This paper analyzes possibly time-varying shock transmission in structural vector autoregressive (VAR) models when the reduced-form VAR coefficients are time-invariant and the shocks are identified through non-Gaussianity. To check for possible time-variation in the impulse responses, we propose...

This paper revisits the topic of time-scale parameterizations of the Heston-Nandi GARCH (1,1) model to create a new, theoretically valid setting compatible with real financial data. We first estimate parameters using three US market indices and six frequencies to let data reveal the correct,...

This paper examines the use of machine learning methods in modeling and forecasting time series with long memory through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM model outperforms traditional approaches in forecasting...

Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital...