The study identifies different global dependence regimes in equity and volatility indices.
problem Varying correlations and higher moments between equity and volatility indices across continents.
method Markov-switching R-vine models to investigate changing dependence structures. result Global regime switching identified in times of 'normal' and 'abnormal' states.
Study infers volatility indicators from Bitcoin blockchain data.
problem Predicting extreme price volatility in Bitcoin.
method Non-negative decomposition of Bitcoin transaction graphs.
result EWI provides more predictive information than other methods.
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.
Study on distributions of realized and implied volatility, using Generalized Beta distribution.
problem Understanding the differences and relationships between realized and implied volatility distributions.
method Used Generalized Beta distribution to fit distributions of realized variance and implied volatility (VIX, VXO). Analyzed differences and correlations.
result Generalized Beta distribution provides the best fit for realized variance but not for implied volatility indices (VIX, VXO).
Study finds Bitcoin volatility exhibits roughness, not constant over time.
problem Investigating roughness in Bitcoin volatility.
method Multifractal detrended fluctuation analysis and shuffled time series analysis.
result Bitcoin volatility exhibits roughness (generalized Hurst exponent < 1/2).
Transformer model with mixed-frequency data improves stock volatility prediction.
problem Improving stock volatility prediction using mixed-frequency data.
method Transformer model trained on mixed-frequency data (GARCH-MIDAS model for frequency alignment).
result Transformer model reduces mean square error from 1.00 to 0.86.
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.
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.
Graph Neural Networks improve volatility prediction in financial markets.
problem Traditional models struggle with complex, non-linear interdependencies in financial markets.
method Temporal Graph Attention Network (Temporal GAT) combines GCNs and GATs to capture dynamic graph structures.
result Temporal GAT outperforms traditional GARCH models in volatility forecasting, especially for short- to mid-term predictions.
Using a time-varying approach, this paper examines the dynamics of volatility in the REIT sector. The results highlight the attractiveness and suitability of using GARCH based approaches in the modeling of daily REIT volatility. The paper examines the influencing factors on REIT volatility, documenting the return and v…
Paper ranks stocks by compression risk, not volatility.
problem Investment risk not correlated with stock price volatility.
method Binary-ternary compressive coding of price change time series.
result Compression risk is a better indicator of stock investment risk.
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.
Paper uses neural networks to analyze oil price impact on Iranian stock and industry indices.
problem Impact of oil price volatility on Tehran stock and industry indices.
method Feed-forward neural networks analysis of two periods: sanctions and post-sanctions.
result Neural networks predict stock and industry indices well, showing significant oil price volatility impact.
The study explains asymmetric volatility using anchoring bias in investor behavior.
problem Understanding the cause of asymmetric volatility in financial markets.
method Empirical analysis of anchoring bias in S&P 500 price fluctuations.
result Anchoring bias explains the asymmetry in volatility responses to shocks.
Measures of implied volatility roughness corrected for bias.
problem Bias in measuring implied volatility roughness.
method Examined implied volatility of short-term options and VIX index, corrected for bias.
result Corrected measures indicate appropriate proxies for underlying volatility.
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.
We investigate the probability distribution of the return intervals τ between successive 1-min volatilities of two Chinese indices exceeding a certain threshold q. The Kolmogorov-Smirnov (KS) tests show that the two indices exhibit multiscaling behavior in the distribution of τ, which follows a stretched exponent…
In a highly interdependent economic world, the nature of relationships between financial entities is becoming an increasingly important area of study. Recently, many studies have shown the usefulness of minimal spanning trees (MST) in extracting interactions between financial entities. Here, we propose a modified MST n…
Quantum circuits predict volatility dynamics preserving asymmetry.
problem Modeling volatility time series with asymmetry.
method Single-qubit quantum circuit learning (QCL) applied to synthetic data generated by Rational GARCH model.
result QCL-based predictions preserve negative return-volatility correlation and anti-persistent behavior.
With the daily and minutely data of the German DAX and Chinese indices, we investigate how the return-volatility correlation originates in financial dynamics. Based on a retarded volatility model, we may eliminate or generate the return-volatility correlation of the time series, while other characteristics, such as the…
Study finds stock markets follow nonextensive statistical mechanics.
problem Understanding nonextensivity in stock market volatilities.
method Analysis of 34 major stock market indices over 10 years.
result Stock markets exhibit nonextensive behavior, distinguishing between developed and developing countries.
We compare the most common SV models such as the Ornstein-Uhlenbeck (OU), the Heston and the exponential OU (expOU) models. We try to decide which is the most appropriate one by studying their volatility autocorrelation and leverage effect, and thus outline the limitations of each model. We add empirical research on ma…
Study examines volatility-based strategy for Chinese ETF options, improving returns in volatile markets.
problem Lack of effective trading strategies in volatile Chinese equity markets.
method Volatility forecasting using GARCH models to dynamically adjust positions and exposures.
result Dynamic adjustment of positions and exposures enhances returns in volatile markets.
Study uses topological signatures to quantify financial market complexity.
problem Capturing temporal organization beyond volatility measures.
method Null validated topological approach using L1 norm of persistence landscapes. result Persistence landscape norms reveal dynamical structure during market stress.
Study adapts OHLC volatility estimators for monitoring market stress in diverse settings.
problem Limited use of range-based volatility estimators in local commodity markets.
method Adapted OHLC volatility estimators to monitor market distress across various contexts.
result OHLC-based volatility indicators detect market disruptions missed by standard momentum indicators.
Bitcoin volatility analysis shows decreasing HE with longer sampling periods.
problem Understanding volatility patterns in Bitcoin using different sample sizes.
method Examined Bitcoin data to analyze Hurst exponent (HE) and multifractality.
result HE decreases as sampling period increases, indicating rough 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.
