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Stochastic Volatility Models (SVM) in the Analysis of Drought Periods

In: Mathematical and Statistical Applications in Life Sciences and Engineering

Author

Listed:
  • Jorge Alberto Achcar

    (University of São Paulo, Medical School
    FMRP University of São Paulo, Department of Social Medicine)

  • Roberto Molina de Souza

    (Federal Technological University of Paraná)

  • Emílio Augusto Coelho-Barros

    (Federal Technological University of Paraná)

Abstract

In the last few years, very atypical behavior of rain precipitation has been observed globally that may be attributed to climate changes. In this chapter, we approach the analysis of rain precipitation for a large city in Brazil: Campinas located in the southeast region of Brazil, São Paulo State, considering the time series of SPI (standard precipitation Index) measures (1, 3, 6, and 12-month timescales) ranging from January 01, 1947 to May 01, 2011. The present authors have previously used nonhomogeneous Poisson process approach (Achcar et al. Environ Ecol Stat 23:405–419, 2016, [1]) to analyze this data set. However, the analysis in this chapter uses a simpler methodology based on recently introduced SV (stochastic volatility) model (Ghysels, Statistical methods on finance, 1996, [9]) under a Bayesian approach. An excellent fit of the model for the data set is seen that shows some periods of great volatility, confirming atypical behavior for the rain precipitation.

Suggested Citation

  • Jorge Alberto Achcar & Roberto Molina de Souza & Emílio Augusto Coelho-Barros, 2017. "Stochastic Volatility Models (SVM) in the Analysis of Drought Periods," Springer Books, in: Avishek Adhikari & Mahima Ranjan Adhikari & Yogendra Prasad Chaubey (ed.), Mathematical and Statistical Applications in Life Sciences and Engineering, chapter 0, pages 159-168, Springer.
    • Handle: RePEc:spr:sprchp:978-981-10-5370-2_7
    DOI: 10.1007/978-981-10-5370-2_7
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