomial Tree and Market-Implied Risk-Neutral Distribution

An Improvement of Gain-Loss Price Bounds on Options Based on Binomial Tree and Market-Implied Risk-Neutral Distribution

Gain–loss based convex risk limits in discrete-time trading

Expected gain–loss pricing and hedging of contingent claims in incomplete markets by linear programming

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