Similar Search Results

MATLAB Applications of Trading Rules and GARCH with Wavelets Analysis

Giovanis, Eleftherios - 2009

In this paper we provide MATLAB routines for two major used trading rules, the moving average indicator and MACD oscillator as also the GARCH univariate regression with Monte Carlo simulations and wavelets decomposition, which is an update of an older algorithm

Persistent link: https://www.econbiz.de/10013153142

Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series

Koopman, Siem Jan; Bos, Charles S. - 2003

The linear Gaussian state space model for which the common variance is treated as a stochastic time-varying variable is considered for the modelling of economic time series. The focus of this paper is on the simultaneous estimation of parameters related to the stochastic processes of the mean...

Persistent link: https://www.econbiz.de/10014100716

Markov-Chain Approximation and Estimation of Nonlinear, Non-Gaussian State Space Models

Farmer, Leland - 2014

I develop a new method for approximating and estimating nonlinear, non-Gaussian state space models. I show that any such model can be well approximated by a discrete-state Markov process and estimated using techniques developed in Hamilton (1989). Through Monte Carlo simulations, I demonstrate...

Persistent link: https://www.econbiz.de/10013048908

Time series models with a common stochastic variance for analysing economic time series

Koopman, Siem Jan; Bos, Charles S. - 2002

The linear Gaussian state space model for which the common variance istreated as a stochastic time-varying variable is considered for themodelling of economic time series. The focus of this paper is on thesimultaneous estimation of parameters related to the stochasticprocesses of the mean part...

Persistent link: https://www.econbiz.de/10011327834

Inference for adaptive time series models : stochastic volatility and conditionally Gaussian state space form

Bos, Charles S.; Shephard, Neil G. - 2004

volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are …

Persistent link: https://www.econbiz.de/10011334849

Are long-run output growth rates falling?

Li, Mengheng; Mendieta-Muñoz, Ivan - 2018

-varying parameter models that incorporate both stochastic volatility and a Heckman-type two-step estimation procedure that deals with …

Persistent link: https://www.econbiz.de/10011823990

On importance sampling for state space models

Jungbacker, Borus; Koopman, Siem Jan - 2005

is given for the stochastic volatility model with leverage. …

Persistent link: https://www.econbiz.de/10011348357

Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models

Barra, Istvan - 2016

evidence for stochastic intensity and stochastic volatility models based on Ornstein-Uhlenbeck processes. For our empirical …

Persistent link: https://www.econbiz.de/10013005987

Multiple-Days-Ahead Value-At-Risk and Expected Shortfall Forecasting for Stock Indices, Commodities and Exchange Rates : Inter-Day Versus Intra-Day Data

Degiannakis, Stavros Antonios - 2018

-day and intra-day volatility models by estimating the AR(1)-GARCH(1,1)-skT and the AR(1)-HAR-RV-skT frameworks, respectively … intra-day volatility model is not as appropriate as it was expected to be for each of the different asset classes; stock … performance of the inter-day and intra-day volatility models across various markets. The inter-day specification predicts and …

Persistent link: https://www.econbiz.de/10012910113

Forecasting Value-at-Risk and Expected Shortfall Using Fractionally Integrated Models of Conditional Volatility : International Evidence

Degiannakis, Stavros Antonios - 2018

forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve …

Persistent link: https://www.econbiz.de/10012910119