Why are volatility models are important in economics and finance?
A volatility model should be able to forecast volatility. Virtually all the financial uses of volatility models entail forecasting aspects of future returns. Typically a volatility model is used to forecast the absolute magnitude of returns, but it may also be used to predict quantiles or, in fact, the entire density.
What do we mean by a ARCH and Garch models and why they are useful?
A GARCH model subsumes ARCH models, where a GARCH(0, q) is equivalent to an ARCH(q) model. As with ARCH, GARCH predicts the future variance and expects that the series is stationary, other than the change in variance, meaning it does not have a trend or seasonal component.
What does Garch model stand for?
Generalized AutoRegressive Conditional Heteroskedasticity
Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) is a statistical model used in analyzing time-series data where the variance error is believed to be serially autocorrelated. GARCH models assume that the variance of the error term follows an autoregressive moving average process.
Why is modeling volatility important for Macroeconometric analysis?
One basic feature of aggregate data is the presence of time-varying variance in real and nominal variables. Modeling these movements in volatility is important to understand the source of aggregate fluctuations, the evolution of the economy, and for policy analysis.
Why do we forecast volatility?
Forecasting the volatility of the price of an asset accurately over the investment holding period is important for an investor to assess investment risk (Ser-Huang and Clive, 2002). Assessing the investment risk through volatility can help investors to make investment decisions and discover investment opportunities.
What does a GARCH model do?
GARCH is a statistical model that can be used to analyze a number of different types of financial data, for instance, macroeconomic data. Financial institutions typically use this model to estimate the volatility of returns for stocks, bonds, and market indices.
How do I use the Garch model in Excel?
Procedure
- Start Excel, open the example file Advanced Forecasting Model, go to the GARCH worksheet, and select Risk Simulator | Forecasting | GARCH.
- Click on the link icon, select the Data Location and enter the required input assumptions (see Figure 1), and click OK to run the model and report.
How does Garch model work?
The general process for a GARCH model involves three steps. The first is to estimate a best-fitting autoregressive model. The second is to compute autocorrelations of the error term. The third step is to test for significance.