Asymmetric Stochastic Volatility Models and Multicriteria Decision Methods in Finance

• Autores: María Carmen García Centeno, Román Mínguez Salido
• Localización: Aestimatio: The IEB International Journal of Finance, ISSN 2173-0164, Nº. 3, 2011, pág. 5
• Idioma: español
• Enlaces
  • Texto completo (pdf)
• Dialnet Métricas: 1 Cita
• Resumen

  • In order to make a decision in any given context, it is necessary to have as much information as possible. For this reason, the objective of this paper is to choose a method, the most objectively possible, to establish an order of preferences between different stock index returns using all available statistical and econometrical information.

    The TGARCH(1,1) and TA-ARSV(1) are models estimated to obtain the econometrical information. This information is evaluated using discrete multicriteria decision methods such as PROMETHEE Methods, with the aim of obtaining a ranking of preferences between the different Stock Market Indexes in several scenarios. The different scenarios proposed show that the results obtained in the complete ranking of the different financial returns are robust

• Referencias bibliográficas
  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31, pp. 307-327.
  • Brans, J.P., Mareschal, B. and Vincke, P.H. (1984). PROMETHEE: a new family of outranking methods in multicriteria analysis, in Brans, J.P....
  • Brans, J.P. and Vincke, P.H. (1985). A preference ranking organization method, the PROMETHEE method, Management Science, 31, pp. 647-656.
  • Doornik, J.A. (2001). An object-oriented matrix programming language using Ox. Timberlake ants Ltd., London, http://timberlake.co.uk.
  • Durbin, J. A. and Koopman, K. J. (1997). Monte Carlo Maximum Likelihood Estimation for Non Gaussian State Space Models, Biometrika, 84, pp....
  • Engle, R.F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation, Econometrica, 50, pp. 987-1007.
  • Figueira, J. and Roy, B. (2002). Determining the weights of criteria in the ELECTRE type methods with a revised Simos1’ procedure, European...
  • García, M. C and Mínguez, R. (2009). Estimation of Asymmetric Stochastic Volatility Models for StockExchange Index Returns, International...
  • Glosten, L., Ragannthan, R. and Runkle, D. (1993). On the relation between the expected value and the volatility of the nominal excess returns...
  • Goumans, M. and Lygerou, V. (2000). An extension of the PROMETHEE method for decision making in fuzzy environment: Ranking of alternative...
  • Harvey, A. C. and Shephard, N. G. (1996). Estimation of an asymmetric stochastic volatility model for asset returns, Journal of Business and...
  • Koopman, S. J., Shephard, N. and Doornik, J. A. (1999). Statistical algorithms for models in state space using SsfPack 2.2, Econometrics Journal,...
  • Koopman, S. J. and Hol-Uspensky, E. A. (2002). The Stochastic Volatility in Mean Model: Empirical evidence from international stock markets,...
  • Racicot, F. E. and Théoret, R. (2010). Forecasting Stochastic Volatility Using the Kalman Filter: An Application to Canadian Interest Rates...
  • So, M. K. J., Li, W. K. and Lam, K. (2002). A threshold stochastic volatility model, Journal of forecasting, 21, 473-500.
  • Sandmann, G. and Koopman, S. J. (1998). Estimation of stochastic volatility models via Monte Carlo Maximum Likelihood, Journal of Econometrics,...
  • Teräsvirta, T. and Zhao, Z. (2006). Stylized facts of return series robust estimates and three popular models of volatility. Working Paper,...
  • Taylor, S.J. (1986). Modelling Financial Time Series, John Wiley and Sons Ltd, New York.
  • Zakoian, J. (1990). Threshold heteroskedastic models, Journal of Economic and Control, 18, pp. 931-995.