It is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average w...

In this paper, we have examined 4 models for Great Salt Lake level forecasting: ARMA (Auto-Regression and Moving Average), ARFIMA (Auto-Regressive Fractional Integral and Moving Average), GARCH (Generalized Auto-Regressive Conditional Heteroskedasticity) and FIGARCH (Fractional Integral Generalized Auto-Regressive Conditional Heteroskedasticity). Through our empirical data analysis where we div...

Artificial Neural Networks (ANNs) are very powerful tool in modern quantitative finance and have immerged as a powerful statistical modeling technology. This paper focuses on the problem of estimation of volatility of Indian Stock market. It begins with volatility calculation by Auto Regressive Conditional Heteroscedastic (ARCH), & Generalized Autoregressive Conditional Heteroscedasticity (GARC...

Indonesia's price index serves as a barometer for the nation's economic condition. One of Indonesia’s is Wholesale Price Index (WPI). WPI that tracks average change in wholesale prices over time. Time series analysis can be used forecasting because one time data. long memory, which condition data from different periods have high link despite being separated by large amount The Autoregressive Fr...

This paper investigates the relationship between inflation and growth uncertainty in Iran for the period of 1988-2008 by using quarterly data. We employ Generalized Autoregressive Conditional Heteroscedasticity in Mean (GARCH-M) model to estimate time-varying conditional residual variance of growth, as a standard measures of growth uncertainty. The empirical evidence shows that growth uncertain...

There is a compelling need to accurately and efficiently compute option values. Existing literature shows that models based on constant stock volatilities have been widely used in option valuation. However, stock volatilities change constantly in real life situations. The introduction of the Auto Regressive Conditional Heteroskedasticity (ARCH) model and subsequently, the Generalized Auto Regre...

a r t i c l e i n f o JEL classification: C32 C51 L94 Q40 Keywords: Wholesale spot electricity price markets Constant and dynamic conditional correlation Multivariate GARCH This paper examines the interrelationships of wholesale spot electricity prices among the four regional A multivariate generalised autoregressive conditional heteroscedasticity model with time-varying correlations. Dynamic c...

a for forecasting purposes arises from the fact that this conditional mean is allowed to be a random varible which depends on the available data, and evolves with time. The conditional variance, however, is r simply var [x e x ] = var [ε ] =σ , which remains constant regardless of the given data. Thus, the linea t t −1 t ε AR (1) model fails to adequately describe the conditional variance. In p...

It is a well-known fact that financial returns exhibit conditional heteroscedasticity and fat tails. While the GARCH-type models are very popular in depicting the conditional heteroscedasticity, the α-stable distribution is a natural candidate for the conditional distribution of financial returns. The α-stable distribution is a generalization of the normal distribution and is described by four ...