Pricing path-dependent exotic options with flow-based generative networks

Kim, Hyun-Gyoon; Kwon, Se-Jin; Kim, Jeong Hoon; Huh, Jeonggyu

doi:10.1016/j.asoc.2022.109049

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…See e.g., Avram, Palmowski & Pistorius [7], Klüppelberg et al [25], Mordecki [40], Xing et al [46], Cai et al [10], Zhang et al [49], Chi [13], Chi and Lin [14], Yin et al [47], Hu et al [20]. In the context of mathematical finance, the first passage time is crucial for the pricing of many path-dependent options, American-type and Russian-type options, such as Kou [26], Kou and Wang ([27], [28]), Asmussen et al [6], Alili and Kyprianou [3], Cai et al [10], Cai and Kou [11], Jeannin and Pistorius [21], Kim et al [24] and together with certain credit risk models, see, for example, Hilberink and Rogers [19], Le Courtois and Quittard-Pinon [34], Dong et al [16], and Leippold and Vasiljevic [35]. Cai and Sun [12] investigate pricing problems of both infinite-and finitematurity stock loans under a hyper-exponential jump diffusion model.…”

Section: Clearly = −mentioning

confidence: 99%

Discounted densities of overshoot and undershoot for Lévy processes with applications in finance

Gao,

Yin

2024

Prob. Eng. Inf. Sci.

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This paper considers the first passage times to constant boundaries and the two-sided exit problem for Lévy process with a characteristic exponent in which at least one of the two jumps having rational Laplace transforms. The joint distribution of the first passage times and undershoot/overshoot are obtained. The processes recover many models that have appeared in the literature such as the compound Poisson risk models, the perturbed compound Poisson risk models, and their dual ones. As applications, we obtain the solutions for popular path-dependent options such as lookback and barrier options in terms of Laplace transforms.

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Section: Clearly = −mentioning

confidence: 99%

Discounted densities of overshoot and undershoot for Lévy processes with applications in finance

Gao,

Yin

2024

Prob. Eng. Inf. Sci.

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Deep learning of optimal exercise boundaries for American options

Kim,

Huh

2024

International Journal of Computer Mathematics

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Enhancing sentiment analysis of online comments: a novel approach integrating topic modeling and deep learning

Zhu,

Zhou,

Chen

et al. 2024

PeerJ Computer Science

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Traditional statistical learning-based sentiment analysis methods often struggle to effectively handle text relevance and temporality. To overcome these limitations, this paper proposes a novel approach integrating Latent Dirichlet Allocation (LDA), Shuffle-enhanced Real-Valued Non-Volume Preserving (RealNVP), a double-layer bidirectional improved Long Short-Term Memory (DBiLSTM) network, and a multi-head self-attention mechanism for sentiment analysis. LDA is employed to extract latent topics within comment texts, revealing text relevance and providing fine-grained user feedback. Shuffle enhancement is applied to RealNVP to effectively model the distribution of text topic features, enhancing performance while avoiding excessive complexity in model structure and computational overhead. The double-layer bidirectional improved LSTM, through the coupling of forget and input gates, captures the dynamic temporal changes in sentiment with greater flexibility. The multi-head self-attention mechanism enhances the model’s ability to select and focus on key information, thereby more accurately reflecting user experiences. Experimental results on both Chinese and English online comment datasets demonstrate that the proposed integrated model achieves improved topic coherence compared to traditional LDA models, effectively mitigating overfitting. Furthermore, the model outperforms single models and other baselines in sentiment classification tasks, as evidenced by superior accuracy and F1 scores. These results underscore the model’s effectiveness for both Chinese and English sentiment analysis in the context of online comments.

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Pricing path-dependent exotic options with flow-based generative networks

Cited by 3 publications

References 21 publications

Discounted densities of overshoot and undershoot for Lévy processes with applications in finance

Discounted densities of overshoot and undershoot for Lévy processes with applications in finance

Deep learning of optimal exercise boundaries for American options

Enhancing sentiment analysis of online comments: a novel approach integrating topic modeling and deep learning

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