Model forecasts global stock market volatility using dynamic graphs and all trading days.
problem Enhance forecasting accuracy and practical utility in global stock market volatility.
method Spatial-temporal graph neural network architecture to capture volatility spillover effect.
result Forecasting performance surpasses baseline models in all scenarios.
Paper uses AI to predict stock market volatility with neural networks and genetic algorithms.
problem Traditional methods for predicting stock market volatility have high errors.
method Back-propagation neural network and genetic algorithm integrated model.
result The model predicts future volatility with low errors and high accuracy.
Study uses CSIE to estimate portfolio volatility relative to market.
problem Estimating relative volatility risk of stock portfolios.
method Cross-sectional intrinsic entropy (CSIE) model to estimate cross-sectional volatility.
result Discover sets of symbols that outperform market indices in terms of return with similar or lower risk.
VolTS uses stats & ML to forecast stock market trends based on volatility.
problem Capturing profitable trading opportunities from market dynamics.
method Combines statistical analysis with machine learning; k-means++ clustering, Granger causality test.
result Effective at identifying profitable trading opportunities through volatility clusters and Granger causality.
Graph Signal Processing improves stock market volatility forecasting.
problem Forecasting realized volatility in a global stock market context.
method Integrating Graph Signal Processing into the HAR model.
result The proposed model outperforms HAR-type benchmarks.
This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. We use the cross-quantilogram, …
New stock market index captures market chaos and volatility.
problem Capturing the chaotic nature of stock market volatility.
method Tensor-based embedding of stock market information, time-dependent dynamical system model.
result Bidirectional causal relation between realized and implied volatility.
Paper introduces CSIE for estimating stock market volatility.
problem Temporal uncertainty in stock market volatility.
method Cross-sectional intrinsic entropy model based on OHLC prices.
result CSIE is 10 times more sensitive to market changes.
We study historical correlations and lead-lag relationships between individual stock risk (volatility of daily stock returns) and market risk (volatility of daily returns of a market-representative portfolio) in the US stock market. We consider the cross-correlation functions averaged over all stocks, using 71 stock pr…
This study examines investor sentiment's impact on stock market liquidity and volatility using deep learning and TVP-VAR models.
problem Investor sentiment's impact on stock market liquidity and volatility.
method Deep learning BERT model for sentiment extraction and TVP-VAR model for time-varying analysis.
result Investor sentiment has a stronger impact on stock market liquidity and volatility, with more pronounced effects in short-term shocks.
New models analyze how ECB's unconventional policies affect stock market volatility.
problem Analyzing the impact of ECB's unconventional policies on stock market volatility.
method Developed MEM with Asymmetry and Policy effects (MAP) models to separate base volatility from policy effects.
result Significant improvement in forecasting power after Expanded Asset Purchase Programme implementation.
This paper develops a two-step estimation methodology, which allows us to apply catastrophe theory to stock market returns with time-varying volatility and model stock market crashes. Utilizing high frequency data, we estimate the daily realized volatility from the returns in the first step and use stochastic cusp cata…
Improves stock market predictions on Election Day.
problem Predicting stock market volatility on Election Day.
method Combining large language models with specialized agents.
result EDSMF model improves S&P 500 prediction accuracy.
This paper calculates risk-dependent centrality of Brazilian stocks, showing rankings vary with external risk and crisis events.
problem Understanding asset rankings in the Brazilian stock market under varying external risks.
method Computed risk-dependent centrality (RDC) for Brazilian stocks traded from 2008 to 2020, analyzing volatility and returns.
result Asset rankings based on RDC vary with external risk and crisis events, with higher volatility in crisis periods.
Study examines asymmetry impacts on Japanese stock market volatility modeling and forecasting.
problem Understanding asymmetry's impact on modeling and forecasting realized volatility in Japanese stock markets.
method Employed heterogeneous autoregressive (HAR) models with three types of asymmetry: positive and negative realized semivariance, asymmetric jumps, and leverage effects.
result Leverage effects significantly influence realized volatility modeling and forecast performance in Japanese stock markets.
Study improves stock price prediction using adaptive Mixture of Experts framework.
problem Tackles diverse volatility regimes in stock price prediction.
method Combines RNN for high-volatility stocks and linear regression for stable stocks with a gating mechanism.
result Achieves up to 33% improvement in MSE for volatile assets and 28% for stable assets.
