Related papers: A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm

Related papers

Assessing Uncertainty in Stock Returns: A Gaussian Mixture Distribution-Based Method [19.332951287688108]
We introduce a novel deep learning model to capture the complex, time-varying nature of asset return distributions in the Chinese stock market. Our approach effectively characterizes short-term fluctuations and non-traditional features of stock returns, such as skewness and heavy tails. It provides more accurate volatility forecasts and offers unique risk insights for different assets, thereby deepening the understanding of return uncertainty.
arXiv Detail & Related papers (2025-03-10T05:20:56Z)
Dynamic graph neural networks for enhanced volatility prediction in financial markets [0.0]
This paper proposes a novel approach using Graph Neural Networks (GNNs) to represent global financial markets as dynamic graphs. By utilizing correlation-based and volatility indices, the Temporal GAT constructs directed graphs that enhance the accuracy of volatility predictions.
arXiv Detail & Related papers (2024-10-22T09:52:15Z)
GARCH-Informed Neural Networks for Volatility Prediction in Financial Markets [0.0]
We present a new, hybrid Deep Learning model that captures and forecasting market volatility more accurately than either class of models are capable of on their own. When compared to other time series models, GINN showed superior out-of-sample prediction performance in terms of the Coefficient of Determination ($R2$), Mean Squared Error (MSE), and Mean Absolute Error (MAE)
arXiv Detail & Related papers (2024-09-30T23:53:54Z)
Probabilistic Forecasting of Real-Time Electricity Market Signals via Interpretable Generative AI [41.99446024585741]
We present WIAE-GPF, a Weak Innovation AutoEncoder-based Generative Probabilistic Forecasting architecture. A novel learning algorithm with structural convergence guarantees is proposed, ensuring that the generated forecast samples match the ground truth conditional probability distribution.
arXiv Detail & Related papers (2024-03-09T00:41:30Z)
Generative Probabilistic Time Series Forecasting and Applications in Grid Operations [47.19756484695248]
Generative probabilistic forecasting produces future time series samples according to the conditional probability distribution given past time series observations. We propose a weak innovation autoencoder architecture and a learning algorithm to extract independent and identically distributed innovation sequences. We show that the weak innovation sequence is Bayesian sufficient, which makes the proposed weak innovation autoencoder a canonical architecture for generative probabilistic forecasting.
arXiv Detail & Related papers (2024-02-21T15:23:21Z)
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction [54.21695754082441]
Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility. Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions. We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction. Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
arXiv Detail & Related papers (2023-08-18T16:21:15Z)
Learning to Predict Short-Term Volatility with Order Flow Image Representation [0.0]
The paper addresses the challenging problem of predicting the short-term realized volatility of the Bitcoin price using order flow information. We propose a method that transforms order flow data over a fixed time interval (snapshots) into images. Images are then used to train both a simple 3-layer Convolutional Neural Network (CNN) and more advanced ResNet-18 and ConvMixer.
arXiv Detail & Related papers (2023-04-04T12:32:25Z)
Volatility forecasting using Deep Learning and sentiment analysis [0.0]
This paper presents a composite model that merges a deep learning approach with sentiment analysis for predicting market volatility. We then describe a composite forecasting model, a Long-Short-Term-Memory Neural Network method, to use historical sentiment and the previous day's volatility to make forecasts.
arXiv Detail & Related papers (2022-10-22T14:54:33Z)
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions [53.37679435230207]
We propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data.
arXiv Detail & Related papers (2022-09-23T16:13:47Z)
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
arXiv Detail & Related papers (2022-03-07T18:59:54Z)
Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover [87.28082715343896]
We consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications. We design a new robust deep neural network architecture by applying algorithm unfolding techniques to a robust version of the underlying recovery problem. The proposed REST network is shown to outperform state-of-the-art model-based and data-driven algorithms in both compressive sensing and radar imaging problems.
arXiv Detail & Related papers (2021-10-20T06:15:45Z)
Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs [0.0]
This paper investigates the ability of hybrid models, mixing GARCH processes and neural networks, to forecast covariance matrices of asset returns. The new model proposed is very promising as it not only outperforms the equally weighted portfolio, but also by a significant margin its econometric counterpart.
arXiv Detail & Related papers (2021-08-25T23:41:43Z)
Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting [93.73198973454944]
Deep learning models have led to significant performance improvements in many problems coming from different domains, including prediction problems of financial time-series data. The Temporal Attention-Augmented Bilinear network was recently proposed as an efficient and high-performing model for Limit Order Book time-series forecasting. In this paper, we propose a low-rank tensor approximation of the model to further reduce the number of trainable parameters and increase its speed.
arXiv Detail & Related papers (2021-07-05T10:15:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.