A new model decomposes equity returns and volatilities into memory components.
problem Understanding long-term equity dynamics and volatility patterns.
method Proposes a multivariate generalization of the variance ratio to decompose long-horizon equity dynamics.
result Identifies a five-factor model capturing persistent, antipersistent, and multi-scale memory in returns and volatility.
Method identifies financial rogue waves close to their onset.
problem Identifying extreme financial events close to their onset.
method Analogy between rogue waves in optics and financial volatility, using Schrödinger equation with potential shaped by Kerr nonlinearity.
result Numerical gradient spikes at the onset of extreme financial events.
CryptoGAT improves cryptocurrency price prediction by treating it as a graph problem.
problem Cryptocurrency price prediction challenges due to extreme volatility.
method CryptoGAT, a Graph Attention Network, redefines cryptocurrency prediction as a cross-asset graph problem.
result CryptoGAT outperforms state-of-the-art methods in cryptocurrency price prediction.
Monotonicity of normalized implied-volatility coordinates under no-arbitrage
problem Monotonicity of normalized implied-volatility coordinates under no-arbitrage
method Elementary discrete no-arbitrage proof
result Monotonicity principle extended to Bachelier implied volatility
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 hybrid Convolutional VAE predicts crypto volatility surfaces, outperforming single-symbol approaches.
problem Predicting crypto volatility surfaces
method Convolutional VAE with hybrid predictor
result Model achieves 0.94-1.56 vol-point RMSE across BTC and ETH markets
Volatility forecasting and return prediction in high-frequency Chinese equity markets.
problem Improving statistical forecasting performance and economic strategy outcomes in equity markets.
method Developing a sequential two-stage framework combining realized volatility modeling and XGBoost return prediction.
result Regime-aware volatility forecasting outperforms baseline models.
A new method models financial returns by separating sign and magnitude, improving forecasting accuracy.
problem Capturing nonlinear predictability in financial return dynamics.
method Decomposes returns into sign and magnitude components, using a joint distribution model.
result Significantly outperforms traditional linear models in forecasting U.S. stock market returns.
The paper categorizes and analyzes various event-linked perpetual futures contracts.
problem Developing a risk-design framework for complex event-linked perpetual futures.
method Formal taxonomy of seven pure-form canonical variants, organized along four design axes.
result Detailed analysis of microstructure properties and limitations of various variants.
Modeling stock returns and volatility using a bivariate gamma generalized Laplace law.
problem Analyzing stock returns and volatility using a new statistical model.
method Maximum likelihood estimation for a bivariate generalized Laplace distribution, simplifying to linear regression.
result Explicit estimators derived with nonstandard convergence rates for certain parameter configurations.
ETFs with 2x and 3x leverage underperformed the S&P 500 index due to compounding and volatility.
problem ETFs with higher leverage failed to match the performance of the underlying index.
method Analyzed the performance of leveraged ETFs compared to the S&P 500 index, accounting for compounding and volatility.
result Two-thirds of the underperformance was due to compounding and volatility, with the rest due to covariance.
Analyzes multi-day stock returns, showing linear volatility and mean dependence.
problem Linear dependence of volatility and mean in accumulated stock returns.
method Modified Jones-Faddy skew t-distribution analysis.
result Linear dependence of volatility and mean on the number of days of accumulation.
Kalshi prediction markets forecast cryptocurrency volatility through monetary policy and inflation signals.
problem Forecasting cryptocurrency volatility using prediction markets.
method Monetary policy and inflation signals from Kalshi prediction markets.
result Signals from Kalshi prediction markets predict cryptocurrency volatility with statistical significance.
Dynamic tracking error framework shows similar performance but varying volatility across different constraints.
problem Differences in governance parameters between Total Portfolio Approach and Strategic Asset Allocation.
method Portfolio simulations using U.S. equity and bond data from 2000 to 2026, spanning 2004 to 2026.
result Realized tracking error volatility varies 12-fold across different constraints, with costs highest during crises.
Cryptocurrency markets show similar returns but different volatility responses to infrastructure and regulatory shocks.
problem Understanding how cryptocurrency markets differentiate between infrastructure and regulatory shocks.
method Event-level block bootstrap inference on 31 cryptocurrency events across Bitcoin, Ethereum, Solana, and Cardano (2019-2025).
result No statistically significant difference in cumulative abnormal returns between infrastructure failures and regulatory enforcement.
Unified model explains market dynamics, linking order flow, volatility, and impact.
problem Understanding the dynamics of order flow, market impact, and volatility in financial markets.
method Proposes a microstructural model using Hawkes processes to distinguish core orders and reaction flow, and analyzes their scaling limits.
result Estimates the persistence parameter H0 and finds it consistent with market impact and volatility properties. Deep learning improves portfolio optimization in volatile markets.
problem Challenges in long-only, multi-asset strategies across market cycles.
method Training DL models with limited regime data using pre-training techniques and transformer architectures.
result Models show resilience and improved predictive accuracy in volatile markets.
