Framework improves ETF volatility forecasting by adapting to market conditions.
problem Challenges in volatility forecasting due to shifting market conditions and varying model performance.
method Risk-sensitive specialist routing using online risk-sensitive evaluation and state-dependent gating.
result Reduces forecast loss by 24% and underprediction loss by 22% compared to rolling-best baseline.
Graph neural networks improve volatility forecasting by capturing spillover effects.
problem Forecasting multivariate realized volatility with spillover effects.
method Customized graph neural networks incorporating spillover effects from multi-hop neighbors.
result Modeling nonlinear spillover effects enhances forecasting accuracy, especially for short-term horizons.
The study compares econometric and deep learning models for forecasting COMEX copper futures volatility.
problem Forecasting volatility of COMEX copper futures across different time intervals.
method Econometric models (GARCH, HAR) and deep learning models (RNN, LSTM, GRU) applied to daily and hourly data.
result Deep learning models outperform econometric models in hourly data, but HAR remains the best overall for daily data.
Extends return extrapolation to nonlinear, asymmetric functions under stochastic volatility.
problem Behavioral anomalies in portfolio choice under stochastic volatility.
method Smooth, nonlinear, asymmetric extrapolation function; CRRA investor; Heston stochastic volatility; Hamilton-Jacobi-Bellman equation; Numerical solutions (finite-difference ADI, deep learning-driven iterative).
result Saturation acts as an endogenous correction mechanism, reducing welfare loss.
We consider an SPDE description of a large portfolio limit model where the underlying asset prices evolve according to certain stochastic volatility models with default upon hitting a lower barrier. The asset prices and their volatilities are correlated via systemic Brownian motions, and the resulting SPDE is defined o…
Extended LSTMs improve volatility prediction by 20%.
problem Predicting asset price volatility with long memory.
method Extended LSTMs with multiple flexible timescales.
result Extended LSTMs outperform rough volatility predictions by 20%.
A new unsupervised learning method calibrates rough volatility models efficiently.
problem Efficient calibration of rough volatility models with minimal data.
method Unsupervised learning using BSDE representation and neural networks.
result The proposed scheme minimizes loss and approximates BSDE solution.
Study on large portfolio losses with correlated volatility processes converging to a stochastic PDE.
problem Large portfolio losses with correlated volatility processes.
method Structural stochastic volatility model, mean-reverting diffusions, stochastic initial-boundary value problem.
result Convergence of empirical measure process to a stochastic PDE solution under certain conditions.
Modified Jones-Faddy skew t-distribution captures asymmetry in stock returns.
problem Negative skew and positive mean in stock returns due to broken symmetry of stochastic volatility.
method Modified Jones-Faddy skew t-distribution applied to split gains and losses, using stochastic differential equations for stock returns and volatility.
result The modified distribution effectively captures the asymmetry in daily S&P500 returns, including its tails.
In this paper we consider Fourier transform techniques to efficiently compute the Value-at-Risk and the Conditional Value-at-Risk of an arbitrary loss random variable, characterized by having a computable generalized characteristic function. We exploit the property of these risk measures of being the solution of an ele…
Investors face constraints in Heston's model; optimal allocation differs from naive capped strategy.
problem Optimizing portfolio allocation with convex constraints in Heston's stochastic volatility model.
method Applied duality methods to derive a closed-form solution.
result The optimal constrained portfolio allocation differs from the naive capped portfolio, leading to different wealth outcomes.
We extend return extrapolation to incorporate asymmetry and saturation, finding that asymmetric nonlinear extrapolation leads to lower welfare loss.
problem Optimal portfolio choice under stochastic volatility
method Smooth, nonlinear extrapolation function with sentiment and variance hedging
result Lower welfare loss with asymmetric nonlinear extrapolation
Introduces σ-Cell for improved financial volatility forecasting.
problem Improving volatility forecasting in financial markets.
method Combines GARCH and deep learning, incorporating stochastic layers and time-varying parameters.
result Demonstrates superior forecasting accuracy compared to traditional models.
Study tail risk in high-frequency finance using L1-regularized regression.
problem Measuring tail risk dynamics in high-frequency financial markets.
method Dynamic extreme value regression model with L1-regularized maximum likelihood estimator. result Severity of extreme losses well predicted by low price impact in high volatility periods.
A new AMM design reduces impermanent loss and retains more liquidity.
problem Inefficiencies in conventional AMM designs lead to liquidity loss and user engagement issues in DEXs.
method Proposes a dual-mechanism framework: a power-law invariant BMM and dynamic rebate system.
result Reduces impermanent loss by 36% and retains 3.98x more liquidity during price volatility.