Study examines how COVID-19 affected stock and crypto market efficiency.
problem Impact of COVID-19 on market efficiency of different asset classes.
method Analysis of price returns, absolute returns, and volatility increments in stock and cryptocurrency markets.
result Market efficiency varied by asset class and market, with some time series showing gradual decline over time.
New model forecasts stock market volatility better than existing methods.
problem Forecasting volatility in stock markets.
method Combines HAR model with path-dependent volatility models.
result HAR-PD model family outperforms basic HAR model family in volatility forecasting.
We exploit a continuous time random walk description of stock prices to obtain a fast and accurate evaluation of their volatility from intraday data. We show that financial markets are usefully described as open physical systems. Indeed we find that the process determining market volatility is not stationary while the …
Predicts stock volatility using Twitter data and random forests.
problem Predicting stock implied volatility using Twitter data.
method Random forests with ablation study on different predictors, including Twitter attention and sentiment features.
result Certain sectors like Consumer Discretionary, Technology, Real Estate, and Utilities are easier to predict.
The study improves stock market valuation using volatility and earnings data.
problem Improving stock market valuation metrics.
method Time series model for asset returns, multivariate kernel density estimation, linear regression.
result The valuation measure is an improvement over Shiller's P/E ratio.
The Financial Chaos Index models stock market volatility across three regimes based on mutual price fluctuations.
problem Capturing regime-dependent volatility in stock markets.
method Developed a regime-switching framework using the Financial Chaos Index (FCIX) and elastic net regression.
result Identified three market regimes: low-chaos, intermediate-chaos, and high-chaos, each with distinct volatility characteristics.
Study shows COVID-19 cases increase stock market volatility in Pakistan.
problem Impact of COVID-19 on stock market volatility in Pakistan.
method Used vector autoregressive (VAR) model to analyze data from February 25, 2020 to December 7, 2020.
result A shock to total daily coronavirus cases in Pakistan leads to a significant increase in stock market volatility.
Asymmetries in volatility spillovers are highly relevant to risk valuation and portfolio diversification strategies in financial markets. Yet, the large literature studying information transmission mechanisms ignores the fact that bad and good volatility may spill over at different magnitudes. This paper fills this gap…
We examine volatility of an Indian stock market in terms of aspects like participation, synchronization of stocks and quantification of volatility using the random matrix approach. Volatility pattern of the market is found using the BSE index for the three-year period 2000-2002. Random matrix analysis is carried out us…
New systemic risk indicator measures stock market reactions globally.
problem Analyzing systemic risk in diverse financial markets.
method Implied and realized volatility approach, focusing on historical and long-term volatility.
result IVRVSRI shows varying stock market reactions and shock persistence across locations.
The study explains how market-makers' hedging affects stock volatility during gamma-squeeze events.
problem Endogenous volatility amplification in option markets during gamma-squeeze events.
method Developed a theoretical framework linking hedging behavior and market turbulence, incorporating beta-normalized volatility.
result Low-beta stocks amplify volatility more during gamma-squeeze events.
In this paper, we employ the Heston stochastic volatility model to describe the stock's volatility and apply the model to derive and analyze the optimal trading strategies for dealers in a security market. We also extend our study to option market making for options written on stocks in the presence of stochastic volat…
Analyzes how rough volatility affects stock pricing and risk premium.
problem Impact of non-deterministic volatility risk on stock pricing.
method Rough volatility model under historical measure, analysis of stochastic volatility risk.
result Impact of non-deterministic volatility risk on pricing is significant.
We shortly review the statistical properties of the escape times, or hitting times, for stock price returns by using different models which describe the stock market evolution. We compare the probability function (PF) of these escape times with that obtained from real market data. Afterwards we analyze in detail the ef…
This paper uses Gaussian processes to forecast short-term stock price volatility.
problem Inaccurate short-term volatility forecasts for high-frequency trades.
method Combines numerical and probabilistic models, specifically Gaussian Processes (GPs), to correct and forecast stock price data.
result Effective short-term volatility forecasts for high-frequency trades using Gaussian Processes.
This paper surveys NLP techniques for predicting stock market movements.
problem Predicting stock market movements with volatility, seasonality, and time dependence.
method Survey of recent literature in NLP and ML for stock market prediction.
result Illustrates recent trends in stock market prediction research.
