Arbeitspapier

Modeling autoregressive conditional skewness and kurtosis with multi-quantile CAViaR

Engle and Manganelli (2004) propose CAViaR, a class of models suitable for estimating conditional quantiles in dynamic settings. Engle and Manganelli apply their approach to the estimation of Value at Risk, but this is only one of many possible applications. Here we extend CAViaR models to permit joint modeling of multiple quantiles, Multi-Quantile (MQ) CAViaR. We apply our new methods to estimate measures of conditional skewness and kurtosis defined in terms of conditional quantiles, analogous to the unconditional quantile-based measures of skewness and kurtosis studied by Kim and White (2004). We investigate the performance of our methods by simulation, and we apply MQ-CAViaR to study conditional skewness and kurtosis of S&P 500 daily returns.

Sprache
Englisch

Erschienen in
Series: ECB Working Paper ; No. 957

Klassifikation
Wirtschaft
Estimation: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Thema
Asset returns
CAViaR
conditional quantiles
Dynamic quantiles
Kurtosis
Skewness
Kapitaleinkommen
Theorie

Ereignis
Geistige Schöpfung
(wer)
White, Halbert
Kim, Tae-Hwan
Manganelli, Simone
Ereignis
Veröffentlichung
(wer)
European Central Bank (ECB)
(wo)
Frankfurt a. M.
(wann)
2008

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • White, Halbert
  • Kim, Tae-Hwan
  • Manganelli, Simone
  • European Central Bank (ECB)

Entstanden

  • 2008

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