We review the notion of linearity of time series, and show that ARCH or stochastic volatility (SV) processes are not only non-linear: they are not even weakly linear, i.e., they do not even possess a martingale representation. Consequently, the use of Bartlett’s formula is unwarranted in the context of data typically modelled as ARCH or SV processes such as financial returns. More surprisingly, we show that even the squares of an ARCH or SV process are not weakly linear. Finally, we present an alternative to Bartlett’s formula that is applicable (and consistent) in the context of financial returns data
The autoregressive conditional heteroscedastic (ARCH) model and its extensions have been widely used...
This article shows that the relationship between kurtosis, persistence of shocks to volatility, and ...
We study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and ...
We consider a volatility model, named ARCH-NNH model, that is specifically an ARCH process with a no...
We consider ARCH processes with persistent covariates and provide asymptotic theories that explain h...
The ARCH process (R. F. Engle, 1982) constitutes a paradigmatic generator of stochastic time series ...
We consider ARCH processes with persistent covariates and provide asymptotic theories that explain h...
We investigate the time series properties of a volatility model, whose conditional variance is speci...
International audienceThe volatility modeling for autoregressive univariate time series is considere...
Many economic and financial time series have been found to exhibit dynamics in variance; that is, th...
This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical ...
The autocorrelations of log-squared, squared, and absolute financial returns are often used to infer...
This article considers the volatility modeling for autoregressive univariate time series. A benchmar...
This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical ...
We study the sample ACVF and ACF of a general stationary sequence under a weak mixing condition and ...
The autoregressive conditional heteroscedastic (ARCH) model and its extensions have been widely used...
This article shows that the relationship between kurtosis, persistence of shocks to volatility, and ...
We study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and ...
We consider a volatility model, named ARCH-NNH model, that is specifically an ARCH process with a no...
We consider ARCH processes with persistent covariates and provide asymptotic theories that explain h...
The ARCH process (R. F. Engle, 1982) constitutes a paradigmatic generator of stochastic time series ...
We consider ARCH processes with persistent covariates and provide asymptotic theories that explain h...
We investigate the time series properties of a volatility model, whose conditional variance is speci...
International audienceThe volatility modeling for autoregressive univariate time series is considere...
Many economic and financial time series have been found to exhibit dynamics in variance; that is, th...
This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical ...
The autocorrelations of log-squared, squared, and absolute financial returns are often used to infer...
This article considers the volatility modeling for autoregressive univariate time series. A benchmar...
This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical ...
We study the sample ACVF and ACF of a general stationary sequence under a weak mixing condition and ...
The autoregressive conditional heteroscedastic (ARCH) model and its extensions have been widely used...
This article shows that the relationship between kurtosis, persistence of shocks to volatility, and ...
We study autodependence in ARCH-models by computing the auto-lower tail dependence coefficients and ...