# Forecasting Volatilities and Correlations with EGARCH Models

@inproceedings{Cumby1993ForecastingVA, title={Forecasting Volatilities and Correlations with EGARCH Models}, author={Robert E. Cumby and Stephen Figlewski and Joel Hasbrouck}, year={1993} }

Volatility varies randomly over time, making forecasting it d@cult. Formal models for systems with timevarying volatility have been developed in recent years, and widely applied in economics and finance. Models in the Autoregressive Conditional Heteroscedasticity (ARCH) family have been particularly popular. Prior studies of ARCH-type models of securities return variances have looked at a single asset and focused on in-sample explanation of volatility movements, rather than forecasting. This… Expand

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#### References

SHOWING 1-10 OF 11 REFERENCES

Alternative Models for Conditional Stock Volatility

- Economics
- 1989

This paper compares several statistical models for monthly stock return volatility. The focus is on U.S. data from 1834-19:5 because the post-1926 data have been analyzed in more detail by others.… Expand

CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH

- Economics
- 1991

This paper introduces an ARCH model (exponential ARCH) that (1) allows correlation between returns and volatility innovations (an important feature of stock market volatility changes), (2) eliminates… Expand

Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation

- Economics
- 1982

Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional… Expand

Generalized autoregressive conditional heteroskedasticity

- Economics, Mathematics
- 1986

A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance… Expand

Consistency tests for heteroskedastic and risk models

- Economics
- 1992

This paper considers a class of consistency tests for the specification of heteroskedastic and risk models. The tests are related to other procedures such as the conditional moment tests of Newey and… Expand

Generalized method of moments specification testing

- Mathematics
- 1985

Abstract This paper analyzes the asymptotic power properties of specification tests which are based on a finite set of moment conditions. It shows that any such test may fail against general… Expand

ARCH Modeling in Finance: A Selective Review of the Theory and Empirical Evidence, with Suggestions for Future Research.

- 1990