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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.

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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.”
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[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.