Traditional statistical learning-based sentiment analysis methods often struggle to effectively
handle text relevance and temporality. To overcome these limitations, this paper proposes a …

Efficiently determining a price and optimal exercise boundary for an American option is a
critical subject in the financial sector. This study introduces a novel application of long short …

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 …

In this study, we discuss a machine learning technique to price exotic options with two
underlying assets based on a non-Gaussian Levy process model. We introduce a new …

This paper presents a unified semi-stochastic kernel regression method for pricing options
under general stochastic volatility model. The method combines semi-stochastic sampling …

The precise and efficient estimation of option prices and Greeks is crucial for effectively
managing and hedging risks in financial derivatives. Traditional approaches often struggle …

Traditional financial engineering based on mathematics has contributed significantly to the
development of the field of derivatives. However, despite the efforts of numerous …