extr_mkt_events_basefc: Base forecasts for the extreme market events dataset

In bayesRecon: Probabilistic Reconciliation via Conditioning

Base forecasts for the extreme market events dataset

Description

Base forecasts for the extr_mkt_events dataset, computed using the model by Agosto, A. (2022). Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2139/ssrn.4119895")}.

Usage

extr_mkt_events_basefc

Format

A list extr_mkt_events_basefc containing

extr_mkt_events_basefc$mu

data frame of the base forecast means, for each day

extr_mkt_events_basefc$size

data frame of the static base forecast size parameters

Details

The predictive distribution for the bottom time series is a multivariate negative binomial with a static vector of dispersion parameters and a time-varying vector of location parameters following a score-driven dynamics. The base forecasts for the upper time series are computed using a univariate version of this model. They are in-sample forecasts: for each training instant, they are computed for time t+1 by conditioning on the counts observed up to time t.

Source

Agosto, A. (2022). Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2139/ssrn.4119895")}

References

Agosto, A. (2022). Multivariate Score-Driven Models for Count Time Series to Assess Financial Contagion. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2139/ssrn.4119895")}

Zambon, L., Agosto, A., Giudici, P., Corani, G. (2024). Properties of the reconciled distributions for Gaussian and count forecasts. International Journal of Forecasting (in press). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.ijforecast.2023.12.004")}.

bayesRecon documentation built on Sept. 11, 2024, 9:08 p.m.