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. Our article combines these two methods in a non-trivial way and proposes a model, which we call th…
Study examines short-term stress of COVID-19 on major global stock indices.
problem Short-term impact of COVID-19 on global stock markets.
method Secondary data from 41 stock exchanges in 32 countries, focusing on first reported cases.
result Volatility in stock markets increases with the rise of COVID-19 cases, and there is a significant negative correlation.
The intrinsic entropy model accurately estimates stock market volatility.
problem Accurately estimating historical volatility of stock market indices.
method Incorporates traded volumes alongside OHLC prices in daily data.
result Intrinsic entropy model delivers reliable estimates with lower coefficient of variation.
Recent studies show that a negative shock in stock prices will generate more volatility than a positive shock of similar magnitude. The aim of this paper is to appraise the hypothesis under which the conditional mean and the conditional variance of stock returns are asymmetric functions of past information. We compare …
We study the cascading dynamics immediately before and immediately after 219 market shocks. We define the time of a market shock T_{c} to be the time for which the market volatility V(T_{c}) has a peak that exceeds a predetermined threshold. The cascade of high volatility "aftershocks" triggered by the "main shock" is …
This study analyzes factors affecting China's stock market volatility.
problem Investigating factors influencing China's stock market volatility.
method Used ARDL model and principal component analysis on daily data from 2010-2024.
result Exchange rate and bond yields significantly impact stock market volatility.
While the use of volatilities is pervasive throughout finance, our ability to determine the instantaneous volatility of stocks is nascent. Here, we present a method for measuring the temporal behavior of stocks, and show that stock prices for 24 DJIA stocks follow a stochastic process that describes an efficiently pric…
Graph auto-encoders predict stock market instability by measuring graph structure changes.
problem Forecasting stock market instability and volatility.
method Use graph auto-encoders to reconstruct graph structure and measure changes.
result Higher GAE reconstruction error correlates with higher volatility.
The study classifies policy announcements' impact on stock market volatility.
problem Evaluating the impact of Central Bank announcements on stock market volatility.
method Proposed a model-based classification method using Markov Switching dynamics and Multiplicative Error Model.
result Successful classification of 144 European Central Bank announcements on stock market volatility.
This paper examines how the U.S.--China trade war affects stock markets, finding evidence of financial contagion and changes in risk channels.
problem The impact of the U.S.--China trade war on stock markets and financial contagion.
method Developed a novel jump-diffusion process to account for risk contagion, using high-frequency financial data and quasi-maximum likelihood estimator.
result Evidence of financial contagion from the U.S. to China, with changes in risk contagion channels.
Deep learning reveals lagged correlations in stock markets, showing accuracy decreases with shorter prediction horizons.
problem Capturing non-linear interactions in financial prediction problems using large-scale datasets.
method Applying deep learning to econometrically constructed gradients to learn and exploit lagged correlations among S&P 500 stocks.
result Model accuracies decrease with shorter prediction horizons, but remain significant in both stable and volatile markets.
Study on stock market volatility and return dispersion during COVID-19.
problem Impact of COVID-19 on stock market volatility and return dispersion.
method Used Google index to proxy epidemic impact, modeled volatility, and analyzed influencing factors of log-return.
result Volatility significantly affected by epidemic and cross-sectional return dispersion, with positive coefficients.
In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we al…
This study uses NLP to predict stock performance based on analyst reports.
problem Predicting stock performance using textual information from analyst reports.
method Natural language processing (NLP) and a customized BERT deep learning model for Chinese text.
result Strong positive sentiment in analyst reports increases excess return and intraday volatility, while strong negative sentiment increases volatility and trading volume but decreases excess return.
Study finds Twitter activity correlates with stock volatility but not sentiment.
problem Understanding the impact of social media on stock market dynamics.
method Collected and analyzed tweets from Twitter and Reddit, examining their sentiment and correlation with stock volatility.
result Twitter activity correlates with stock volatility but not sentiment.
Study examines how institutional differences and crises affect volatility in ASEAN stock markets.
problem Understanding how institutional differences and crises impact volatility in emerging Asian stock markets.
method By-window EGARCH/TGARCH analysis of daily stock index returns for Indonesia, Malaysia, and the Philippines from 2010 to 2024.
result All three markets show strong volatility persistence and fat-tailed returns; crises increase persistence and asymmetry, while tail thickness rises.