The paper explores how score-driven models can approximate rough volatility.
problem Modeling rough volatility with long memory structures.
method Extending score-driven models to include infinite-lag structures and heavy-tailed decay.
result Score-driven models converge to fractional Ornstein-Uhlenbeck processes under appropriate scaling.
New model predicts time-varying interactions in complex systems.
problem Challenges in modeling time-varying interactions in complex systems.
method Score-Driven Kinetic Ising Model (KIM) generalization.
result Model accurately predicts dynamics and separates different components.
Paper improves online time series forecasting by combining natural gradient and robust t-distribution.
problem Online time series forecasting challenges in rapidly adapting to evolving data.
method Reframed neural network optimization as a parameter filtering problem, using natural gradient and Student's t likelihood.
result Natural Score-driven Replay (NatSR) achieves stronger forecasting performance than state-of-the-art methods.
The analysis of the intraday dynamics of correlations among high-frequency returns is challenging due to the presence of asynchronous trading and market microstructure noise. Both effects may lead to significant data reduction and may severely underestimate correlations if traditional methods for low-frequency data are…
Paper proposes BOCPD for real-time order flow and market impact prediction.
problem Persistent order flow patterns in financial markets.
method Bayesian online change-point detection (BOCPD) with score-driven approach.
result Model outperforms existing models in predicting order flow and market impact.
GAS-Norm improves deep learning time series forecasting in non-stationary settings.
problem Deep learning models struggle with non-stationary time series data.
method Combines GAS model for adaptive normalization with deep neural networks.
result Improves deep learning performance in 21 out of 25 settings.
We propose a new class of models specifically tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting the recent advancements in Score Driven (SD) models typically used in time series eco…
A new model captures irregularly spaced high-frequency prices and their volatility.
problem Modeling high-frequency prices with irregular spacing and market noise.
method Observation-driven model using Skellam distribution with time-varying volatility and smoothing splines.
result The model provides a good fit to IBM stock data and measures daily realized volatility.
The Split-Session Cluster GARCH model captures tail heterogeneity in overnight and intraday returns.
problem Capturing tail behavior and dependence in multivariate asset returns.
method Convolution-t distributions, session and sector clustering, block-structured correlation matrices. result Session-specific and sector-level tail parameters improve model fit and out-of-sample performance.
Novel model captures high-dimensional copulas with spectral dynamics and regularization.
problem Modeling time-varying, asymmetric, tail-dependent copulas in high dimensions.
method Score-driven dynamics for eigenvalues, non-linear shrinkage for biases, parsimonious and scalable.
result Model outperforms recent alternatives in capturing co-movements and diversification potential.
Gradient filters track moving parameters under noisy data and misspecification.
problem Tracking multidimensional time-varying parameters under noisy observations and model misspecification.
method Gradient-based filters update parameters using the gradient of a postulated objective function, evaluated at either the predicted or updated parameters.
result Novel sufficient conditions for exponential stability of the filtered parameter path, and finite-sample and asymptotic mean squared error bounds.
SJDs unify masked, continuous, and hybrid diffusion models.
problem Unified modeling of diffusion processes.
method Continuous-time Markov processes with token embeddings and hazard rates.
result Unified model recovers masked, continuous, and hybrid diffusion as limits.