Bayesian statistical inference for European options with stock liquidity

Gao, Rui; Li, Yaqiong; Lin, Lisha

doi:10.1016/j.physa.2018.12.008

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…Rombouts and Stentoft [ 22 ] introduced an approach to conducting posterior inference on European option price and demonstrated that the Bayesian method performs better than traditional methods when sample data is small. Gao et al [ 26 ] introduced an approach to conducting posterior inference on the European call option pricing model in an imperfectly liquid market. Recently, Hu et al [ 27 ] proposed a new semi-parametric nonlinear volatility model to capture stock returns and they recommended a Bayesian sampling algorithm for estimating the model parameters.…”

Section: Introductionmentioning

confidence: 99%

Pricing quanto options with market liquidity risk

Gao,

Bai

2023

PLoS ONE

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This paper investigates the pricing problem of quanto options with market liquidity risk using the Bayesian method. The increasing volatility of global financial markets has made liquidity risk a significant factor that should be taken into consideration while evaluating option prices. To address this issue, we first derive the pricing formula for quanto options with liquidity risk. Next, we construct a likelihood function to conduct posterior inference on model parameters. We then propose a numerical algorithm to conduct statistical inferences on the option prices based on the posterior distribution. This proposed method considers the impact of parameter uncertainty on option prices. Finally, we conduct a comparison between the Bayesian method and traditional estimation methods to examine their validity. Empirical results show that our proposed method is feasible for pricing and predicting quanto options with liquidity risk, particularly for parameter estimations with a small sample size.

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Section: Introductionmentioning

confidence: 99%

Pricing quanto options with market liquidity risk

Gao,

Bai

2023

PLoS ONE

Self Cite

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show abstract

“…Rombouts and Stentoft [16] introduced an approach to conduct posterior inference on European option price and demonstrated that Bayesian method performs better than traditional method when sample data is small. Gao et al [20] introduced an approach to conduct posterior inference on the European call option pricing model in an imperfectly liquid market. Recently, Hu et al [21] proposed a new semi-parametric nonlinear volatility model to capture stock returns and they recommended a Bayesian sampling algorithm for estimating the model parameters.…”

Section: Introductionmentioning

confidence: 99%

Posterior inference on Quanto option pricing model with liquidity-adjustment

Gao

Bai

2022

Preprint

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This paper proposes an alternative approach to estimate parameters and evaluate the quanto option with stock liquidity under Bayesian framework. First, we derive an explicit expression of the quanto option price with liquidity-adjustment in an alternative way. Then, an applicable likelihood is proposed to conduct posterior inference on model parameters based on the joint distribution of underlying stock price dynamics and the exchange rate process, which provides a new perspective to estimate the correlation coefficient. Moreover, the statistical inference on the quanto option price is conducted by the posterior distribution and numerical algorithm. The proposed method considers the impact of parameter uncertainty on option prices, particularly the correlation coefficient randomness. Finally, the numerical analysis is performed for examining the effectiveness of the proposed method in terms of estimating parameters and pricing quanto options. Empirical results indicate that the proposed method is feasible in pricing and predicting quanto option with liquidity-adjustment.

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“…The numerical experiments provided evidence that there might be potential advantages for using Bayesian inference when less return data is available. Gao et al [18] performed Bayesian statistical inference for the pricing of European call option with stock liquidity in an incomplete market. Their numerical experiments with applications to the S&P500 index option indicated the potential advantages of Bayesian methods compared with traditional statistical methods in parameter estimations as well as option pricing.…”

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confidence: 99%

The Numerical Simulation of Quanto Option Prices Using Bayesian Statistical Methods

Lin

Gao

et al. 2019

Preprint

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In the paper, the pricing of Quanto options is studied, where the underlying foreign asset and the exchange rate are correlated with each other. Firstly, we adopt Bayesian methods to estimate unknown parameters entering the pricing formula of Quanto options, including the volatility of stock, the volatility of exchange rate and the correlation. Secondly, we compute and predict prices of different four types of Quanto options based on Bayesian posterior prediction techniques and Monte Carlo methods. Finally, we provide numerical simulations to demonstrate the advantage of Bayesian method used in this paper comparing with some other existing methods. This paper is a new application of the Bayesian methods in the pricing of multi-asset options.

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Bayesian statistical inference for European options with stock liquidity

Cited by 8 publications

References 28 publications

Pricing quanto options with market liquidity risk

Pricing quanto options with market liquidity risk

Posterior inference on Quanto option pricing model with liquidity-adjustment

The Numerical Simulation of Quanto Option Prices Using Bayesian Statistical Methods

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