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.
Study shows feedback effect between capital flows volatility and financial stability in DRC.
problem Volatility of capital flows can undermine financial stability in DRC.
method Dynamic regression model and vector autoregressive (VAR) model to analyze feedback effects and policy impacts.
result Feedback effect between capital flows volatility and financial stability exists in DRC, but policies do not effectively mitigate volatility.
Study shows death ratio of COVID-19 deaths increases financial volatility.
problem Impact of COVID-19 deaths on financial markets.
method Analysis of VIX index based on daily case reports and death ratios.
result Death ratio positively influences financial volatility.
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…
Social media signals are most informative about financial volatility when sentiment is high.
problem Understanding when social media can predict financial market volatility.
method Cluster analysis of social and financial variables using information theory.
result Social media is most informative about financial volatility when the ratio of bullish to bearish sentiment is high.
The paper introduces a new volatility model for state heterogeneous financial markets using high-frequency data.
problem State heterogeneity in financial volatility processes.
method Developed a state heterogeneous GARCH-Ito (SG-Ito) model based on continuous Ito diffusion process.
result Empirical studies reveal various state heterogeneities in S&P 500 index volatility.
Model simulates financial time series with volatility clustering and cross correlations.
problem Simulate financial time series with volatility clustering and cross correlations.
method Introduced an Ising model with interactions between financial time series.
result Simulated financial time series exhibit volatility clustering and cross correlations.
The study models geophysical and financial volatility using GARCH and stochastic volatility models.
problem Forecasting volatility in geophysical and financial time series.
method Presented a class of volatility models with time-varying parameters, using GARCH and stochastic volatility models.
result Stochastic volatility model outperforms GARCH (1, 1) in forecasting one-step-ahead volatility.
LSTM models struggle with volatility prediction due to financial complexities.
problem Volatility prediction in financial markets is challenging due to various factors.
method Comparison of LSTM models with econometric models for volatility prediction.
result LSTM models do not outperform strong econometric models in volatility prediction.
DSVM model predicts financial market volatility with better accuracy.
problem Predicting financial market volatility accurately.
method Deep latent variable models with variational inference.
result DSVM outperforms GARCH models in predicting volatility.
TimeMixer predicts global financial asset volatility, excelling in short-term forecasts.
problem Predicting volatility in global financial markets is challenging due to complexity and non-linear dynamics.
method Uses TimeMixer, a multiscale-mixing model for forecasting across different scales.
result TimeMixer performs exceptionally well in short-term volatility forecasting but less so in longer-term predictions.
Study volatility spillovers among many financial assets using a t-distributed VAR model.
problem Understanding volatility spillovers among multiple financial assets.
method Used a large t-Vector AutoRegressive (VAR) model with t-distributed errors for a large number of assets.
result Revealed bidirectional volatility spillovers between energy and biofuel, and between energy and agricultural commodities.
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.
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.
The article reviews how to set stochastic volatility model parameters.
problem Choosing parameters for stochastic volatility models.
method Examines existing literature on various methods.
result Different approaches to setting stochastic volatility parameters.
Develops neural network for implied volatility surface prediction with financial domain knowledge.
problem Predicting implied volatility surface using neural networks.
method Incorporates prior financial domain knowledge into neural network architecture and training process.
result Model outperforms benchmarks and satisfies financial conditions.
Proposes a new metric for financial risk based on volatility's local deviations.
problem Inefficiencies in classical risk metrics like volatility.
method Introduces pointwise regularity via the Hurst-Holder exponent.
result A more nuanced assessment of market inefficiencies and mechanisms for restoring equilibrium.
New method identifies uncertainty shocks in financial markets using revised VIX.
problem Traditional VIX fails to capture non-Gaussian, heavy-tailed asset returns.
method Fit a double-subordinated Normal Inverse Gaussian Levy process to S&P 500 option prices to construct a revised VIX.
result Revised VIX provides a more comprehensive measure of volatility reflecting extreme movements and heavy tails.
Study quasiconvex risk measures in volatile financial markets.
problem Financial risk measurement in markets with variable volatility.
method Defined quasiconvex risk measures on Lp(⋅) space with p(⋅) as a random variable. result Deduced dual representation for the defined quasiconvex risk measures.
New financial volatility models capture dynamic volatility better.
problem Traditional volatility models miss important volatility dynamics.
method Integrate recurrent neural networks into GARCH models.
result Improved in-sample and out-of-sample volatility forecasting.
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.
This paper develops dynamic risk measures for fluctuating market volatility.
problem Complex and heightened market volatility requires new risk measures.
method Introduces risk measures on Lp(⋅) space with variable exponent. result Established dual representations for these risk measures.
This paper proposes a new framework for financial risk that considers predictability rather than volatility.
problem Volatility's limitations as a risk measure, especially in complex strategies and non-stationary markets.
method Developed a new paradigm based on stochastic processes and the Multifractional Process with Random Exponent (MPRE) framework.
result A formal definition of 'fair volatility' that aligns with market efficiency and provides a measure of market inefficiency.
M2VN forecasts financial volatility by fusing time series data with news embeddings.
problem Forecasting financial volatility with unstructured news data.
method Combines deep neural networks with open-source market features and news embeddings.
result M2VN outperforms existing models in financial volatility forecasting.
The paper optimizes financial derivatives for market completion in SV models.
problem Optimizing financial derivatives for market completion in stochastic volatility models.
method Simulation-based method to approximate optimal portfolio strategy, using double optimization approach (utility maximization and risk exposure minimization).
result Strangle options are the best choices for market completion in equity options.
