Showing 1 - 10 of 13,232
This study investigates the practical importance of several VaR modeling and forecasting issues in the context of intraday stock returns. Value-at-Risk (VaR) predictions obtained from daily GARCH models extended with additional information such as the realized volatility and squared overnight...
Persistent link: https://www.econbiz.de/10013105936
Persistent link: https://www.econbiz.de/10010233866
Persistent link: https://www.econbiz.de/10009736952
Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns Abstract: Motivated by the recurrent Neural Networks, this paper proposes a recurrent Support Vector Regression (SVR) procedure to forecast nonlinear ARMA model based simulated data...
Persistent link: https://www.econbiz.de/10003770766
Persistent link: https://www.econbiz.de/10011474104
The study reports empirical evidence that artificial neural network based models are applicable to forecasting of stock market returns. The Nigerian stock market logarithmic returns time series was tested for the presence of memory using the Hurst coefficient before the models were trained. The...
Persistent link: https://www.econbiz.de/10011488820
Persistent link: https://www.econbiz.de/10011311313
The increasing availability of financial market data at intraday frequencies has not only led to the development of improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source for longer horizon volatility forecasts. In this paper...
Persistent link: https://www.econbiz.de/10011326944
The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility, which accommodates level shifts, day-of-the-week...
Persistent link: https://www.econbiz.de/10011335205
Goal of this paper is to analyze and forecast realized volatility through nonlinear and highly persistent dynamics. In particular, we propose a model that simultaneously captures long memory and nonlinearities in which level and persistence shift through a Markov switching dynamics. We consider...
Persistent link: https://www.econbiz.de/10011739849