Modelling financial time series with SEMIFAR GARCH model

Modelling financial time series with SEMIFAR-GARCH model

A class of semiparametric fractional autoregressive GARCH models (SEMIFARGARCH), which includes deterministic trends, difference stationarity and stationarity with shortand long-range dependence, and heteroskedastic model errors, is very powerful for modelling financial time series. This paper discusses the model fitting, including an efficient algorithm and parameter estimation of GARCH error ...

Volatility Forecasts in Financial Time Series with HMM-GARCH Models

Nowadays many researchers use GARCH models to generate volatility forecasts. However, it is well known that volatility persistence, as indicated by the sum of the two parameters G1 and A1[1], in GARCH models is usually too high. Since volatility forecasts in GARCH models are based on these two parameters, this may lead to poor volatility forecasts. It has long been argued that this high persist...

Modelling Financial Time Series with S-PLUS

Volatility Modelling of Multivariate Financial Time Series by Using ICA-GARCH Models

Volatility modelling of asset returns is an important aspect for many financial applications, e.g., option pricing and risk management. GARCH models are usually used to model the volatility processes of financial time series. However, multivariate GARCH modelling of volatilities is still a challenge due to the complexity of parameters estimation. To solve this problem, we suggest using Independ...

Stochastic interest rate modelling with continuous-time GARCH(1,1) model

There is a massive amount of study deals with the stochastic modelling of the interest rates. The first approach to specify the interest rate movements as continuoustime Ito process was introduced by Merton in 1973. But his approach had many shortcomings including possibility of negative interest rates. In his seminal work in 1977, Vasicek developed Merton’s model by introducing mean-reverting ...