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...

The purpose of this study is to concentrate on the investigation of days-of-week effect on Tehran Stock Exchange and its comparison with other emerging markets. Using Classical Linear Regression (CLR) as well as Autoregressive Conditional Heteroskedasticity (ARCH) models it in indicated has indicated that there is significantly positive total return on Saturdays and significantly negative total...

We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored...

Autoregressive conditional heteroskedasticity (ARCH) models pioneered by Engle (1982) and their extended version have been proven to be very successful in modeling the volatility of financial time series; see Bollerslev et al. (1994). Bayesian inference on ARCH models has been implemented using the importance sampling technique proposed by Geweke (1989) and more recently using Markov chain Mont...

Saham merupakan produk pasar modal yang menjadi salah satu instrumen investasi. Banyak investor memilih saham sebagai investasi dikarenakan memberikan keuntungan menarik. Metode estimasi metode tepat bagi para untuk memprediksi harga sehingga dapat membantu mengoptimalkan keuntungannya. Penelitian ini bertujuan menentukan model terbaik dari data menggunakan ARCH-GARCH dan mendapatkan hasil Kalm...

A new variant of the ARCH class of models for forecasting conditional variance, to be called the Generalized AutoRegressive Conditional Heteroskedasticity Parkinson Range (GARCH-PARK-R) Model, is proposed. The GARCH-PARK-R model, utilizing the extreme values, is a good alternative to the Realized Volatility that requires a large amount of intra-daily data, which remain relatively costly and are...

the purpose of this study is to concentrate on the investigation of days-of-week effect on tehran stock exchange and its comparison with other emerging markets. using classical linear regression (clr) as well as autoregressive conditional heteroskedasticity (arch) models it in indicated has indicated that there is significantly positive total return on saturdays and significantly negative total...

We consider maximum likelihood estimation of a particular noninvertible ARMA model with autoregressive conditionally heteroskedastic (ARCH) errors. The model can be seen as an extension to so-called all-pass models in that it allows for autocorrelation and for more flexible forms of conditional heteroskedasticity. These features may be attractive especially in economic and financial application...