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Found 13 papers, 3 papers with code
Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study
Through comparative experiments, we assessed the empirical performance of these solvers in high-dimensional contexts.
Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling
In this framework, a hedger and a generator, which respectively model the underlying asset process and the underlying asset process, are trained in an adversarial manner.
Efficient Learning of Nested Deep Hedging using Multiple Options
That is, we develop a fully-deep approach of deep hedging in which the hedging instruments are also priced by deep neural networks that are aware of frictions.
Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty
Machine learning-based stock prediction methods including the TC method have been concentrating on point prediction.
Unified Perspective on Probability Divergence via Maximum Likelihood Density Ratio Estimation: Bridging KL-Divergence and Integral Probability Metrics
This paper provides a unified perspective for the Kullback-Leibler (KL)-divergence and the integral probability metrics (IPMs) from the perspective of maximum likelihood density-ratio estimation (DRE).
Contrastive Representation Learning with Trainable Augmentation Channel
In contrastive representation learning, data representation is trained so that it can classify the image instances even when the images are altered by augmentations.
Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction
The task we consider is portfolio construction in a speculative market, a fundamental problem in modern finance.
No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging
Deep hedging (Buehler et al. 2019) is a versatile framework to compute the optimal hedging strategy of derivatives in incomplete markets.
Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction
We show the effectiveness of our method by conducting experiments on real market data.
Deep Portfolio Optimization via Distributional Prediction of Residual Factors
In this study, we propose a novel method of constructing a portfolio based on predicting the distribution of a financial quantity called residual factors, which is known to be generally useful for hedging the risk exposure to common market factors.
The equivalence between Stein variational gradient descent and black-box variational inference
We formalize an equivalence between two popular methods for Bayesian inference: Stein variational gradient descent (SVGD) and black-box variational inference (BBVI).
Smoothness and Stability in GANs
Generative adversarial networks, or GANs, commonly display unstable behavior during training.
Differential Privacy without Sensitivity
The exponential mechanism is a general method to construct a randomized estimator that satisfies $(\varepsilon, 0)$-differential privacy.
