The numerical simulation of Quanto option prices using Bayesian statistical methods

Lin, Lisha; Li, Yaqiong; Gao, Rui; Wu, Jianhong

doi:10.1016/j.physa.2020.125629

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…However, for complicated models, the Bayesian statistical method, which can fully consider prior information and parameter uncertainty, performs better in parameter inference and prediction. Therefore, some researchers suggest applying the Bayesian method to estimate model parameters; see, e.g., [21][22][23][24][25]. Karolyi [21] considered the impact of the randomness of volatility on stock returns and proposed an approach to evaluating European call options under the Bayesian framework.…”

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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“…Therefore, some researches proposed to apply Bayesian approach to estimate option price models, see, e.g., [15,16,17,18,19]. Karolyi [15] considered the impact of randomness of volatility on stock returns and proposed an approach to evaluate European call options under Bayesian framework.…”

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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