Conditional Volatility Estimation by Conditional Quantile Autoregression

dc.contributor.author	Mutunga, D. N.
dc.contributor.author	Mwita, Peter N.
dc.contributor.author	Muema, B. K.
dc.date.accessioned	2018-11-16T07:32:17Z
dc.date.available	2018-11-16T07:32:17Z
dc.date.issued	2014
dc.identifier.uri	http://ir.mksu.ac.ke/handle/123456780/1610
dc.description.abstract	This paper considers the problem of estimating conditional volatility function using conditional quantile autoregression function. We estimate the interquantile autoregression range and the conditional volatility function under known distributional assumptions. The conditional volatility function estimator is found to be theoretically consistent. A small simulation study ascertains that the Volatility Estimator is consistent. Mathematics Subject Classification: 62G05; 62M1	en_US
dc.language.iso	en_US	en_US
dc.publisher	HIKARI Ltd	en_US
dc.subject	Quantile	en_US
dc.subject	InterQuantile	en_US
dc.subject	Autoregression	en_US
dc.title	Conditional Volatility Estimation by Conditional Quantile Autoregression	en_US
dc.type	Article	en_US

School of Pure and Applied Sciences [259]
Scholarly Articles by Faculty & Students in the School of Pure and Applied Sciences