Parametric estimation of monthly volatility using autoregressive conditional duration models
Shouwei LIU
10.25440/smu.12309752.v1
https://researchdata.smu.edu.sg/articles/thesis/Parametric_estimation_of_monthly_volatility_using_autoregressive_conditional_duration_models/12309752
<table><tr>
<td><p>This
paper employs a method to estimate monthly volatility by integrating the
conditional return variance over a month using the autoregressive conditional
duration (ACD) models. The ACD models fit the daily data surprisingly well.
Maximum likelihood Estimation (MLE) method is used to estimate the
conditional expected duration equation, which is assumed to follow the
augmented ACD models. The estimated monthly stock volatility are adopted to
investigate, if any, the link between macroeconomic variability and the stock
market volatility. We find that, for the period 1944/01-1975/06, PPI
inflation, monetary base growth and industrial production predict stock
market volatility very well, which are estimated by ACD methods; the monthly
stock volatility, estimated from ACD models, also helps predict the
macroeconomic volatility in the period 1975/07-2008/12.</p></td></tr></table>
2020-05-15 08:45:32
AACD models
monthly volatility
daily data
macroeconomics
autoregression
Macroeconomics (incl. Monetary and Fiscal Theory)
Financial Econometrics