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Persistent link: https://www.econbiz.de/10014159095
In this paper we focus on analyzing the predictive accuracy of three different types of forecasting techniques, Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Singular Spectral Analysis (SSA), used for predicting chaotic time series data. These techniques...
Persistent link: https://www.econbiz.de/10012947889
We elaborate economic explanations for the time-varying risk of month, quarter and year base load electricity forward contracts traded on the Nord Pool Energy Exchange from January 2006 to March 2010. Daily risk quantities are generated by decomposing realized volatility in its continuous and...
Persistent link: https://www.econbiz.de/10008989697
This paper adopts a new approach called DECO-FIAPARCH model for estimating the optimal hedge ratio (HR) in Turkish Stock Index Futures market in the presence of asymmetry and long memory. The study covers the period from May 3, 2005 until April 4, 2019, total of 3,508 daily observations. The...
Persistent link: https://www.econbiz.de/10012793517
The study proposes and a family of regime switching GARCH neural network models to model volatility. The proposed MS-ARMA-GARCH-NN models allow MS type regime switching in both the conditional mean and conditional variance for time series and further augmented with artificial neural networks to...
Persistent link: https://www.econbiz.de/10013090501
Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments implied-volatility information, derived from contemporaneously observed option prices or history-based volatility predictors, such as GARCH models, are investigated, to determine...
Persistent link: https://www.econbiz.de/10009767118
This paper considers spot variance path estimation from datasets of intraday high frequency asset prices in the … microstructure noise has an adverse effect on both spot variance estimation and jump detection. In our approach we can analyze high …
Persistent link: https://www.econbiz.de/10011379469
This paper analyses inflation forecasting power of artificial neural networks with alternative univariate time series models for Turkey. The forecasting accuracy of the models is compared in terms of both static and dynamic forecasts for the period between 1982:1 and 2009:12. We find that at...
Persistent link: https://www.econbiz.de/10009125642
In this paper we present the Radial Basis Neural Network Function. We examine some simple numerical examples of time-series in economics and finance. The forecasting performance is significant superior, especially in financial time-series, to traditional econometric modeling indicating that...
Persistent link: https://www.econbiz.de/10013138753
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