Browse
Submit Data
Databases
API
Help

Dataset Information

16 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU?

ABSTRACT: This study mainly investigates which predictors (VIX or EPU index) are useful to forecast future volatility for 19 equity indices based on HAR framework during coronavirus pandemic. Out-of-sample analysis shows that the HAR-RV-VIX model exhibits superior forecasting performance for 12 stock markets, while EPU index just can improve forecast accuracy for 5 equity indices, implying that VIX index is more useful for most stock markets' future volatility during coronavirus crisis. The results are robust in recursive window method, alternative realized measures and sub-sample analysis; moreover, VIX index still contains the strongest predictive ability by considering kitchen sink model and mean combination forecast. Furthermore, we further discuss the predictive effect of VIX and EPU index before the coronavirus crisis. Our article provides policy makers, researchers and investors with new insights into exploiting the predictive ability of VIX and EPU index for international stock markets during coronavirus pandemic. Highlights • This study investigates which predictors are useful to forecast future volatility during coronavirus pandemic.• VIX index is more useful for most stock markets’ future volatility during coronavirus crisis.• VIX index still contains the strongest predictive ability by considering KS and mean combination forecast.• The results are robust in recursive window method, alternative realized measures and sub-sample analysis.

SUBMITTER: Wang J

PROVIDER: S-EPMC7521353 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

What threatens stock markets more - The coronavirus or the hype around it?

Project description:We use a linear regularized model with structural changes and found that the coronavirus pandemic had a direct and an indirect effect (via media hype) on stock markets. We reveal a correlation between internet search queries, discussions of the pandemic in the press and social media, and changes in stock market indices. We demonstrated that the effect of the pandemic coverage in digital and printed media and the effect of Google queries was comparable to, and sometimes even exceeded, the effect of the pandemic itself. We showed the effect of hype on the volume of Google queries and social media publications.

| S-EPMC8741485 | biostudies-literature

Fear of the coronavirus and the stock markets.

Project description:Since the outbreak of the COVID-19 pandemic, stock markets around the world have experienced unprecedented declines amid high uncertainty. In this paper, we use Google search volume activity as a gauge of panic and fear. The chosen search terms are specific to the coronavirus crisis and correspond to phrases related to nonpharmaceutical intervention policies to fight physical contagion. We show that during this period, fear of the coronavirus - manifested as excess search volume - represents a timely and valuable data source for forecasting stock price variation around the world.

| S-EPMC7448772 | biostudies-literature

Infected Markets: Novel Coronavirus, Government Interventions, and Stock Return Volatility around the Globe.

Project description:Do government interventions aimed at curbing the spread of COVID-19 affect stock market volatility? To answer this question, we explore the stringency of policy responses to the novel coronavirus pandemic in 67 countries around the world. We demonstrate that non-pharmaceutical interventions significantly increase equity market volatility. The effect is independent from the role of the coronavirus pandemic itself and is robust to many considerations. Furthermore, two types of actions that are usually applied chronologically particularly early-information campaigns and public event cancellations-are the major contributors to the growth of volatility.

| S-EPMC7240275 | biostudies-literature

Applications of gravity models to evaluate and forecast US international air freight markets post-GFC.

Project description:In addition to being the world's largest economy, the United States (US) has been the foremost driver of consumer spending, free trade and open skies policies. Trade-driven economic growth and air freight markets have been commonly linked in air transport research. Despite increased security after the 9/11 terrorist attacks, international air imports to the US has continued to grow at a rate faster than consumer spending. This paper aims to evaluate whether gravity models are robust enough to forecast and accurately account for substantial economic shocks such as the Global Financial Crisis (GFC). Our research suggests that US demand for air freight is highly sensitive to transport costs, competition from sea freight and consumer spending patterns of perishable, low value and high value commodities across the 19 commodity groups examined, rather than manufacturing income or factors associated with product origin, which is all the more interesting in the context of the recent protectionist rhetoric of the US administration.

| S-EPMC7194952 | biostudies-literature

Infectious disease pandemic and permanent volatility of international stock markets: A long-term perspective.