Developed a method to detect jumps and estimate volatility in financial data.
problem Identifying jumps in financial time series data.
method Threshold method for jump detection and volatility estimation.
result Unprecedented accuracy in volatility estimation across various parameter values.
Study finds ESG investments more resilient than traditional equity indices during market turmoil.
problem Resilience of ESG investments during financial instability.
method Daily returns analysis using MGND and EGARCH-in-mean models.
result ESG investments show higher resilience compared to traditional equity indices during crises.
Persistence norms explain financial uncertainty better than volatility.
problem Capturing financial instability and predictability.
method Applied topological data analysis to financial markets.
result Persistence norms are significant in explaining financial uncertainty, while volatility is less effective.
New model explains low-volatility anomaly using adaptive multi-factor approach.
problem Explaining the low-volatility anomaly in stock markets.
method Used Adaptive Multi-Factor (AMF) model with GIBS algorithm to identify significant risk factors.
result Low-volatility portfolios perform better due to loaded risk factors, not just low volatility.
The Chicago Board Options Exchange (CBOE) Volatility Index, VIX, is calculated based on prices of out-of-the-money put and call options on the S&P 500 index (SPX). Sometimes called the "investor fear gauge," the VIX is a measure of the implied volatility of the SPX, and is observed to be correlated with the 30-day real…
Study on estimating volatility of volatility using Fourier methods and provides insights into volatility dynamics.
problem Estimating the volatility of volatility (vol-of-vol) accurately and efficiently.
method Used Fourier methodology to estimate integrated volatility of volatility, bias-corrected and without bias-correction, comparing their asymptotic properties and accuracy.
result The bias-corrected estimator reaches the optimal rate n1/4, while the uncorrected estimator has a slower rate and smaller asymptotic variance. This study compares three volatility metrics for Bitcoin, highlighting high expected volatility.
problem Understanding Bitcoin's volatility in financial markets.
method Historical volatility, forecasted volatility (GARCH models), and implied volatility (from options market).
result High expected volatility across all methodologies, especially implied volatility.
Study shows best fit of squared vol ratios by Beta Prime distribution.
problem Analyzing correlations between VIX, VXO, and realized variances.
method Examined VIX, VXO squared vol correlations and realized variances, fitted with Beta Prime distribution.
result Ratio of squared vol indices best fitted by Beta Prime distribution with parameters dependent on month.
Symbolic regression finds simple formulas for implied volatility.
problem Discovering accurate parametric representations for implied volatility.
method Symbolic regression to find analytic formulas from market data.
result Symbolic regression identifies compact parametrizations with competitive fitting performance.
New model predicts implied volatility using past asset price paths.
problem Forecasting implied volatility surfaces and asset prices.
method Proposes a new model using past asset price trajectories to predict implied volatility.
result Large part of implied volatility movements can be explained by past returns and squares.
QTMRL uses RL with multi-indicators to improve trading adaptability.
problem Traditional trading models fail in volatile markets due to rigid assumptions.
method Combines multi-indicators with RL for adaptive portfolio management.
result QTMRL outperforms baselines in profitability and risk control.
We investigate the large-volatility dynamics in financial markets, based on the minute-to-minute and daily data of the Chinese Indices and German DAX. The dynamic relaxation both before and after large volatilities is characterized by a power law, and the exponents p± usually vary with the strength of the large vo…
Deep learning model predicts stock volatility using Google trends.
problem Predicting stock volatility using neural networks.
method Long Short-Term Memory neural network with Google domestic trends as input.
result Model outperforms benchmarks with MAPE of 24.2%.
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.
Study examines how trading volumes and transactions affect stock volatility.
problem Understanding the impact of trading volumes and transactions on stock volatility.
method Used GARCH models to analyze daily stock data of the Tokyo Stock Exchange.
result GARCH effects are not always removed by adding trading volumes or transactions, suggesting they don't fully represent information arrivals.
New models explain rough and persistent volatility patterns.
problem Understanding and modeling the rough and persistent nature of asset price volatility.
method Introduced a new class of continuous-time models based on the Brownian semistationary process.
result Models show evidence of roughness and long memory in volatility time series.
Enhanced volatility model using LSTM and realized volatility.
problem Volatility modeling in financial markets.
method Combining deep learning (LSTM) and realized volatility measures in a Bayesian framework.
result Superior predictive performance compared to benchmark models.
Study compares ANN and GARCH models for volatility prediction across sectors.
problem Comparing ANN and GARCH models for volatility prediction.
method Examined five sectors with low, medium, and high volatility, using three GARCH specifications and three ANN architectures.
result ANN model performs better for low volatility, GARCH for medium and high.
Study examines volatility of Nikkei Stock Average, finding returns follow a Gaussian process.
problem Analyzing volatility of Nikkei Stock Average on Tokyo Stock Exchange.
method Calculated realized volatility in morning and afternoon sessions, investigating return dynamics.
result Return dynamics of Nikkei Stock Average are consistent with Gaussian distribution.
Paper proposes a method to robustly estimate volatility from OTM options.
problem Accurately measuring volatility in real-world markets with limited option trading.
method Constructs an arbitrage-free continuous option pricing function from bid-ask spreads of OTM options.
result Robustly calculates volatility indices with theoretical consistency, even in low-liquidity markets.
Simulation of financial markets with 300 assets shows volatility clustering and unstable periods.
problem Understanding volatility clustering and unstable periods in multi-asset financial markets.
method Large-scale simulation of an Ising-based financial market model with 300 assets.
result Volatility clustering and unstable periods identified in the simulated financial market.