.png){kind=icon}
Return
A multivariate GARCH-jump mixture model
.png){kind=icon}
OA
AI
DOI：10.1002/for.3019.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a new parsimonious multivariate GARCH-jump (MGARCH-jump) mixture model with multivariate jumps that allows both jump sizes and jump arrivals to be correlated among assets. Dependent jumps impact the conditional moments of returns and beta dynamics of a stock. Applied to daily stock returns, the model identifies co-jumps well and shows that both jump arrivals and jump sizes are highly correlated. The jump model has better out-of-sample forecasts compared with a benchmark multivariate GARCH model.
Keywords:
beta dynamics
co-jump
jumps
multinomial
multivariate GARCH
value at risk
Journal
IF:
2.7
Papers:
2.3K
Citations:
3.0K
Organization
Cited Papers
Fibre-rich Foods to Treat Obesity and Prevent Colon Cancer trial study protocol: a randomised clinical trial of fibre-rich legumes targeting the gut microbiome, metabolome and gut transit time of overweight and obese patients with a history of noncancerous adenomatous polyps.png)
BMJ OpenOAAI
BMJ OpenIF0
OAAIShareSaveShareSaveEthanol-induced developmental neurodegeneration in secretin receptor-deficient miceNeuroReport
NeuroReportIF0
PREAI
ShareSaveShareSave
ShareSaveShareSave
View more