Risk

Range-based volatility forecasting: an extended conditional autoregressive range model

This paper proposes an extended conditional autoregressive range (EXCARR) model to describe the range-based volatility dynamics of financial assets. Our EXCARR model not only takes the conditional ...
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 ...
Drexel University

Epidemiology and Biostatistics Dissertation Defense: Guangzi Song

Guangzi Song, MS, will present "Estimation of the Informativeness of the Conditional Autoregressive Model Framework with Applications" The use of the conditional autoregressive (CAR) model framework ...
JSTOR Daily

Least Absolute Deviation Estimation for Fractionally Integrated Autoregressive Moving Average Time Series Models with Conditional Heteroscedasticity

We consider a unified least absolute deviation estimator for stationary and nonstationary fractionally integrated autoregressive moving average models with conditional heteroscedasticity. Its ...
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 ...