Local Identification of ARMAX Structures Subject to Nonlinear Constraints.
Long memory properties and covariance structure of the EGARCH model
The EGARCH model of Nelson [29] is one of the most successful ARCH models which may exhibit characteristic asymmetries of financial time series, as well as long memory. The paper studies the covariance structure and dependence properties of the EGARCH and some related stochastic volatility models. We show that the large time behavior of the covariance of powers of the (observed) ARCH process is determined by the behavior of the covariance of the (linear) log-volatility process; in particular, a...
Long memory properties and covariance structure of the EGARCH model
The EGARCH model of Nelson [29] is one of the most successful ARCH models which may exhibit characteristic asymmetries of financial time series, as well as long memory. The paper studies the covariance structure and dependence properties of the EGARCH and some related stochastic volatility models. We show that the large time behavior of the covariance of powers of the (observed) ARCH process is determined by the behavior of the covariance of the (linear) log-volatility process; in particular,...
Modelling financial time series using reflections of copulas
We have intensified studies of reflections of copulas (that we introduced recently in [6]) and found that their convex combinations exhibit potentially useful fitting properties for original copulas of the Normal, Frank, Clayton and Gumbel types. We show that these properties enable us to construct interesting models for the relations between investment in stocks and gold.
Modelling stock returns with AR-GARCH processes.
Financial returns are often modelled as autoregressive time series with random disturbances having conditional heteroscedastic variances, especially with GARCH type processes. GARCH processes have been intensely studied in financial and econometric literature as risk models of many financial time series. Analyzing two data sets of stock prices we try to fit AR(1) processes with GARCH or EGARCH errors to the log returns. Moreover, hyperbolic or generalized error distributions occur to be good models...
Modified neural network algorithms for predicting trading signals of stock market indices.
Multiscale behavior of a simple model for stock markets.
Multivariate nonlinear analysis and prediction of Shanghai stock market.
New Methods for Combining Forecasts
On estimating the yield and volatility curves
Pricing exotic options under a high-order Markovian regime switching model.
Propriétés du spectre évolutif d'un processus non stationnaire
Rate of convergence for a class of RCA estimators
This work deals with Random Coefficient Autoregressive models where the error process is a martingale difference sequence. A class of estimators of unknown parameter is employed. This class was originally proposed by Schick and it covers both least squares estimator and maximum likelihood estimator for instance. Asymptotic behavior of such estimators is explored, especially the rate of convergence to normal distribution is established.
Relationship Between Inflation and Inflation Uncertainty: the Case of Serbia
Seasonal time series with missing observations
Popular exponential smoothing methods dealt originally only with equally spaced observations. When time series contains gaps, smoothing constants have to be adjusted. Cipra et al., following Wright’s approach of irregularly spaced observations, have suggested ad hoc modification of smoothing constants for the Holt-Winters smoothing method. In this article the fact that the underlying model of the Holt-Winters method is a certain seasonal ARIMA is used. Minimum mean square error smoothing constants...
Semi-Chaotic Financial Series and Neural Network Forecasting: Evidence from European Stock Markets
Some applications of time series models to financial data
Stock price forecasting: Autoregressive modelling and fuzzy neural network.
Most models for the time series of stock prices have centered on autoregresive (AR) processes. Traditionaly, fundamental Box-Jenkins analysis [3] have been the mainstream methodology used to develop time series models. Next, we briefly describe the develop a classical AR model for stock price forecasting. Then a fuzzy regression model is then introduced. Following this description, an artificial fuzzy neural network based on B-spline member ship function is presented as an alternative to the stock...
Subordination, self-similarity, and option pricing.
The Box-Jenkins approach to time series analysis and forecasting : principles and applications