Project description:Understanding the impact of infectious disease pandemic on stock market volatility is of great concerns for investors and policy makers, especially during recent new coronavirus spreading period. Using an extended GARCH-MIDAS model and a newly developed Infectious Disease Equity Market Volatility Tracker (EMV-ID), we investigate the effects of infectious disease pandemic on volatility of US, China, UK and Japan stock markets through January 2005 to April 2020. The empirical results show that, up to 24-month lag, infectious disease pandemic has significant positive impacts on the permanent volatility of international stock markets, even after controlling the influences of past realized volatility, global economic policy uncertainty and the volatility leverage effect. At different lags of eruptions in infectious disease pandemic, EMV-ID has distinct effects on various stock markets while it has the smallest impact on permanent volatility of China's stock market.

| S-EPMC7391063 | biostudies-literature

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets.

Project description:The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction.

| S-EPMC5685607 | biostudies-literature

Linear response theory in stock markets.

Project description:Linear response theory relates the response of a system to a weak external force with its dynamics in equilibrium, subjected to fluctuations. Here, this framework is applied to financial markets; in particular we study the dynamics of a set of stocks from the NASDAQ during the last 20 years. Because unambiguous identification of external forces is not possible, critical events are identified in the series of stock prices as sudden changes, and the stock dynamics following an event is taken as the response to the external force. Linear response theory is applied with the log-return as the conjugate variable of the force, providing predictions for the average response of the price and return, which agree with observations, but fails to describe the volatility because this is expected to be beyond linear response. The identification of the conjugate variable allows us to define the perturbation energy for a system of stocks, and observe its relaxation after an event.

| S-EPMC8630003 | biostudies-literature

LSTM-based sentiment analysis for stock price forecast.

Project description:Investing in stocks is an important tool for modern people's financial management, and how to forecast stock prices has become an important issue. In recent years, deep learning methods have successfully solved many forecast problems. In this paper, we utilized multiple factors for the stock price forecast. The news articles and PTT forum discussions are taken as the fundamental analysis, and the stock historical transaction information is treated as technical analysis. The state-of-the-art natural language processing tool BERT are used to recognize the sentiments of text, and the long short term memory neural network (LSTM), which is good at analyzing time series data, is applied to forecast the stock price with stock historical transaction information and text sentiments. According to experimental results using our proposed models, the average root mean square error (RMSE ) has 12.05 accuracy improvement.

| S-EPMC7959635 | biostudies-literature

Loss of structural balance in stock markets.

Project description:We use rank correlations as distance functions to establish the interconnectivity between stock returns, building weighted signed networks for the stocks of seven European countries, the US and Japan. We establish the theoretical relationship between the level of balance in a network and stock predictability, studying its evolution from 2005 to the third quarter of 2020. We find a clear balance-unbalance transition for six of the nine countries, following the August 2011 Black Monday in the US, when the Economic Policy Uncertainty index for this country reached its highest monthly level before the COVID-19 crisis. This sudden loss of balance is mainly caused by a reorganization of the market networks triggered by a group of low capitalization stocks belonging to the non-financial sector. After the transition, the stocks of companies in these groups become all negatively correlated between them and with most of the rest of the stocks in the market. The implied change in the network topology is directly related to a decrease in stock predictability, a finding with novel important implications for asset allocation and portfolio hedging strategies.

| S-EPMC8190088 | biostudies-literature

Quantifying the randomness of the stock markets.

Project description:Randomness has been mathematically defined and quantified in time series using algorithms such as Approximate Entropy (ApEn). Even though ApEn is independent of any model and can be used with any time series, as the markets have different statistical values, it cannot be applied directly to make comparisons between series of financial data. In this paper, we develop further the use of Approximate Entropy to quantify the existence of patterns in evolving data series, defining a measure to allow comparisons between time series and epochs using a maximum entropy approach. We apply the methodology to the stock markets as an example of its application, showing that the number of patterns changed for the six analyzed markets depending on the economic situation, in agreement with the Adaptive Markets Hypothesis.

| S-EPMC6726611 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data