Do industries predict stock market volatility? Evidence from machine learning models
In a novel take on the gradual information diffusion hypothesis of Hong et al.(2007), we
examine the predictive role of industries over aggregate stock market volatility. Using high …
examine the predictive role of industries over aggregate stock market volatility. Using high …
[HTML][HTML] The impact of oil and global markets on Saudi stock market predictability: A machine learning approach
This study investigates the predictability power of oil prices and six international stock
markets namely, China, France, UK, Germany, Japan, and the USA, on the Saudi stock …
markets namely, China, France, UK, Germany, Japan, and the USA, on the Saudi stock …
The impact of foreign stock market indices on predictions volatility of the WIG20 index rates of return using neural networks
E Fraszka-Sobczyk, A Zakrzewska - Computational Economics, 2024 - Springer
The paper investigates the issue of volatility of stock index returns on the Warsaw Stock
Exchange (WIG20 index returns volatility). The purpose of this review is to compare how …
Exchange (WIG20 index returns volatility). The purpose of this review is to compare how …
Enhancing cryptocurrency market volatility forecasting with daily dynamic tuning strategy
This study proposes a novel parameter tuning strategy, daily dynamic tuning, and applies it
to forecast volatility in the cryptocurrency market. Comparative analysis with HAR-RV and …
to forecast volatility in the cryptocurrency market. Comparative analysis with HAR-RV and …
Forecasting the volatility of crude oil basis: Univariate models versus multivariate models
Q Geng, Y Wang - Energy, 2024 - Elsevier
The studies on investigating and forecasting crude oil volatility primarily focus on oil price
volatility. This research aims to forecast the volatility of the oil futures basis using …
volatility. This research aims to forecast the volatility of the oil futures basis using …
[HTML][HTML] Implied volatility is (almost) past-dependent: Linear vs non-linear models
We explore and demonstrate a clear pattern of past-dependence in predicting implied
volatility, which extends up to twenty days and is present in both linear and nonlinear …
volatility, which extends up to twenty days and is present in both linear and nonlinear …
Return volatility of Asian stock exchanges; a GARCH DCC analysis with reference of Bitcoin and global crude oil price movement
Purpose This study aims to investigate the conditional volatility of the Asian stock market
concerning Bitcoin and global crude oil price movement. Design/methodology/approach …
concerning Bitcoin and global crude oil price movement. Design/methodology/approach …
[PDF][PDF] Forecasting Tail Risk via Neural Networks with Asymptotic Expansions
Y Sakurai, Z Chen - 2024 - imf.org
We propose a new machine-learning-based approach for forecasting Value-at-Risk (VaR)
named CoFiE-NN where a neural network (NN) is combined with Cornish-Fisher …
named CoFiE-NN where a neural network (NN) is combined with Cornish-Fisher …
An Inconvenient Truth about Forecast Combinations
It is well-known that the weighted averages of two competing forecasts may reduce mean
squared prediction errors (MSPE) and may also introduce certain inefficiencies. In this …
squared prediction errors (MSPE) and may also introduce certain inefficiencies. In this …
Heuristic decision-making and behavioral heterogeneity in the Chinese stock market
This paper applies a heuristic decision-making approach to a heterogeneous agent model
(HAM) with two types of investors and use the heuristic HAM to investigate excess volatility …
(HAM) with two types of investors and use the heuristic HAM to investigate excess volatility …