Results 21 to 30 of about 157 (60)
Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning
, 2022 Understanding non-linear relationships among financial instruments has various applications in investment processes ranging from risk management, portfolio construction and trading strategies.
Mehta, Dhagash +3 more
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Derivatives Sensitivities Computation under Heston Model on GPU
, 2023 This report investigates the computation of option Greeks for European and Asian options under the Heston stochastic volatility model on GPU. We first implemented the exact simulation method proposed by Broadie and Kaya and used it as a baseline for ...
Arsaguet, Pierre-Antoine, Bilokon, Paul
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Two-stage Modeling for Prediction with Confidence
, 2022 The use of neural networks has been very successful in a wide variety of applications. However, it has recently been observed that it is difficult to generalize the performance of neural networks under the condition of distributional shift.
Chen, Dangxing
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Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study
, 2023 Option pricing, a fundamental problem in finance, often requires solving non-linear partial differential equations (PDEs). When dealing with multi-asset options, such as rainbow options, these PDEs become high-dimensional, leading to challenges posed by ...
Assabumrungrat, Rawin +2 more
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FinBen: A Holistic Financial Benchmark for Large Language Models
LLMs have transformed NLP and shown promise in various fields, yet their potential in finance is underexplored due to a lack of comprehensive evaluation benchmarks, the rapid development of LLMs, and the complexity of financial tasks.
Ananiadou, Sophia +33 more
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Advancing Algorithmic Trading: A Multi-Technique Enhancement of Deep Q-Network Models
, 2023 This study enhances a Deep Q-Network (DQN) trading model by incorporating advanced techniques like Prioritized Experience Replay, Regularized Q-Learning, Noisy Networks, Dueling, and Double DQN. Extensive tests on assets like BTC/USD and AAPL demonstrate
Hu, Gang
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In this work, we present a novel machine learning approach for pricing high-dimensional American options based on the modified Gaussian process regression (GPR). We incorporate deep kernel learning and sparse variational Gaussian processes to address the
Ding, Deng +4 more
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Application of Black-Litterman Bayesian in Statistical Arbitrage
\begin{abstract} In this paper, we integrated the statistical arbitrage strategy, pairs trading, into the Black-Litterman model and constructed efficient mean-variance portfolios. Typically, pairs trading underperforms under volatile or distressed market
Zhou, Qiqin
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Neural option pricing for rough Bergomi model
The rough Bergomi (rBergomi) model can accurately describe the historical and implied volatilities, and has gained much attention in the past few years. However, there are many hidden unknown parameters or even functions in the model.
Li, Guanglian, Teng, Changqing
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A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models
We develop a novel deep learning approach for pricing European basket options written on assets that follow jump-diffusion dynamics. The option pricing problem is formulated as a partial integro-differential equation, which is approximated via a new ...
Georgoulis, Emmanuil H. +2 more
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