Abstracts

Mathematics

Search abstracts:

Performance of GARCH models in forecasting stock market volatility

Article Abstract:

A study has been conducted to examine the performance of the generalized autoregressive conditional heteroscedasticity (GARCH) model and its modifications using the rate of returns from the daily stock market indices of the Kuala Lumpur Stock Exchange. Findings have revealed the presence of weaknesses of imposing the parameter estimates of the GARCH model to certain constraints, such as stationary or non-negativity. When compared to the GARCH model from the aspect of restriction, the exponential GARCH model has been observed to have no such restriction on the parameters.

Research, Economic forecasting, Stock price forecasting

---

Forecasting interest rate swap spreads using domestic and international risk factors: evidence from linear and non-linear models

Article Abstract:

The ability of factor models to predict the dynamics of US and British interest rate swap spreads is studied. Evidence suggests that non-linear models have better forecasting ability than linear ones. Performances of the smooth transition vector autoregressive and nearest-neighbors models are also studied.

United Kingdom, United States, Forecasting, Interest rate swaps, Report

---

Similar abstracts:

• Abstracts: Forecasting stock market volatility using (non-linear) Garch models. Do seasonal unit roots matter for forecasting monthly industrial production?
• Abstracts: Evaluation of correlation forecasting models for risk management. A Bayesian nonlinear support vector machine error correction model
• Abstracts: Preferences over solutions to the bargaining problem. Bayesian vector autoregressions with stochastic volatility
• Abstracts: Asymptotic theory of integrated conditional moment tests. Robust Wald tests in SUR systems with adding-up restrictions
• Abstracts: When is rational behavior consistent with rules of thumb?: a link between evolutionary terminology and neoclassical methodology

This website is not affiliated with document authors or copyright owners. This page is provided for informational purposes only. Unintentional errors are possible.