1126 The ournal of Finance 0,c5 0.03x .01 认M 18591869 918s91899190919191929193919491959196919791989 January t880-Dacambar 1987 Figure 5. Predictions of the monthly standard deviations of stock returns(---)and of money base growth rates ()for 1880-1987. A 12th-order autoregression with different ate, and then the absolute values of the residuals are used to estimate valatility in iel conditional volatility, a 12th-order autoregressive model with different monthly is used to predict the standard deviation in month t based on lagged standard deviation estimates. This plot contains fitted values from the volatility regression models stock volatility, high-grade bond return volatility lernel, and short-term interest volatility that allows for different monthly intercepts. The VAR model uses both the monthly measure of stock return volatility IEsl and the daily measure a 0 These VAR models are generalizations of the autoregressive model in(3b), but they include lagged values of other variables to help predict volatility. The F-tests in Table iii measure the significance of the lagged values of the column variable in predicting the row variable, given the other variables in the model. F tatistics that are larger than the 0.01 critical value 2.28 are indicated with asterisks The largest F-statistics are on the main diagonal of these matrices, and the size of the statistics decreases away from the diagonal. For example, lagged stock 1e Models using the volatility of medium-grade(Baa-rated)bond return volatility, lerma, instead of high-grade bond return volatility, yielded similar results for the post- 1920 periods. Medium-grade bond valatility is more strongly related to the stock volatility and more weakly related to the short term interest rate volatility but the relations with the macroeconomic valatility series are generally similar. Because these data are only available from 1920 to 1987 and the results are similar, they are not reported