Results 71 to 80 of about 690,121 (120)

A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options

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, Lu, Weiguo, Wu, Xuan, Yuan, Gangnan, Zhuang, Jirong   +4 more
core  

Multi-asset and generalised Local Volatility. An efficient implementation [PDF]

arXiv
This article presents a generic hybrid numerical method to price a wide range of options on one or several assets, as well as assets with stochastic drift or volatility. In particular for equity and interest rate hybrid with local volatility.
arxiv  

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
core  

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
core  

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., Papapantoleon, Antonis, Smaragdakis, Costas   +2 more
core  

A time-stepping deep gradient flow method for option pricing in (rough) diffusion models

We develop a novel deep learning approach for pricing European options in diffusion models, that can efficiently handle high-dimensional problems resulting from Markovian approximations of rough volatility models.
Papapantoleon, Antonis, Rou, Jasper
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$\text{Alpha}^2$: Discovering Logical Formulaic Alphas using Deep Reinforcement Learning

Alphas are pivotal in providing signals for quantitative trading. The industry highly values the discovery of formulaic alphas for their interpretability and ease of analysis, compared with the expressive yet overfitting-prone black-box alphas.
Jiang, Shengyi, Liu, Tianyuan, Xu, Feng, Yin, Yan, Zhang, Xinyu, Zhang, Zongzhang   +5 more
core  

Text mining arXiv: a look through quantitative finance papers

This paper explores articles hosted on the arXiv preprint server with the aim to uncover valuable insights hidden in this vast collection of research. Employing text mining techniques and through the application of natural language processing methods, we
Bianchi, Michele Leonardo
core  

Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Asset Simulators

Derivative hedging and pricing are important and continuously studied topics in financial markets. Recently, deep hedging has been proposed as a promising approach that uses deep learning to approximate the optimal hedging strategy and can handle ...
Hirano, Masanori
core  

Gradient-enhanced sparse Hermite polynomial expansions for pricing and hedging high-dimensional American options

We propose an efficient and easy-to-implement gradient-enhanced least squares Monte Carlo method for computing price and Greeks (i.e., derivatives of the price function) of high-dimensional American options.
Li, Guanglian, Yang, Jiefei
core