Pricing Derivatives Securities with Prior Information on Long- Memory Volatility

Alejandro Islas Camargo () and Francisco Venegas-Martínez
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Alejandro Islas Camargo: ITAM, Departamento de Estadística

Economía Mexicana NUEVA ÉPOCA, 2003, vol. XII, issue 1, 103-134

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.

Keywords: contingent pricing; econometric modeling (search for similar items in EconPapers)
Date: 2003
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Citations: View citations in EconPapers (2)

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