Original scientific paper As the stock market volatility is highly nonlinear, coupling and time varying, it is difficult to predict by the traditional forecasting methods. For explaining the existing problems of the current volatility forecasting method, we use the model based on the weighted least squares support vector regression (WLSSVR) method to predict the stock index volatility in this p...

This paper makes a comparison of global, feedback and smoothed-piecewise neural prediction models for financial time series (FTS) prediction problem. Each model is implemented by various neural network (NN) architectures: global model by a multilayer perceptron (MLP), feedback model by a recurrent neural network (RNN) and smoothed-piecewise model by a mixture of experts (MoE) structure. The adv...

Recent research suggests that long memory can be caused by regime switching and is easily confused with it. However, if the causes of confusion were properly controlled, they could distinguished. Motivated this idea, our study aims to distinguish between financial volatility. We firstly modeled volatility using Fractionally Integrated Exponential GARCH (FIEGARCH) Markov Regime-Switching EGARCH ...

the present research aims to evaluate impacts of crude oil price return index, bloomberg petroleum index and bloomberg energy index on stock market returns of 121 companies listed in tehran stock exchange in a 10 years' period from early 2006 to april 2016. first, explanatory variables were aligned with petroleum products index mostly due to application of dollar data. subsequently, to che...

This paper investigates the dynamic relationship between volatility, volume and open interest in CSI 300 futures market using asymmetric GARCH model, Granger causality test, variance decomposition and impulse response function based on 1-min data. ARMA-EGARCH model is employed and find that both contemporaneous and lagged volume is positively related to volatility, and current open interest has...

This paper provides a theoretical explanation for the heteroscedasticity of asset returns. In line with existing empirical results, our model yields an asymmetric relationship between stock return and volatility. Based on the simple assumptions that investors behave according to Prospect Theory and are subject to mental accounting in a dynamic setting, we analytically derive the unit-root versi...

Volatility of a stock may incur a risk premium, leading to a positive correlation between volatility and returns. On the other hand the leverage effect, whereby negative returns increase volatility, acts in the opposite direction. We propose a reformulation and extension of the ARCH in Mean model, in which the logarithm of scale is driven by the score of the conditional distribution. This EGARC...

In this paper, we investigate the long run dynamics of the intraday range of the GBP/USD, JPY/USD and CHF/USD exchange rates. We use a non-parametric filter to extract the low frequency component of the intraday range, and model the cyclical deviation of the range from the long run trend as a stationary autoregressive process. We find that the long run trend is time-varying but highly persisten...