Add to ORKGModels trained under assumptions in the complete market usually don't take effect in the incomplete market. This paper solves the hedging problem in incomplete market with three sources of incompleteness: risk factor, illiquidity, and discrete transaction dates. A new jump-diffusion model is proposed to describe stochastic asset prices. Three neutral networks, including RNN, LSTM, Mogrifier-LSTM are used to attain hedging strategies with MSE Loss and Huber Loss implemented and compared.As a result, Mogrifier-LSTM is the fastest model with the best results under MSE and Huber Loss
This paper proposes a new approach to pricing European options using deep learning techniques under ...
In this work we apply Recursive Neural Networks in finance, namely we use Recursive Neural Networks ...
We study neural networks as nonparametric estimation tools for the hedging of options. To this end, ...
Inspired by the recent paper Buehler et al. (2018), this thesis aims to investigate the optimal hedg...
Using techniques from deep learning, we show that neural networks can be trained successfully to rep...
Machine learning and deep learning have realized incredible success in areas such as computer vision...
Transition probability density functions (TPDFs) are fundamental to computational finance, including...
This work studies the deep learning-based numerical algorithms for optimal hedging problems in marke...
The computational speedup of computers has been one of the de ning characteristics of the 21st centu...
Deep learning has been widely used in hedge funds and asset management firms. The increasing complex...
There is a growing number of applications of machine learning and deep learning in quantitative and ...
Artificial intelligence, AI, has received increasing attention from the finance industry over recent...
In this paper, we propose a machine learning algorithm for time-inconsistent portfolio optimization....
We present a method for finding optimal hedging policies for arbitrary initial portfolios and market...
We study the capability of arbitrage-free neural-SDE market models to yield effective strategies for...
This paper proposes a new approach to pricing European options using deep learning techniques under ...
In this work we apply Recursive Neural Networks in finance, namely we use Recursive Neural Networks ...
We study neural networks as nonparametric estimation tools for the hedging of options. To this end, ...
Inspired by the recent paper Buehler et al. (2018), this thesis aims to investigate the optimal hedg...
Using techniques from deep learning, we show that neural networks can be trained successfully to rep...
Machine learning and deep learning have realized incredible success in areas such as computer vision...
Transition probability density functions (TPDFs) are fundamental to computational finance, including...
This work studies the deep learning-based numerical algorithms for optimal hedging problems in marke...
The computational speedup of computers has been one of the de ning characteristics of the 21st centu...
Deep learning has been widely used in hedge funds and asset management firms. The increasing complex...
There is a growing number of applications of machine learning and deep learning in quantitative and ...
Artificial intelligence, AI, has received increasing attention from the finance industry over recent...
In this paper, we propose a machine learning algorithm for time-inconsistent portfolio optimization....
We present a method for finding optimal hedging policies for arbitrary initial portfolios and market...
We study the capability of arbitrage-free neural-SDE market models to yield effective strategies for...
This paper proposes a new approach to pricing European options using deep learning techniques under ...
In this work we apply Recursive Neural Networks in finance, namely we use Recursive Neural Networks ...
We study neural networks as nonparametric estimation tools for the hedging of options. To this end, ...