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Item type:Publication, 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Extreme Value Theory: An Application to the Peruvian Stock Market Returns(Pontificia Universidad Católica del Perú. Departamento de Economía, 2014)Using daily observations of the index and stock market returns for the Peruvian case from January 3, 1990 to May 31, 2013, this paper models the distribution of daily loss probability, estimates maximum quantiles and tail probabilities of this distribution, and models the extremes through a maximum threshold. This is used to obtain the better measurements of the Value at Risk (VaR) and the Expected Short-Fall (ES) at 95% and 99%. One of the results on calculating the maximum annual block of the negative stock market returns is the observation that the largest negative stock market return (daily) is 12.44% in 2011. The shape parameter is equal to -0.020 and 0.268 for the annual and quarterly block, respectively. Then, in the .rst case we have that the non-degenerate distribution function is Gumbel-type. In the other case, we have a thick-tailed distribution (Fréchet). Estimated values of the VaR and the ES are higher using the Generalized Pareto Distribution (GPD) in comparison with the Normal distribution and the di¤erences at 99.0% are notable. Finally, the non-parametric estimation of the Hill tail-index and the quantile for negative stock market returns shows quite instability. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Stochastic Volatility in Peruvian Stock Market and Exchange Rate Returns: a Bayesian Approximation(Pontificia Universidad Católica del Perú. Departamento de Economía, 2014)This study is one of the first to utilize the SV model to model Peruvian financial series, as well as estimating and comparing with GARCH models with normal and t-student errors. The analysis in this study corresponds to Peru s stock market and exchange rate returns. The importance of this methodology is that the adjustment of the data is better than the GARCH models using the assumptions of normality in both models. In the case of the SV model, three Bayesian algorithms have been employed where we evaluate their respective inefficiencies in the estimation of the model’s parameters being the most efficient the Integration sampler. The estimated parameters in the SV model under the various algorithms are consistent, as they display little inefficiency. The Figures of the correlations of the iterations suggest that there are no problems at the time of Markov chaining in all estimations. We find that the volatilities in exchange rate and stock market volatilities follow similar patterns over time. That is, when economic turbulence caused by the economic circumstances occurs, for example, the Asian crisis and the recent crisis in the United States, considerable volatility was generated in both markets.