Testing for linear autoregressive dynamics under heteroskedasticity
نویسندگان
چکیده
One puzzling behavior of asset returns for various frequencies is the of ten observed positive autocorrelation at lag To some extent this can be explained by standard asset pricing models when assuming time varying risk premia However one often nds better results when directly tting an autoregressive model for which there is little economic foundation One may ask whether the underlying process does in fact contain an au toregressive component It is therefore of interest to have a statistical test at hand that performs well under the stylized facts of nancial returns In this paper we investigate empirical properties of competing devices to test for autoregressive dynamics in case of heteroskedastic errors For the volatility process we assume GARCH TGARCH and stochastic volatility The results indicate that standard QML inference for the autoregressive parameter is negatively a ected by misspeci cation of the volatility pro cess We show that bootstrapped versions of a likelihood ratio andWhite s t statistic have better size properties and comparable power properties Applied to German stock data the alternative tests in many cases yield very di erent p values
منابع مشابه
Pricing under Linear Autoregressive Dynamics , Heteroskedasticity , and Conditional
Daily returns of nancial assets are frequently found to exhibit positive autocor-relation at lag 1. When specifying a linear AR(1) conditional mean, one may ask how this predictability aaects option prices. We investigate the dependence of option prices on autoregressive dynamics under stylized facts of stock returns, i.e., conditional heteroskedasticity, leverage eeect, and conditional leptoku...
متن کاملTesting and modelling autoregressive conditional heteroskedasticity of streamflow processes
Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle’s Lagrange Multiplier test, clear evidences are found for t...
متن کاملTesting for Vector Autoregressive Dynamics under Heteroskedasticity
In this paper we introduce a bootstrap procedure to test parameter restrictions in vector autoregressive models which is robust in cases of conditionally heteroskedastic error terms. The adopted wild bootstrap method does not require any parametric specification of the volatility process and takes contemporaneous error correlation implicitly into account. Via a Monte Carlo investigation empiric...
متن کاملExponential Conditional Volatility Models
The asymptotic distribution of maximum likelihood estimators is derived for a class of exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models. The result carries over to models for duration and realised volatility that use an exponential link function. A key feature of the model formulation is that the dynamics are driven by the score. Keywords: Duration models; g...
متن کامل