Similar Search Results

Sequential monitoring for changes from stationarity to mild non-stationarity

Horváth, Lajos; Liu, Zhenya; Rice, Gregory; Wang, Shixuan - In: Journal of econometrics 215 (2020) 1, pp. 209-238

Persistent link: https://www.econbiz.de/10012439449

Empirical process of the squared residuals of an ARCH sequence

Horváth, Lajos; Kokoszka, Piotr; Teyssière, Gilles - 1999

Persistent link: https://www.econbiz.de/10001424868

Large sample distribution of weighted sums of ARCH(p) squared residual correlations

Horváth, Lajos; Kokoszka, Piotr - In: Econometric theory 17 (2001) 2, pp. 283-295

Persistent link: https://www.econbiz.de/10001568398

Asymptotics for GARCH squared residual correlations

Berkes, István; Horváth, Lajos; Kokoszka, Piotr - In: Econometric theory 19 (2003) 4, pp. 515-540

Persistent link: https://www.econbiz.de/10001777176

Estimation of the maximal moment exponent of a GARCH (1,1) sequence

Berkes, István; Horváth, Lajos; Kokoszka, Piotr - In: Econometric theory 19 (2003) 4, pp. 565-586

Persistent link: https://www.econbiz.de/10001777182

Bootstrap misspecification tests for ARCH based on the empirical process of squared residuals

Horváth, Lajos; Kokoszka, Piotr; Tessière, Gilles - 2003

Persistent link: https://www.econbiz.de/10001790731

Change-point monitoring in linear models

Aue, Alexander; Horváth, Lajos; Hušková, Marie; … - In: The econometrics journal 9 (2006) 3, pp. 373-403

Persistent link: https://www.econbiz.de/10003390158

Sample and implied volatility in GARCH models

Horváth, Lajos; Kokoszka, Piotr; Zitikis, Ričardas - In: Journal of financial econometrics : official journal of … 4 (2006) 4, pp. 617-635

Persistent link: https://www.econbiz.de/10003565751

Sequential change-point detection in Garch (p,q) models

Berkes, István; Gombay, Edit; Horváth, Lajos; … - In: Econometric theory 20 (2004) 6, pp. 1140-1167

Persistent link: https://www.econbiz.de/10002424888

Empirical process of the squared residuals of an ARCH sequence

Horváth, Lajos; Kokoszka, Piotr; Teyssière, Gilles - 1999

We show that the empirical process of the squared residuals of an ARCH(p) sequence converges in distribution 1,0 a Gaussirm process B(F(t)) +t f(t) e, where F is the distribution function of the squared innovations, f its derivative, {B(tl, 0 <; t>1} a Brownian bridge and e a normal random variable....</;>

Persistent link: https://www.econbiz.de/10009582420