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Found 12 papers, 0 papers with code
Saddle-Point Approach to Large-Time Volatility Smile
We extend upon the saddle-point equation presented in [1] to derive large-time model-implied volatility smiles, providing its theoretical foundation and studying its applications in classical models.
Post trade allocation: how much are bunched orders costing your performance?
However, as many have found over the years, there is no simple solution for post trade allocation between accounts that results in a uniform distribution of returns.
Simulating financial time series using attention
It is also challenging because of the complex statistical properties of the real financial data.
Solving barrier options under stochastic volatility using deep learning
We develop an unsupervised deep learning method to solve the barrier options under the Bergomi model.
Predicting Status of Pre and Post M&A Deals Using Machine Learning and Deep Learning Techniques
We also integrate sentiment scores into our methodology using different model architectures, but our preliminary results show that the performance is not changing much compared to the simple FFNN framework.
Supervised Neural Networks for Illiquid Alternative Asset Cash Flow Forecasting
In this paper, we develop a benchmark model and present two novel approaches (direct vs. indirect) to predict the cash flows of private equity funds.
Two-Stage Sector Rotation Methodology Using Machine Learning and Deep Learning Techniques
In this paper, we propose a two-stage methodology that consists of predicting ETF prices for each sector using market indicators and ranking sectors based on their predicted rate of returns.
Deep reinforcement learning on a multi-asset environment for trading
Our trading strategy is trained and tested both on real and simulated price series and we compare the results with an index benchmark.
Explainable AI in Credit Risk Management
Artificial Intelligence (AI) has created the single biggest technology revolution the world has ever seen.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
+1
Regularized Generative Adversarial Network
We propose a framework for generating samples from a probability distribution that differs from the probability distribution of the training set.
The VIX index under scrutiny of machine learning techniques and neural networks
Using subset selection approaches on top of the original CBOE methodology, as well as building machine learning and neural network models including Random Forests, Support Vector Machines, feed-forward neural networks, and long short-term memory (LSTM) models, we will show that a small number of options is sufficient to replicate the VIX index.
Supervised Deep Neural Networks (DNNs) for Pricing/Calibration of Vanilla/Exotic Options Under Various Different Processes
We apply supervised deep neural networks (DNNs) for pricing and calibration of both vanilla and exotic options under both diffusion and pure jump processes with and without stochastic volatility.
