No abstract

This paper performs a thorough statistical examination of the time-series properties of the daily market volatility index (VIX) from the Chicago Board Options Exchange (CBOE). The motivation lies not only on the widespread consensus that the VIX is a barometer of the overall market sentiment as to what concerns investors' risk appetite, but also on the fact that there are many trading strategies that rely on the VIX index for hedging and speculative purposes. Preliminary analysis suggests that the VIX index displays long-range dependence. This is well in line with the strong empirical evidence in the literature supporting long memory in both options-implied and realized variances. We thus resort to both parametric and semiparametric heterogeneous autoregressive (HAR) processes for modeling and forecasting purposes. Our main findings are as follows. First, we confirm the evidence in the literature that there is a negative relationship between the VIX index and the S&P 500 index return as well as a positive contemporaneous link with the volume of the S&P 500 index. Second, the term spread has a slightly negative long-run impact in the VIX index, when possible multicollinearity and endogeneity are controlled for. Finally, we cannot reject the linearity of the above relationships, neither in sample nor out of sample. As for the latter, we actually show that it is pretty hard to beat the pure HAR process because of the very persistent nature of the VIX index.

No abstract

This paper develops a family of autoregressive conditional duration (ACD) models that encompasses most specifications in the literature. The nesting relies on a Box-Cox transformation with shape parameter l to the conditional duration process and a possibly asymmetric shocks impact curve. We establish conditions for the existence of higher-order moments, strict stationarity, geometric ergodicity and b-mixing property with exponential decay. We next derive moment recursion relations and the autocovariance function of the power l of the duration process. Finally, we assess the practical usefulness of our family of ACD models using New York stock exchange (NYSE) transactions data, with special attention to IBM price durations. The results warrant the extra flexibility provided either by the Box-Cox transformation or by the asymmetric response to shocks. r 2004 Elsevier B.V. All rights reserved. JEL Classification: C22; C41

This paper deals with the testing of autoregressive conditional duration (ACD) models by gauging the distance between the parametric density and hazard rate functions implied by the duration process and their non-parametric estimates. We derive the asymptotic justification using the functional delta method for fixed and gamma kernels, and then investigate the finitesample properties through Monte Carlo simulations. Although our tests display some size distortion, bootstrapping suffices to correct the size without compromising their excellent power. We show the practical usefulness of such testing procedures for the estimation of intraday volatility patterns. r

We propose to smooth the entire objective function, rather than only the check function, in a linear quantile regression context. Not only does the resulting smoothed quantile regression estimator yield a lower mean squared error and a more accurate Bahadur-Kiefer representation than the standard estimator, but it is also asymptotically differentiable. We exploit the latter to propose a quantile density estimator that does not suffer from the curse of dimensionality. This means estimating the conditional density function without worrying about the dimension of the covariate vector. It also allows for two-stage efficient quantile regression estimation. Our asymptotic theory holds uniformly with respect to the bandwidth and quantile level. Finally, we propose a rule of thumb for choosing the smoothing bandwidth that should approximate well the optimal bandwidth. Simulations confirm that our smoothed quantile regression estimator indeed performs very well in finite samples. JEL classification numbers C14, C21

No abstract

Different types of activated carbon were prepared by chemical activation of brewer's spent grain (BSG) lignin using H(3)PO(4) at various acid/lignin ratios (1, 2, or 3g/g) and carbonization temperatures (300, 450, or 600 degrees C), according to a 2(2) full-factorial design. The resulting materials were characterized with regard to their surface area, pore volume, and pore size distribution, and used for detoxification of BSG hemicellulosic hydrolysate (a mixture of sugars, phenolic compounds, metallic ions, among other compounds). BSG carbons presented BET surface areas between 33 and 692 m(2)/g, and micro- and mesopores with volumes between 0.058 and 0.453 cm(3)/g. The carbons showed high capacity for adsorption of metallic ions, mainly nickel, iron, chromium, and silicon. The concentration of phenolic compounds and color were also reduced by these sorbents. These results suggest that activated carbons with characteristics similar to those commercially found and high adsorption capacity can be produced from BSG lignin.