JSTOR Daily

Buffered Autoregressive Models With Conditional Heteroscedasticity: An Application to Exchange Rates

This article introduces a new model called the buffered autoregressive model with generalized autoregressive conditional heteroscedasticity (BAR-GARCH). The proposed model, as an extension of the BAR ...
Risk

Range-based volatility forecasting: a multiplicative component conditional autoregressive range model

To capture the "long-memory" effect in volatility, a multiplicative component conditional autoregressive range (MCCARR) model is proposed. We show theoretically that the MCCARR model can capture the ...
Risk

Forecasting stock market volatility: an asymmetric conditional autoregressive range mixed data sampling (ACARR-MIDAS) model

In this paper we extend the conditional autoregressive range (CARR) model to the asymmetric CARR mixed data sampling (ACARR-MIDAS) model, which takes into consideration volatility asymmetry as well as ...
JSTOR Daily

Hybrid quantile regression estimation for time series models with conditional heteroscedasticity

Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 80, No. 5 (2018), pp. 975-993 (19 pages) Estimating conditional quantiles of financial time series is essential for ...
Investopedia

What Are Autoregressive Models? How They Work and Example

Thomas J Catalano is a CFP and Registered Investment Adviser with the state of South Carolina, where he launched his own financial advisory firm in 2018. Thomas' experience gives him expertise in a ...