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Pricing Derivatives Securities with Prior Information on Long- Memory Volatility

  • Alejandro Islas Camargo


    (ITAM, Departamento de Estadística)

  • Francisco Venegas Martínez

    (Oxford University)

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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Article provided by in its journal Economia Mexicana NUEVA EPOCA.

Volume (Year): XII (2003)
Issue (Month): 1 (January-June)
Pages: 103-134

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Handle: RePEc:emc:ecomex:v:12:y:2003:i:1:p:103-134
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