Study benchmarks cryptocurrency risk using GBM, revealing Lognormal limitations.
problem Tackles limitations of Lognormal assumption in modeling cryptocurrency volatility and VaR.
method Applies Geometric Brownian Motion (GBM) with Maximum Likelihood Estimation and correlated Monte Carlo Simulation.
result Observed limitations of Lognormal assumption in cryptocurrency volatility and VaR calculations.
Study reveals how investor flows impact stock prices, especially during herding episodes.
problem Understanding how information transmits through prices and why it breaks down.
method Combining regularized deconvolution with Hawkes process analysis.
result Institutional price impact deteriorates sharply during herding episodes in small-cap stocks, while large-cap stocks maintain resilience.
Study shows priority access in ELA auctions is less valuable due to volatility risks.
problem Impact of volatility on time-based transaction ordering policies.
method Single-round model with risk-averse bidders, tested using ETH price data.
result Priority access is discounted due to volatility and risk aversion.
The study compares on-chain option prices with a model and finds significant differences.
problem Measuring and comparing on-chain option prices with a model-based benchmark.
method Used a two-regime MS-AR-(GJR)-GARCH model to estimate volatility and GLS to compare prices.
result On-chain option prices are significantly higher than model-based benchmarks, especially for call options.
CASP improves portfolio optimization by considering asset covariance.
problem Infeasibility in cardinality-constrained portfolio optimization.
method CASP uses volatility-normalized selection and covariance-aware projection.
result CASP-Basic delivers lower portfolio variance than standard Euclidean repair.
The study identifies key factors affecting cryptocurrency prices, including market beta, trading volume, and volatility.
problem Understanding the factors influencing cryptocurrency prices and their dynamics over time.
method ARDL technique and error-correction models applied to weekly data of Bitcoin, Ethereum, Dash, Litecoin, and Monero from 2010-2018.
result Cryptomarket-related factors are significant determinants of cryptocurrency prices in both short- and long-run, while attractiveness matters only in the long-run.
Paper proposes PMformer for better cryptocurrency price forecasting.
problem Huge volatility and trade-off between univariate and multivariate models.
method Partial-multivariate approach using PMformer.
result PMformer achieves significant statistical accuracy in forecasting.
Classical time series models forecast Bitcoin prices and volatility accurately.
problem Forecasting Bitcoin prices and volatility using classical models.
method ARIMA, SARIMA, GARCH, and EGARCH models were trained and tested on Bitcoin price data.
result ARIMA models performed best for short-term price dynamics, while EGARCH models were best for volatility.
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.
Study shows negative war news correlates with increased stock market volatility.
problem Understanding the impact of geopolitical events on financial markets.
method Used BERT model for sentiment analysis and GARCH model for volatility forecasting.
result Negative news sentiment during geopolitical crises is associated with increased stock market volatility.
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.
New framework predicts cryptocurrency trends by analyzing news and market data.
problem Cryptocurrency market volatility and news sensitivity challenges prediction accuracy.
method Multi-agent system with three innovations: news analysis, fusion mechanism, and coordination architecture.
result Statistically significant improvements over state-of-the-art methods.
A trading system uses LLMs to adapt to volatile crypto markets.
problem Volatility and market sentiment in cryptocurrencies make traditional models ineffective.
method Specialized LLM agents for technical analysis, sentiment evaluation, and decision-making; verbal feedback for continuous improvement.
result Agents outperform buy-and-hold strategy with consistent gains across market phases.
New method calibrates MQHawkes model using non-parametric approach, identifying cross-Hawkes and cross-leverage effects.
problem Calibrating complex Hawkes processes with non-parametric methods.
method Non-parametric calibration using General Method of Moments on coarse-grained MQHawkes model.
result Identification of cross-Hawkes and cross-leverage effects in futures markets.
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.
Study uses agent-based simulation to analyze impact of OBI strategy on financial markets.
problem Improving execution in markets with supply-demand imbalance.
method Built an execution algorithm that accounts for OBI, tested it in artificial markets.
result OBI strategy can improve execution, especially in volatile markets.
New machine learning model identifies key drivers of market troughs.
problem Misrepresentation of market trough drivers by simpler models.
method Flexible DML average partial effect causal machine learning framework.
result Volatility of options-implied risk appetite and market liquidity are key drivers.
Neural Lévy model improves risk and density forecasting for financial returns.
problem Financial returns exhibit heavy tails, volatility clustering, and jumps.
method Proposes a neural Lévy jump-diffusion framework that learns conditional drift, diffusion, jump intensity, and size distribution.
result Demonstrates improved calibration, sharper tail control, and risk reduction.
Detects jumps in financial asset prices with U-shape volatility.
problem Identifying jumps in financial asset prices with varying volatility.
method Threshold method applied to five-minute log-returns.
result Visualized jumps and volatility patterns for Apple Inc. (AAPL) stock.
Paper introduces probabilistic forecasting methods for cryptocurrency volatility.
problem Inadequate point forecasting methods for capturing full spectrum of volatility outcomes.
method Combines multiple base models (statistical and machine learning) to estimate conditional quantiles of cryptocurrency realized variance.
result QRS method outperforms sophisticated alternatives for Bitcoin volatility forecasting.
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.