ARCHGARCHmalleilla
ARCHGARCHmalleilla is a class of econometric models designed to describe time-varying volatility in financial returns by extending ARCH and GARCH frameworks with a path-dependent component inspired by Malliavin calculus. The model aims to capture how past shocks influence not only the current conditional variance but also the sensitivity of volatility to the entire shock history, allowing for richer dynamics such as long memory and nonlinear leverage effects. It is proposed to improve volatility forecasting and risk assessment in markets characterized by heavy tails and regime-like behavior.
The term ARCHGARCHmalleilla emerged in the econometric literature in the early 2020s, attributed to researchers seeking
Estimation relies on quasi-maximum likelihood or Bayesian procedures, often requiring computational techniques such as particle filtering
Applications include financial return forecasting, value-at-risk, and option pricing where standard ARCH/GARCH models may struggle to