Blockchain-based exchanges adopt based on token pair volatility and personal use.
problem Token value loss and arbitrage issues in decentralized exchanges.
method Investigation of Automated Market Makers (AMMs) using transaction-level data.
result AMMs are adopted for high personal use or highly correlated token price movements.
Proposes overnight volatility model for better market dynamics.
problem Lack of high-frequency data during close-to-open period.
method Itô diffusion model with weighted least squares estimation.
result Developed and validated overnight volatility model.
Analyzes robust portfolio optimization with multi-factor stochastic volatility.
problem Optimizing portfolios under uncertainty and volatility risks.
method Analytical derivation of optimal strategy under worst-case scenarios, comparison with strategies ignoring uncertainty, and numerical experiments.
result Effects of ambiguity and derivative trading on optimal portfolio selection.
This paper discusses a novel explanation for asymmetric volatility based on the anchoring behavioral pattern. Anchoring as a heuristic bias causes investors focusing on recent price changes and price levels, which two lead to a belief in continuing trend and mean-reversion respectively. The empirical results support ou…
Optimizes trading strategies with price impact, predictable returns, and stochastic volatility.
problem Dynamic portfolio optimization under complex market conditions.
method Multi-scale volatility expansion, singular and regular perturbations, asymptotic approximations.
result Improved portfolio strategy with reduced profit and loss (PnL) through corrections for small price impact.
In practice daily volatility of portfolio returns is transformed to longer holding periods by multiplying by the square-root of time which assumes that returns are not serially correlated. Under this assumption this procedure of scaling can also be applied to contributions to volatility of the assets in the portfolio. …
The paper compares three option pricing models with varying volatility dynamics.
problem Comparing the accuracy and efficiency of different option pricing models with changing volatility.
method Used stochastic volatility models including Heston and MSV, and compared them with existing models on 15 index option datasets.
result Stochastic volatility models achieve comparable accuracy to existing models and are faster to calibrate.
Develops a deep learning method for enforcing no-arbitrage in local volatility surfaces.
problem No-arbitrage conditions not enforced in deep learning approaches for local volatility.
method Jointly interpolates European vanilla option prices, enforcing no-arbitrage through modified loss functions or network architectures.
result Demonstrates the effectiveness of enforcing no-arbitrage in local volatility surfaces using deep learning.
Previous research has shown that for stock indices, the most likely time until a return of a particular size has been observed is longer for gains than for losses. We establish that this so-called gain/loss asymmetry is present also for individual stocks and show that the phenomenon is closely linked to the well-known …
Leveraged ETFs can outperform their targets in certain market conditions, contrary to the volatility drag hypothesis.
problem The long-term performance decay of leveraged ETFs due to volatility drag.
method Unified framework incorporating AR(1) and AR-GARCH models, continuous-time regime switching, and flexible rebalancing frequencies.
result Return dynamics, including return autocorrelation, volatility clustering, and regime persistence, determine LETF performance.
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.
The paper studies efficient simulation methods for financial firm values under fast mean-reverting volatility.
problem Estimating the probability of firm default under fast mean-reverting stochastic volatility models.
method Approximations using ergodic averages and central limit theorem corrections for efficient simulation.
result Accuracy of approximations assessed through numerical simulation and payoff function estimation.
This study interprets AMM fees as implied volatility, validating their relevance in digital asset markets.
problem Understanding the volatility of fees in decentralized exchange systems.
method Reinterpreting AMM fees as implied volatility and applying fixed-for-floating swaps to quote and validate these volatilities.
result The implied volatilities of digital assets can be accurately quoted using AMM fees, validating the approach.
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.
A new method simulates implied volatility surfaces for multiple assets.
problem Generating consistent market scenarios for multiple asset implied volatilities.
method Combining functional data analysis and neural SDEs with a penalty for model misspecification.
result Simulated market scenarios are consistent with historical features and lie within the sub-manifold of essentially free static arbitrage.
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.
Generative diffusion models forecast implied vol surfaces without arbitrage issues.
problem Forecasting arbitrage-free implied volatility surfaces using historical data with path-dependent dynamics.
method Generative diffusion model (DDPM) with conditional training on market variables, including EWMAs and returns. Dynamic penalty scheme based on SNR to enforce arbitrage-free surfaces.
result Superior performance in volatility forecasting compared to existing methods.
Study reveals latent state computation in stochastic volatility models.
problem Understanding latent stochastic dynamics in noisy, partially observed observations.
method Multivariate stochastic volatility setting, controlled experiments on various architectures.
result Evidence of a two-stage computation: latent state encoding and output head mapping.
