Gamma stochastic volatility models

This paper presents gamma stochastic volatility models and investigates its distributional and time series properties. The parameter estimators obtained by the method of moments are shown analytically to be consistent and asymptotically normal. The simulation results indicate that the estimators behave well. The insample analysis shows that return models with gamma autoregressive stochastic volatility processes capture the leptokurtic nature of return distributions and the slowly decaying autocorrelation functions of squared stock index returns for the USA and UK. In comparison with GARCH and EGARCH models, the gamma autoregressive model picks up the persistence in volatility for the US and UK index returns but not the volatility persistence for the Canadian and Japanese index returns. The out-of-sample analysis indicates that the gamma autoregressive model has a superior volatility forecasting performance compared to GARCH and EGARCH models.

Impact of zinc substitution on the structural and magnetic properties of chemically derived nanosized manganese zinc mixed ferrites

Mn1-xZnxFe2O4 nanoparticles (x=0-1) were synthesized by wet chemical co-precipitation techniques. X-ray diffraction, transmission electron microscopy and high-resolution transmission electron microscopy were effectively utilized to investigate the different structural parameters. The elemental analysis was conducted using energy-dispersive spectrum and inductively coupled plasma analysis. The magnetic properties such as magnetization and coercivity were measured using vibrating sample magnetometer. The observed magnetization values of the nanoparticles were found to be lower compared to the bulk counterpart. The magnetization showed a gradual decrease with zinc substitution except for a small increase from x=0.2 to 0.3. The Curie temperature was found to be enhanced in the case of ferrites in the nanoregime. The variation in lattice constant, reduced magnetization values, variation of magnetization with zinc substitution, the presence of a net magnetic moment for the zinc ferrite and the enhancement in Curie temperature in Mn1-xZnxFe2O4 all provide evidence to the existence of a metastable cation distribution together with possible surface effects at the nanoregime.

Computationally Efficient Bootstrap Prediction Intervals for Returns and Volatilities in ARCH and GARCH Processes

We propose a novel, simple, efficient and distribution-free re-sampling technique for developing prediction intervals for returns and volatilities following ARCH/GARCH models. In particular, our key idea is to employ a Box–Jenkins linear representation of an ARCH/GARCH equation and then to adapt a sieve bootstrap procedure to the nonlinear GARCH framework. Our simulation studies indicate that the new re-sampling method provides sharp and well calibrated prediction intervals for both returns and volatilities while reducing computational costs by up to 100 times, compared to other available re-sampling techniques for ARCH/GARCH models. The proposed procedure is illustrated by an application to Yen/U.S. dollar daily exchange rate data.