Alejandro Islas Camargo () (ITAM, Departamento de Estadística) Francisco Venegas Martínez (Oxford University)
Abstract
This paper investigates the existence of long memory in the volatility of the Mexican stock market. We use a stochastic volatility (SV) model to derive statistical test for changes in volatility. In this case, estimation is carried out through the Kalman filter (KF) and the improved quasi-maximum likelihood (IQML). We also test for both persistence and long memory by using a long-memory stochastic volatility (LMSV) model, constructed by including an autoregressive fractionally integrated moving average (ARFIMA) process in a stochastic volatility scheme. Under this framework, we work up maximum likelihood spectral estimators and bootstraped confidence intervals. In the light of the empirical findings, we develop a Bayesian model for pricing derivative securities with prior information on long-memory volatility.
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Volume (Year): XII (2003) Issue (Month): 1 (January-June) Pages: 103-134 Download reference. The following formats are available: HTML
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