Neural network approach to smooth implied volatility surfaces.
problem Generating realistic arbitrage-free option prices from sparse or erroneous data.
method Soft constraints to ensure arbitrage-free pricing, combined with standard IVS models.
result Deep neural networks improve prediction accuracy over shallow ones.
Bayesian GPR model predicts extreme stock market losses.
problem Forecasting rare but impactful extreme negative returns in equity markets.
method Developed a Bayesian Generalised Pareto Regression model linking scale parameter to market volatility.
result The Cauchy prior provides the best balance between predictive accuracy and model simplicity.
Paper calculates greeks for DeFi LPs and introduces Impermanent Gain.
problem Liquidity Providers in DeFi are exposed to Impermanent Loss.
method Tailored Black & Scholes formulas for DeFi markets.
result Introduced Impermanent Gain for risk management.
Paper proposes a hybrid model for VaR forecasting using SVR, GARCH, and KDE.
problem Inaccurate VaR estimates due to time-varying volatility and distributional characteristics.
method SVR-GARCH-KDE hybrid model combining nonlinear and nonparametric approaches.
result The SVR-GARCH-KDE hybrid outperforms benchmark models in VaR forecasting, especially for longer horizons.
In this paper, we study an optimal excess-of-loss reinsurance and investment problem for an insurer in defaultable market. The insurer can buy reinsurance and invest in the following securities: a bank account, a risky asset with stochastic volatility and a defaultable corporate bond. We discuss the optimal investment …
Study improves risk management for volatile markets using expectiles.
problem Limitations of traditional risk measures during market stress.
method Develops expectile-based framework for FTSE 100 index.
result Expectile-based Value-at-Risk (EVaR) outperforms traditional VaR measures.
The paper develops a new framework for pricing and hedging liquidity in crypto markets.
problem Arbitrage and risk management in crypto market making.
method Developed a new mathematical framework using a coordinate system defined by price and intrinsic liquidity.
result Established a linear dependence of asset reserves and value functions on intrinsic liquidity, facilitating arbitrage-free pricing and delta hedging.
Optimal portfolios are formed by combining momentum, size, and volatility characteristics, enhancing utility for all investors.
problem Estimation error in forming optimal portfolios from characteristics.
method Maximizing an in-sample loss function that is more concave than the utility function, linking weights to characteristics.
result Optimal portfolios with significantly higher certainty equivalents than benchmarks for all investors.
New algorithm calibrates local volatility from option prices using deep neural networks.
problem Calibrating local volatility from market option prices with reduced interpolation and reprice errors.
method Deep self-consistent learning using neural networks to approximate both option prices and local volatility.
result Improved performance in terms of reduced interpolation and reprice errors compared to existing methods.
Different optimizer choices lead to different financial model predictions.
problem The impact of optimizer choice on neural network models in financial time series.
method Analysis of large-scale volatility forecasting for S&P 500 stocks using various model-training-pipeline pairs.
result Optimizer choice reshapes non-linear response profiles and temporal dependence in financial models, leading to different functional outcomes.
DCNN improves volatility smile and skewness calibration without arbitrage constraints.
problem Calibrating volatility smile and skewness surfaces with no arbitrage constraints.
method Derivative-Constrained Neural Network (DCNN) incorporating derivatives in the loss function.
result DCNN generates a smooth surface that satisfies no-arbitrage conditions.
Study analyzes how COVID-19 impacts crypto and stock market volatility.
problem Impact of COVID-19 on cryptocurrency and stock market volatility.
method Two-stage multivariate EGARCH model with DCC approach, VaR and CFVaR.
result Significant spillover effects and conditional volatility surges after shocks.
Paper proposes Multi-Transformer for more accurate stock volatility forecasts.
problem Accurate equity risk models needed for effective risk management.
method Introduces Multi-Transformer neural network architecture, adapted from Transformer models.
result Empirical results show Multi-Transformer leads to more accurate risk measures.
The paper examines statistical properties of IL and LVR in automated market makers.
problem Assessing the performance of automated market makers and their profitability.
method Analysis of random walk properties and statistical integral combined with CFMM mechanics.
result IL and LVR have identical expectation values but different distribution functions for Brownian motion.
Study shows market volatility affects optimal communication design for trading strategies.
problem Investigating how communication impacts trading strategy performance in multi-agent systems.
method 5-agent LLM-based trading systems across 450 experiments spanning 21 months, comparing 5 organizational structures.
result Communication improves performance but depends on market characteristics, with competitive conversation excelling in volatile tech stocks.