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Found 21 papers, 7 papers with code
Semi-parametric financial risk forecasting incorporating multiple realized measures
A semi-parametric joint Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting framework employing multiple realized measures is developed.
Data Scaling Effect of Deep Learning in Financial Time Series Forecasting
For many years, researchers have been exploring the use of deep learning in the forecasting of financial time series.
Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihood
We combine the Wasserstein Gaussianization transformation with robust BSL, and an efficient Variational Bayes procedure for posterior approximation, to develop a highly efficient and reliable approximate Bayesian inference method for likelihood-free problems.
Particle Mean Field Variational Bayes
The Mean Field Variational Bayes (MFVB) method is one of the most computationally efficient techniques for Bayesian inference.
Deep Learning Enhanced Realized GARCH
We propose a new approach to volatility modeling by combining deep learning (LSTM) and realized volatility measures.
An Introduction to Quantum Computing for Statisticians and Data Scientists
Quantum computers promise to surpass the most powerful classical supercomputers when it comes to solving many critically important practical problems, such as pharmaceutical and fertilizer design, supply chain and traffic optimization, or optimization for machine learning tasks.
Quantum Speedup of Natural Gradient for Variational Bayes
Variational Bayes (VB) is a critical method in machine learning and statistics, underpinning the recent success of Bayesian deep learning.
A practical tutorial on Variational Bayes
This tutorial gives a quick introduction to Variational Bayes (VB), also called Variational Inference or Variational Approximation, from a practical point of view.
Adaptive Hierarchical Hyper-gradient Descent
In this study, we investigate learning rate adaption at different levels based on the hyper-gradient descent framework and propose a method that adaptively learns the optimizer parameters by combining multiple levels of learning rates with hierarchical structures.
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting
Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the financial sector to measure the market risk and manage the extreme market movement.
COMBINED FLEXIBLE ACTIVATION FUNCTIONS FOR DEEP NEURAL NETWORKS
Based on this, we develop two novel flexible activation functions that can be implemented in LSTM cells and auto-encoder layers.
Variational Bayes on Manifolds
Nonetheless, the development of the existing VB algorithms is so far generally restricted to the case where the variational parameter space is Euclidean, which hinders the potential broad application of VB methods.
A Statistical Recurrent Stochastic Volatility Model for Stock Markets
The Stochastic Volatility (SV) model and its variants are widely used in the financial sector while recurrent neural network (RNN) models are successfully used in many large-scale industrial applications of Deep Learning.
Manifold Optimization Assisted Gaussian Variational Approximation
Gaussian variational approximation is a popular methodology to approximate posterior distributions in Bayesian inference especially in high dimensional and large data settings.
Subsampling MCMC - An introduction for the survey statistician
The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work.
Bayesian Deep Net GLM and GLMM
Efficient computational methods for high-dimensional Bayesian inference are developed using Gaussian variational approximation, with a parsimonious but flexible factor parametrization of the covariance matrix.
Computation
Subsampling Sequential Monte Carlo for Static Bayesian Models
SMC sequentially updates a cloud of particles through a sequence of distributions, beginning with a distribution that is easy to sample from such as the prior and ending with the posterior distribution.
Hamiltonian Monte Carlo with Energy Conserving Subsampling
The key insight in our article is that efficient subsampling HMC for the parameters is possible if both the dynamics and the acceptance probability are computed from the same data subsample in each complete HMC iteration.
The block-Poisson estimator for optimally tuned exact subsampling MCMC
A pseudo-marginal MCMC method is proposed that estimates the likelihood by data subsampling using a block-Poisson estimator.
Speeding Up MCMC by Efficient Data Subsampling
We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework where the likelihood function for $n$ observations is estimated from a random subset of $m$ observations.
Efficient variational inference for generalized linear mixed models with large datasets
We propose a divide and recombine strategy for the analysis of large datasets, which partitions a large dataset into smaller pieces and then combines the variational distributions that have been learnt in parallel on each separate piece using the hybrid Variational Bayes algorithm.
Methodology
