TY  - GEN
AB  - We introduce generalized autoregressive positive-valued (GARP) processes, a class of autoregressive and moving-average processes that extends the class of existing autoregressive positive-valued (ARP) processes in one important dimension: each conditional moment dynamic is driven by a different and identifiable moving average of the variable of interest. The article provides ergodicity conditions for GARP processes and derives closed-form conditional and unconditional moments. The article also presents estimation and inference methods, illustrated by an application to European option pricing where the daily realized variance follows a GARP dynamic. Our results show that using GARP processes reduces pricing errors by substantially more than using ARP processes. <p><p>Supplemental material for peer-reviewed article published in the Journal of Business & Economic Statistics. Paper published online September 21, 2023.
AD  - Bank of Canada
AU  - Feunou, Bruno
DA  - 2023-09-21
DO  - 10.6084/m9.figshare.23744123.v2
DO  - DOI
ID  - 701
LA  - eng
N2  - We introduce generalized autoregressive positive-valued (GARP) processes, a class of autoregressive and moving-average processes that extends the class of existing autoregressive positive-valued (ARP) processes in one important dimension: each conditional moment dynamic is driven by a different and identifiable moving average of the variable of interest. The article provides ergodicity conditions for GARP processes and derives closed-form conditional and unconditional moments. The article also presents estimation and inference methods, illustrated by an application to European option pricing where the daily realized variance follows a GARP dynamic. Our results show that using GARP processes reduces pricing errors by substantially more than using ARP processes. <p><p>Supplemental material for peer-reviewed article published in the Journal of Business & Economic Statistics. Paper published online September 21, 2023.
PB  - Figshare
PY  - 2023-09-21
T1  - (Supplemental Material for) Generalized Autoregressive Positive-valued Processes
TI  - (Supplemental Material for) Generalized Autoregressive Positive-valued Processes
Y1  - 2023-09-21
ER  -