The presence of volatility in residential property market prices helps investors generate substantial profit while also causing fear among investors since high volatility implies a high return with a high risk. In a financial time series, volatility refers to the degree to which the residential property market price increases or decreases during a particular period. The present study aims to forecast the volatility returns of real residential property prices (RRPP) in Malaysia using three different families of generalized autoregressive conditional heteroskedasticity (GARCH) models. The study compared the standard GARCH, EGARCH, and GJR-GARCH models to determine which model offers a better volatility forecasting ability. The results reveale...
Engle (1982) introduced the autoregressive conditionally heteroskedastic model for quantifying the c...
This paper compares and evaluates various generalized autoregressive conditional heteroscedastic (GA...
This paper compares and estimates standard and asymmetric GARCH models with daily returns data of th...
Market properties and shares are important in the field of finance in order to measure the economic ...
Reliable and accurate forecasts can provide important input for fund manager and policymakers to m...
A new variant of the ARCH class of models for forecasting the conditional variance, to be called the...
It is well-known that financial time series exhibits changing variance and this can have important c...
Along with the large number of investors transacting on Islamic stocks, the movement of stock prices...
This paper aims to investigate the effectiveness of four volatility forecasting models, i.e. Exponen...
This paper examines the dynamics of return and dynamic volatility across the Malaysian and pan-Asian...
This paper applies the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to t...
The purpose of this study is to examine the house price volatility in three urban areas in Malaysia....
This paper examines and estimate the three GARCH(1,1) models (GARCH, EGARCH and GJR-GARCH) using the...
A new variant of the ARCH class of models for forecasting the conditional variance, to be called the...
The current housing market in the United States has never been more unstable. Since 2000, home price...
Engle (1982) introduced the autoregressive conditionally heteroskedastic model for quantifying the c...
This paper compares and evaluates various generalized autoregressive conditional heteroscedastic (GA...
This paper compares and estimates standard and asymmetric GARCH models with daily returns data of th...
Market properties and shares are important in the field of finance in order to measure the economic ...
Reliable and accurate forecasts can provide important input for fund manager and policymakers to m...
A new variant of the ARCH class of models for forecasting the conditional variance, to be called the...
It is well-known that financial time series exhibits changing variance and this can have important c...
Along with the large number of investors transacting on Islamic stocks, the movement of stock prices...
This paper aims to investigate the effectiveness of four volatility forecasting models, i.e. Exponen...
This paper examines the dynamics of return and dynamic volatility across the Malaysian and pan-Asian...
This paper applies the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to t...
The purpose of this study is to examine the house price volatility in three urban areas in Malaysia....
This paper examines and estimate the three GARCH(1,1) models (GARCH, EGARCH and GJR-GARCH) using the...
A new variant of the ARCH class of models for forecasting the conditional variance, to be called the...
The current housing market in the United States has never been more unstable. Since 2000, home price...
Engle (1982) introduced the autoregressive conditionally heteroskedastic model for quantifying the c...
This paper compares and evaluates various generalized autoregressive conditional heteroscedastic (GA...
This paper compares and estimates standard and asymmetric GARCH models with daily returns data of th...