Note: This page has been translated by MathWorks. Please click here

To view all translated materials including this page, select Japan from the country navigator on the bottom of this page.

To view all translated materials including this page, select Japan from the country navigator on the bottom of this page.

Monte Carlo simulation of conditional variance models

`V = simulate(Mdl,numObs)`

`V = simulate(Mdl,numObs,Name,Value)`

```
[V,Y] =
simulate(___)
```

simulates
conditional variance paths with additional options specified by one
or more `V`

= simulate(`Mdl`

,`numObs`

,`Name,Value`

)`Name,Value`

pair arguments. For example,
you can generate multiple sample paths or specify presample innovation
paths.

[1] Bollerslev, T. “Generalized Autoregressive Conditional
Heteroskedasticity.” *Journal of Econometrics*.
Vol. 31, 1986, pp. 307–327.

[2] Bollerslev, T. “A Conditionally Heteroskedastic
Time Series Model for Speculative Prices and Rates of Return.” *The
Review of Economics and Statistics*. Vol. 69, 1987, pp.
542–547.

[3] Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. *Time
Series Analysis: Forecasting and Control*. 3rd ed. Englewood
Cliffs, NJ: Prentice Hall, 1994.

[4] Enders, W. *Applied Econometric Time Series*.
Hoboken, NJ: John Wiley & Sons, 1995.

[5] Engle, R. F. “Autoregressive Conditional Heteroskedasticity
with Estimates of the Variance of United Kingdom Inflation.” *Econometrica*.
Vol. 50, 1982, pp. 987–1007.

[6] Glosten, L. R., R. Jagannathan, and D.
E. Runkle. “On the Relation between the Expected Value and
the Volatility of the Nominal Excess Return on Stocks.” *The
Journal of Finance*. Vol. 48, No. 5, 1993, pp. 1779–1801.

[7] Hamilton, J. D. *Time Series Analysis*.
Princeton, NJ: Princeton University Press, 1994.

[8] Nelson, D. B. “Conditional Heteroskedasticity
in Asset Returns: A New Approach.” *Econometrica*.
Vol. 59, 1991, pp. 347–370.

Was this topic helpful?