Statistical deep learning for spatial and spatiotemporal data
CK Wikle, A Zammit-Mangion - Annual Review of Statistics and …, 2023 - annualreviews.org
Deep neural network models have become ubiquitous in recent years and have been
applied to nearly all areas of science, engineering, and industry. These models are …
applied to nearly all areas of science, engineering, and industry. These models are …
Statistical deep learning for spatial and spatio-temporal data
CK Wikle, A Zammit-Mangion - arXiv preprint arXiv:2206.02218, 2022 - arxiv.org
Deep neural network models have become ubiquitous in recent years, and have been
applied to nearly all areas of science, engineering, and industry. These models are …
applied to nearly all areas of science, engineering, and industry. These models are …
Deep integro-difference equation models for spatio-temporal forecasting
A Zammit-Mangion, CK Wikle - Spatial Statistics, 2020 - Elsevier
Integro-difference equation (IDE) models describe the conditional dependence between the
spatial process at a future time point and the process at the present time point through an …
spatial process at a future time point and the process at the present time point through an …
Fast covariance parameter estimation of spatial Gaussian process models using neural networks
F Gerber, D Nychka - Stat, 2021 - Wiley Online Library
Gaussian processes (GPs) are a popular model for spatially referenced data and allow
descriptive statements, predictions at new locations, and simulation of new fields. Often, a …
descriptive statements, predictions at new locations, and simulation of new fields. Often, a …
Volatility forecasting with hybrid neural networks methods for risk parity investment strategies
We present a hybrid method for computing volatility forecasts that can be used to implement
a risk-controlled strategy for a multi-asset portfolio consisting of both US and international …
a risk-controlled strategy for a multi-asset portfolio consisting of both US and international …
[HTML][HTML] Bayesian neural networks for macroeconomic analysis
Macroeconomic data is characterized by a limited number of observations (small T), many
time series (big K) but also by featuring temporal dependence. Neural networks, by contrast …
time series (big K) but also by featuring temporal dependence. Neural networks, by contrast …
[HTML][HTML] Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models
H Nguyen, A Virbickaitė - Energy Economics, 2023 - Elsevier
Stock and oil relationship is usually time-varying and depends on the current economic
conditions. In this study, we propose a new Dynamic Stochastic Mixed data sampling (DSM) …
conditions. In this study, we propose a new Dynamic Stochastic Mixed data sampling (DSM) …
Risk measurement in Bitcoin market by fusing LSTM with the joint-regression-combined forecasting model
X Lu, C Liu, KK Lai, H Cui - Kybernetes, 2021 - emerald.com
Risk measurement in Bitcoin market by fusing LSTM with the joint-regression-combined
forecasting model | Emerald Insight Books and journals Case studies Expert Briefings Open …
forecasting model | Emerald Insight Books and journals Case studies Expert Briefings Open …
Ask" who", not" what": Bitcoin volatility forecasting with Twitter data
Understanding the variations in trading price (volatility), and its response to exogenous
information, is a well-researched topic in finance. In this study, we focus on finding stable …
information, is a well-researched topic in finance. In this study, we focus on finding stable …
From Deep Filtering to Deep Econometrics
R Stok, P Bilokon - arXiv preprint arXiv:2311.06256, 2023 - arxiv.org
Calculating true volatility is an essential task for option pricing and risk management.
However, it is made difficult by market microstructure noise. Particle filtering has been …
However, it is made difficult by market microstructure noise. Particle filtering has been …