New financial model with sandwiched volatility for option pricing.
problem Developing a new financial model for option pricing.
method Introducing a new model with stochastic volatility driven by a Gaussian Volterra process, ensuring the solution is sandwiched between two arbitrary Hölder continuous functions.
result Developed an algorithm for pricing options with discontinuous payoffs using Malliavin calculus.
The relaxation dynamics of aftershocks after large volatility shocks are investigated based on two high-frequency data sets of the Shanghai Stock Exchange Composite (SSEC) index. Compared with previous relevant work, we have defined main financial shocks based on large volatilities rather than large crashes. We find th…
Random walk of expectations explains volatility clustering in financial markets.
problem Volatility clustering in financial markets is a predictable pattern in price changes.
method Modeling expectations as a random walk to explain volatility clustering.
result Random walk of expectations leads to volatility clustering.
We calculate the realized volatility in the spin model of financial markets and examine the returns standardized by the realized volatility. We find that moments of the standardized returns agree with the theoretical values of standard normal variables. This is the first evidence that the return dynamics of the spin fi…
Forecast future volatilities and correlations based on current trends.
problem Predict future volatilities and correlations in financial markets.
method Use cubic and quadratic polynomials of current trend strengths.
result Accurate quantification of trend effects on volatilities and correlations.
A spin model is used for simulations of financial markets. To determine return volatility in the spin financial market we use the GARCH model often used for volatility estimation in empirical finance. We apply the Bayesian inference performed by the Markov Chain Monte Carlo method to the parameter estimation of the GAR…
Agents learn implied volatility in financial markets, resolving theory-practice gap.
problem Inconsistent volatility across strike prices in Black-Scholes models.
method Introduce learning agents updating beliefs based on market opinions, prove convergence using control theory.
result Opinion dynamics converge to true implied volatility, resolving model-practice discrepancy.
Proposes a new model for estimating financial volatility with jumps.
problem Estimating volatility from financial time series with jumps.
method Gibbs Sampler with exact posterior distributions.
result Model captures speculative movements and propagates jumps in volatility.
New analysis shows low volatility can be unstable in financial markets.
problem Understanding the relationship between volatility and market stability.
method Using mean first hitting time as a stability indicator and comparing to standard volatility measures.
result Low volatility can be associated with higher instability in financial markets.
Solves financial volatility clustering using Minkowski metric in GARCH(1,1) model.
problem Financial volatility clustering and long memory process.
method Minkowski metric applied to GARCH(1,1) model.
result Equivalent to dark volatility or hidden risk fear field.
New mathematical tools help analyze rough volatility in financial markets.
problem Mathematical models of rough volatility lost Markovianity and semi-martingality.
method Use of Hairer's regularity structures, an extension of rough path theory.
result Shows regularity structures can analyze rough volatility models.
Financial volatility risk and its relation to a business cycle-related intrinsic time is addressed through a multiple round evolutionary quantum game equilibrium leading to turbulence and multifractal signatures in the financial returns and in the risk dynamics. The model is simulated and the results are compared with …
The paper examines how long-memory dynamics, rough-volatility, and persistence affect equity volatility forecasting.
problem The study investigates how long-memory dynamics, rough-volatility, and persistence impact equity volatility forecasting.
method The paper combines semiparametric long-memory estimation, rough-volatility diagnostics, and structured forecasting regressions.
result Persistence measures improve out-of-sample volatility forecasts, particularly during periods of elevated market volatility and in volatility-managed portfolio applications.
We propose a novel method to quantify the clustering behavior in a complex time series and apply it to a high-frequency data of the financial markets. We find that regardless of used data sets, all data exhibits the volatility clustering properties, whereas those which filtered the volatility clustering effect by using…
In this paper, we model financial markets with semi-Markov volatilities and price covarinace and correlation swaps for this markets. Numerical evaluations of vari- nace, volatility, covarinace and correlations swaps with semi-Markov volatility are presented as well. The novelty of the paper lies in pricing of volatilit…
New method clusters financial time series into volatility regimes.
problem Finding the number of volatility regimes in nonstationary financial time series.
method Change point detection and clustering of segment distributions.
result Optimized trading strategy based on learned volatility regimes.
We investigate financial markets under model risk caused by uncertain volatilities. For this purpose we consider a financial market that features volatility uncertainty. To have a mathematical consistent framework we use the notion of G-expectation and its corresponding G-Brownian motion recently introduced by Peng (20…
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…
Model shows financialization increases agricultural commodity market volatility.
problem Impact of financialization on agricultural commodity markets.
method Stylized model of production and exchange with long-term and short-term investors.
result Financialization increases farms' default risk and production output volatility.
The paper examines sizing strategies for algorithmic trading in volatile markets.
problem High volatility creates challenges for algorithmic traders.
method Investigates different sizing models and backtesting techniques for financial trading.
result Sizing models can lower Value at Risk (VaR) during crisis events.
We detect and quantify asymmetries in volatility spillovers using the realized semivariances of petroleum commodities: crude oil, gasoline, and heating oil. During the 1987--2014 period we document increasing spillovers from volatility among petroleum commodities that substantially change after the 2008 financial crisi…
Study uses neural networks for fast Hawkes model parameter estimation in finance.
problem Estimating parameters of Hawkes models from high-frequency financial data.
method Recurrent neural networks for parameter estimation.
result Significantly faster computational performance compared to traditional methods.
Proposes a new way to represent uncertainty using implied volatility.
problem Uncertainty in financial markets and biological systems.
method Mathematical analysis of various probability distributions.
result Representation of different probability distributions using BSM implied volatility.