Pricing Derivatives Securities with Prior Information on Long- Memory Volatility

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 li...

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Detalles Bibliográficos
Autores principales: Alejandro Islas Camargo, Francisco Venegas Martínez
Formato: Artículo científico
Publicado: Centro de Investigación y Docencia Económicas, A.C. 2003
Materias:
Economía y Finanzas
contingent pricing
econometric modeling
Acceso en línea:http://www.redalyc.org/articulo.oa?id=32312104
http://biblioteca.clacso.edu.ar/gsdl/cgi-bin/library.cgi?a=d&c=mx/mx-010&d=32312104oai
Aporte de:
Red de Bibliotecas Virtuales de Ciencias Sociales (CLACSO) de Consejo Latinoamericano de Ciencias Sociales
id I16-R122-32312104oai
record_format dspace
institution Consejo Latinoamericano de Ciencias Sociales
institution_str I-16
repository_str R-122
collection Red de Bibliotecas Virtuales de Ciencias Sociales (CLACSO)
topic Economía y Finanzas
contingent pricing
econometric modeling
spellingShingle Economía y Finanzas
contingent pricing
econometric modeling
Alejandro Islas Camargo
Francisco Venegas Martínez
Pricing Derivatives Securities with Prior Information on Long- Memory Volatility
topic_facet Economía y Finanzas
contingent pricing
econometric modeling
description 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.
format Artículo científico
Artículo científico
author Alejandro Islas Camargo
Francisco Venegas Martínez
author_facet Alejandro Islas Camargo
Francisco Venegas Martínez
author_sort Alejandro Islas Camargo
title Pricing Derivatives Securities with Prior Information on Long- Memory Volatility
title_short Pricing Derivatives Securities with Prior Information on Long- Memory Volatility
title_full Pricing Derivatives Securities with Prior Information on Long- Memory Volatility
title_fullStr Pricing Derivatives Securities with Prior Information on Long- Memory Volatility
title_full_unstemmed Pricing Derivatives Securities with Prior Information on Long- Memory Volatility
title_sort pricing derivatives securities with prior information on long- memory volatility
publisher Centro de Investigación y Docencia Económicas, A.C.
publishDate 2003
url http://www.redalyc.org/articulo.oa?id=32312104
http://biblioteca.clacso.edu.ar/gsdl/cgi-bin/library.cgi?a=d&c=mx/mx-010&d=32312104oai
work_keys_str_mv AT alejandroislascamargo pricingderivativessecuritieswithpriorinformationonlongmemoryvolatility
AT franciscovenegasmartinez pricingderivativessecuritieswithpriorinformationonlongmemoryvolatility
bdutipo_str Repositorios
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