Fast Monte Carlo Simulation for Pricing Equity-Linked Securities

Jang, Hae‐Dong; Kim, Sangkwon; Han, Junhee; Lee, Seongjin; Ban, Jungyup; Han, Hyunsoo; Lee, Chaeyoung; Jeong, Darae; Kim, Junseok

doi:10.1007/s10614-019-09947-2

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…For example, Fabozzi et al [35] proved that the assumption of the OLS methodhomoscedasticity of errors-does not hold in the LSM model and the resulting OLS estimation is not unbiased, it is actually more prone to overfitting the continuation value curve. So, necessary improvement can be made in the way of replacement of OLS with different regression methods such as weighted least square regression [15] and the FAST model [36]. However, the theoretical methods to correct the estimation bias of OLS still lack support from the real market data [37,38].…”

Section: Motivation and Overviewmentioning

confidence: 99%

Pricing Chinese Convertible Bonds with Learning-Based Monte Carlo Simulation Model

Zhu,

Wen,

2024

Axioms

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In this paper, we explore a novel model for pricing Chinese convertible bonds that seamlessly integrates machine learning techniques with traditional models. The least squares Monte Carlo (LSM) method is effective in handling multiple state variables and complex path dependencies through simple regression analysis. In our approach, we incorporate machine learning techniques, specifically support vector regression (SVR) and random forest (RF). By employing Bayesian optimization to fine-tune the random forest, we achieve improved predictive performance. This integration is designed to enhance the precision and predictive capabilities of convertible bond pricing. Through the use of simulated data and real data from the Chinese convertible bond market, the results demonstrate the superiority of our proposed model over the classic LSM, confirming its effectiveness. The development of a pricing model incorporating machine learning techniques proves particularly effective in addressing the complex pricing system of Chinese convertible bonds. Our study contributes to the body of knowledge on convertible bond pricing and further deepens the application of machine learning in the field in an integrated and supportive manner.

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Section: Motivation and Overviewmentioning

confidence: 99%

Pricing Chinese Convertible Bonds with Learning-Based Monte Carlo Simulation Model

Zhu,

Wen,

2024

Axioms

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“…Kim et al (2011) added exit-probability, which means the probability of touching barrier, to the conventional FDM. In case of Monte Carlo simulation method, Jang et al (2019) presented numerical algorithms for one-, two-, and three-asset step-down ELS. As with many studies, these researches focus on improving accuracy and computational speed of algorithm.…”

Section: Introductionmentioning

confidence: 99%

Semi closed-form pricing autocallable ELS using Brownian Bridge

Lee

Hong

2021

CSAM

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This paper discusses the pricing of autocallable structured product with knock-in (KI) feature using the exit probability with the Brownian Bridge technique. The explicit pricing formula of autocallable ELS derived in the existing paper handles the part including the minimum of the Brownian motion using the inclusion-exclusion principle. This has the disadvantage that the pricing formula is complicate because of the probability with minimum value and the computational volume increases dramatically as the number of autocall chances increases. To solve this problem, we applied an efficient and robust simulation method called the Brownian Bridge technique, which provides the probability of touching the predetermined barrier when the initial and terminal values of the process following the Brownian motion in a certain interval are specified. We rewrite the existing pricing formula and provide a brief theoretical background and computational algorithm for the technique. We also provide several numerical examples computed in three different ways: explicit pricing formula, the Crude Monte Carlo simulation method and the Brownian Bridge technique.

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Evaluation of Non-survey Methods for the Construction of Regional Input–Output Matrices When There is Partial Historical Information

Mardones

Silva

2022

Comput Econ

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Fast Monte Carlo Simulation for Pricing Equity-Linked Securities

Cited by 4 publications

References 16 publications

Pricing Chinese Convertible Bonds with Learning-Based Monte Carlo Simulation Model

Pricing Chinese Convertible Bonds with Learning-Based Monte Carlo Simulation Model

Semi closed-form pricing autocallable ELS using Brownian Bridge

Evaluation of Non-survey Methods for the Construction of Regional Input–Output Matrices When There is Partial Historical Information

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