Volatility measurement, forecasting, model evaluation, and financial risk.
Forecast disagreement is observable before a volatility forecast is evaluated, but it is not a sufficient model-selection rule. Across eight equity indices from 2006 to 2018, a frozen rule replaces XGBoost with HAR when disp…
Predictive accuracy and decision quality can rank volatility forecasts differently because portfolio maps are invariant to some forecast errors and sensitive to others. This paper formalizes decision equivalence: statistical…
Adaptive forecasting systems are usually evaluated by predictive loss. This paper asks a different question: what happens to collective model diversity when forecasting systems retain different amounts of historical informat…
Continual learning requires a choice about how quickly old information should lose influence. This paper studies the tail-risk consequences of that choice in adaptive volatility forecasting. Using daily historical replay for…
Volatility models are normally selected by likelihood, information criteria, residual diagnostics, and forecast loss. These criteria need not reveal whether a model reproduces the temporal direction of risk. We compare Gauss…
Machine-learning models often improve volatility forecasts, but it is unclear whether their predictive information can be translated into stable economic mechanisms. This paper treats that distinction as an empirical identif…
Applied volatility research routinely infers state dependence by estimating a model separately in subsamples defined by a conditioning variable and comparing slopes. This paper shows that in mixedfrequency volatility models …
Global financial stress can synchronize market movements while generating distinct loss distributions. We establish this separation using rolling empirical distributions for the S&P 500, FTSE 100, Nikkei 225 and Hang Seng fr…
We propose a Constraint Geometry State-Space Model (CGSSM) for dynamic correlation. The CGSSM replaces the correlation matrix Rt with an interpretable two-part latent state: a scalar market-wide synchronization level λt and …
Do exogenous climate regimes reorganize the dependence structure of commodity markets, or do they merely shift the general level of co-movement? We introduce the concept of Market Architecture — defined as the persistent top…
Where an uncertainty variable enters a volatility model determines the estimand, yet this distinction is often blurred. We combine a reproducible review of 36 GARCH-family applications with a controlled expanding-window demo…
This paper investigates whether global supply chain pressure exerts a measurable influence on the long-run volatility of industrial metal futures, and whether this influence is conditional on the geopolitical environment. Us…
This paper embeds the Oceanic Niño Index (ONI) into a two-stage DCC-MIDAS model to examine how ENSO drives long-run correlation between soybean futures and WTI crude oil over September 2000 to June 2026 (N = 6,465 daily obse…
Hybrid GARCH–deep learning models are often presented as a cumulative progression in which econometric structure and neural flexibility jointly improve volatility forecasts. This critical review asks a narrower question: wha…
This paper develops a unified methodological framework for studying cointegration, timevarying hedge ratios, and GARCH-type volatility dynamics in mean-reverting financial spreads. When asset prices are integrated of order o…
This paper examines how volatility model rankings depend on the choice of loss function when the true conditional variance is unobservable and forecast evaluation must rely on noisy volatility proxies. Using daily data for E…
The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) framework introduced by Bollerslev remains one of the standard tools for modeling conditional volatility in financial time series, value-at-risk measureme…