Option Pricing in Markets With Informed Traders

Hu, Y.; Shirvani, Abootaleb; Stoyanov, Stoyan V.; Kim, Young S.; Fabozzi, Frank J.; Rachev, Svetlozar T.

doi:10.1142/s0219024920500375

Cited by 8 publications

(8 citation statements)

References 48 publications

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“…In the Black-Scholes-Merton formulation, the loss of µ in the risk-neutral world means that the instantaneous drift can be arbitrarily large while the option price remains unchanged, a condition known as the option pricing discontinuity puzzle. This puzzle has been addressed by Hu et al (2020a). The loss of µ is due to the absurd assumption that the trader can trade continuously in time.…”

Section: The Standard Approach−loss Of Information In Passing To the ...mentioning

confidence: 99%

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Option Pricing Using a Skew Random Walk Binary Tree

Hu,

Lindquist,

Rachev

et al. 2024

JRFM

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We develop a binary tree pricing model with underlying asset price dynamics following Itô–McKean skew Brownian motion. Our work was motivated by the Corns–Satchell, continuous-time, option pricing model. However, the Corns–Satchell market model is incomplete, while our discrete-time market model is defined in the natural world, extended to the risk-neutral world under the no-arbitrage condition where derivatives are priced under uniquely determined risk-neutral probabilities, and is complete. The skewness introduced in the natural world is preserved in the risk-neutral world. Furthermore, we show that the model preserves skewness under the continuous-time limit. We provide empirical applications of our model to the valuation of European put and call options on exchange-traded funds tracking the S&P Global 1200 index.

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Section: The Standard Approach−loss Of Information In Passing To the ...mentioning

confidence: 99%

“…There is no need to pass to continuous-time price dynamics when using binomial option pricing. (For various generalizations of the binomial pricing market model in Step 1, see Hu et al 2020aHu et al , 2020bHu et al , 2022. See also Jarrow et al 2009 8 ).…”

Section: The Standard Approach−loss Of Information In Passing To the ...mentioning

confidence: 99%

Option Pricing Using a Skew Random Walk Binary Tree

Hu,

Lindquist,

Rachev

et al. 2024

JRFM

Self Cite

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“…The long position in the forward contract could be taken by any trader who believes that S (n,h,g) (k+1)∆t = S (n,h,g,u) (k+1)∆t is more likely to happen. 48 When h(x) = g(x) = 0, this forward contract was introduced in Hu et al (2020). 49 The case of a misinformed trader can be considered in a similar manner.…”

Section: Forward Contract Strategymentioning

confidence: 99%

“…In Section 6, we extend the results in Section 5 to markets with informed (and misinformed) traders. Here, we follow the general framework of a financial market with informed, misinformed, and noisy traders introduced and studied in Hu et al (2020). Using the call option where the underlying stock is Microsoft (MSFT) 14 , we estimate the implied information rate of MSFT call option traders.…”

Section: Introductionmentioning

confidence: 99%

Option Pricing Incorporating Factor Dynamics in Complete Markets

Shirvani

Lindquist

et al. 2020

JRFM

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Using the Donsker–Prokhorov invariance principle, we extend the Kim–Stoyanov–Rachev–Fabozzi option pricing model to allow for variably-spaced trading instances, an important consideration for short-sellers of options. Applying the Cherny–Shiryaev–Yor invariance principles, we formulate a new binomial path-dependent pricing model for discrete- and continuous-time complete markets where the stock price dynamics depends on the log-return dynamics of a market influencing factor. In the discrete case, we extend the results of this new approach to a financial market with informed traders employing a statistical arbitrage strategy involving trading of forward contracts. Our findings are illustrated with numerical examples employing US financial market data. Our work provides further support for the conclusion that any option pricing model must preserve valuable information on the instantaneous mean log-return, the probability of the stock’s upturn movement (per trading interval), and other market microstructure features.

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“…3 To illustrate this claim, suppose that a trader would like to determine if potential mispricing in the option market exists due to a market bubble that the trader suspects will burst. 4 The trader's option pricing model, when calibrated to option market data, should recover, uniquely, the spot price dynamics (Ross, 2015) of the underlying asset (Kim et al (2016), Hu et al (2020a)). If that recovered spot price process does not conform with market data on the asset spot price, this could be a trading signal that there is potential option mispricing.…”

Section: Introductionmentioning

confidence: 99%

Market Complete Option Valuation using a Jarrow-Rudd Pricing Tree with Skewness and Kurtosis

Hu¹,

Shirvani²,

Lindquist³

et al. 2021

Preprint

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Applying the Cherny-Shiryaev-Yor invariance principle, we introduce a generalized Jarrow-Rudd (GJR) option pricing model with uncertainty driven by a skew random walk. The GJR pricing tree exhibits skewness and kurtosis in both the natural and risk-neutral world. We construct implied surfaces for the parameters determining the GJR tree. Motivated by Merton's pricing tree incorporating transaction costs, we extend the GJR pricing model to include a hedging cost. We demonstrate ways to fit the GJR pricing model to a market driver that influences the price dynamics of the underlying asset. We supplement our findings with numerical examples.

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Option Pricing in Markets With Informed Traders

Cited by 8 publications

References 48 publications

Option Pricing Using a Skew Random Walk Binary Tree

Option Pricing Using a Skew Random Walk Binary Tree

Option Pricing Incorporating Factor Dynamics in Complete Markets

Market Complete Option Valuation using a Jarrow-Rudd Pricing Tree with Skewness and Kurtosis

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