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    Volatility of Stock Market and Exchange Rate Returns in Peru: Long Memory or Short Memory with Level Shifts?
    (Pontificia Universidad Católica del Perú. Departamento de Economía, 2014)
    Though the econometrics literature on this area is extensive, in Peru few studies have been dedicated to the analysis of financial returns in general and volatility in particular. As part of an empirical research agenda suggested by Humala and Rodríguez (2013), this paper represents one of the first attempts to distinguish between long- and short-memory (with level shifts) in volatility of Peru’s stock market and exchange rate returns. We utilize the statistical approach put forward by Perron and Qu (2010). The data is end-of-day and span the period January 3, 1990 to June 13, 2013 (5,831 observations) for the stock market returns, and January, 3 1997 until June 24, 2013 (4,110 observations) for exchange rate returns. The analysis of the ACF, the periodogram and the fractional parameter estimation for the two volatilities suggest that the theoretical predictions of Perron and Qu s simple mixture model (2010) are correct. The results are more conclusive for stock market volatility in comparison with those of the exchange rate. The application of one of the statistics employed by Perron and Qu (2010) suggest the rejection of a long-memory hypothesis for both volatilities. Nonetheless, the other statistics provide weak evidence against the null hypothesis, above all for the exchange rate market. To reinforce the findings, some results associated with other investigations are presented.
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    Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models
    (Pontificia Universidad Católica del Perú. Departamento de Economía, 2020-02)
    Seven GARCH and stochastic volatility (SV) models are used to model and compare empirically the volatility of returns on four commodities: gold, copper, oil, and natural gas. The results show evidence of fat tails and random jumps created by supply/demand imbalances, international instability episodes, geopolitical tensions, and market speculation, among other factors. We also find evidence of a leverage effect in oil and copper, resulting from their dependence on world economic activity; and of an inverse leverage effect in gold and natural gas, consistent with the formerís role as safe asset and with uncertainty about the latterís future supply. Additionally, in most cases there is no evidence of an impact of volatility on the mean. Finally, we find that the best-performing return volatility models are GARCH-t for gold, SV-t for copper and oil, and SV with leverage effects (SV-L) for natural gas.