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on Econometric Time Series |
| By: | Behlool Sabir; M. S. Santhanam |
| Abstract: | The study of record statistics of correlated series is gaining momentum. In this work, we study the records statistics of the time series of select stock market data and the geometric random walk, primarily through simulations. We show that the distribution of the age of records is a power law with the exponent $\alpha$ lying in the range $1.5 \le \alpha \le 1.8$. Further, the longest record ages follow the Fr\'{e}chet distribution of extreme value theory. The records statistics of geometric random walk series is in good agreement with that from the empirical stock data. |
| Date: | 2014–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1407.3742&r=ets |
| By: | Cristina García de la Fuente; Pedro Galeano; Michael P. Wiper |
| Abstract: | Financial returns often present a complex relation with previous observations, along with a slight skewness and high kurtosis. As a consequence, we must pursue the use of flexible models that are able to seize these special features: a financial process that can expose the intertemporal relation between observations, together with a distribution that can capture asymmetry and heavy tails simultaneously. A multivariate extension of the GARCH such as the Dynamic Conditional Correlation model with Skew-Slashinnovations for financial time series in a Bayesian framework is proposed in the present document, and it is illustrated using an MCMC within Gibbs algorithm performed onsimulated data, as well as real data drawn from the daily closing prices of the DAX,CAC40, and Nikkei indices |
| Keywords: | Bayesian inference, Dynamic Conditional Correlation, Financial time series, Infinite mixture, Kurtosis, MCMC, Skew-Slash |
| Date: | 2014–06 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws141711&r=ets |
| By: | Maria Angeles Carnero Fernandez; Ana Pérez; Esther Ruiz Ortega |
| Abstract: | The identification of asymmetric conditional heteroscedasticity is often based on samplecross-correlations between past and squared observations. In this paper we analyse theeffects of outliers on these cross-correlations and, consequently, on the identification ofasymmetric volatilities. We show that, as expected, one isolated big outlier biases thesample cross-correlations towards zero and hence could hide true leverage effect.Unlike, the presence of two or more big consecutive outliers could lead to detectingspurious asymmetries or asymmetries of the wrong sign. We also address the problemof robust estimation of the cross-correlations by extending some popular robustestimators of pairwise correlations and autocorrelations. Their finite sample resistanceagainst outliers is compared through Monte Carlo experiments. Situations with isolatedand patchy outliers of different sizes are examined. It is shown that a modified Ramsayweightedestimator of the cross-correlations outperforms other estimators in identifyingasymmetric conditionally heteroscedastic models. Finally, the results are illustrated withan empirical application |
| Keywords: | Cross-correlations, Leverage effect, Robust correlations, EGARCH |
| Date: | 2014–07 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:ws141912&r=ets |