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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.

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