A component GARCH model with time varying weights

We present a novel GARCH model that accounts for time varying, state dependent, persistence in the volatility dynamics. The proposed model generalizes the component GARCH model of Ding and Granger (1996). The volatility is modelled as a convex combination of unobserved GARCH components where the combination weights are time varying as a function of appropriately chosen state variables. In order to make inference on the model parameters, we develop a Gibbs sampling algorithm. Adopting a fully Bayesian approach allows to easily obtain medium and long term predictions of relevant risk measures such as value at risk and expected shortfall. Finally we discuss the results of an application to a series of daily returns on the S&P500.GARCH, persistence, volatility components, value-at-risk, expected shortfall

Combination of multivariate volatility forecasts

This paper proposes a novel approach to the combination of conditional covariance matrix forecasts based on the use of the Generalized Method of Moments (GMM). It is shown how the procedure can be generalized to deal with large dimensional systems by means of a two-step strategy. The finite sample properties of the GMM estimator of the combination weights are investigated by Monte Carlo simulations. Finally, in order to give an appraisal of the economic implications of the combined volatility predictor, the results of an application to tactical asset allocation are presented.Multivariate GARCH, Forecast Combination, GMM, Portfolio Optimization

A component GARCH model with time varying weights

We present a novel GARCH model that accounts for time varying, state dependent, persistence in the volatility dynamics. The proposed model generalizes the component GARCH model of Ding and Granger (1996). The volatility is modelled as a convex combination of unobserved GARCH components where the combination weights are time varying as a function of appropriately chosen state variables. In order to make inference on the model parameters, we develop a Gibbs sampling algorithm. Adopting a fully Bayesian approach allows to easily obtain medium and long term predictions of relevant risk measures such as value at risk and expected shortfall. Finally we discuss the results of an application to a series of daily returns on the S&P500

A model for enhanced coal bed methane recovery aimed at carbon dioxide storage: The role of sorption, swelling and composition of injected gas

Numerical simulations on the performance of CO2 storage and enhanced coal bed methane (ECBM) recovery in coal beds are presented. For the calculations, aone-dimensional mathematical model is used consisting of mass balances describing gas flow and sorption, and a geomechanical relationship to account for porosity and permeability changes during injection. Important insights are obtained regarding the gas flow dynamics during displacement and the effects of sorption and swelling on the ECBM operation. In particular, initial faster CH4 recovery is obtained when N2 is added to the injected mixture, whereas pure CO2 allows for a more effective displacement in terms of total CH4 recovery. Moreover, it is shown that coal swelling dramatically affects the gas injectivity, as the closing of the fractures associated with it strongly reduces coal's permeability. As a matter of fact, injection of flue gas might represent a useful option to limit this proble

Dynamic conditional correlation models for realized covariance matrices

New dynamic models for realized covariance matrices are proposed. The expected value of the realized covariance matrix is specified in two steps: one for each realized variance, and one for the realized correlation matrix. The realized correlation model is a scalar dynamic conditional correlation model. Estimation can be done in two steps as well, and a QML interpretation is given to each step, by assuming a Wishart conditional distribution. The model is applicable to large matrices since estimation can be done by the composite likelihood method

Time-varying parameters Realized GARCH models for tracking attenuation bias in volatility dynamics

This paper proposes novel approaches to the modeling of attenuation bias effects in volatility forecasting. Our strategy relies on suitable generalizations of the Realized GARCH model by Hansen et al. (2012) where the impact of lagged realized measures on the current conditional variance is weighted according to the accuracy of the measure itself at that specific time point. This feature allows assigning more weight to lagged volatilities when they are more accurately measured. The ability of the proposed models to generate accurate forecasts of volatility and related tail risk measures, Value-at-Risk and Expected Shortfall, is assessed by means of an application to a set of major stock market indices. The results of the empirical analysis show that the proposed specifications are able to outperform standard Realized GARCH models in terms of out-of-sample forecast performance under both statistical and economic criteria

Time Varying Heteroskedastic Realized GARCH models for tracking measurement error bias in volatility forecasting

This paper proposes generalisations of the Realized GARCH model by Hansen et al. (2012), in three different directions. First, heteroskedasticity in the noise term in the measurement equation is allowed, since this is generally assumed to be time-varying as a function of an estimator of the Integrated Quarticity for intra-daily returns. Second, in order to account for attenuation bias effects, the volatility dynamics are allowed to depend on the accuracy of the realized measure. This is achieved by letting the response coefficient of the lagged realized measure depend on the time-varying variance of the volatility measurement error, thus giving more weight to lagged volatilities when they are more accurately measured. Finally, a further extension is proposed by introducing an additional explanatory variable into the measurement equation, aiming to quantify the bias due to effect of jumps and measurement errors

Understanding of cyclodepolymerization kinetics for the production of cyclic polyethylene furanoate oligomers

In the last two decades intense research was focused on Polyethylene Furanoate (PEF) as a promising substitute for Polyethylene Terephthalate (PET). Comparisons of PEF and PET material properties revealed improved gas barrier properties, lower melting temperature, as well as higher tensile strength. Together with the reduced carbon foot-print, these properties make PEF an attractive alternative to PET. Most PEF discussed in the literature is produced by polycondensation reactions (PC), a process, which is intrinsically slow due to diffusion limitations. In fact, it can take up to several days at high temperatures and high vacuum to produce high molecular weight PEF. The long exposure to high temperature is especially detrimental in the PEF case, since PEF is much more prone to thermal degradation than PET. Please download the file below for full content

Time Varying Heteroskedastic Realized GARCH models for tracking measurement error bias in volatility forecasting

This paper proposes generalisations of the Realized GARCH model by Hansen et al. (2012), in three different directions. First, heteroskedasticity in the noise term in the measurement equation is allowed, since this is generally assumed to be time-varying as a function of an estimator of the Integrated Quarticity for intra-daily returns. Second, in order to account for attenuation bias effects, the volatility dynamics are allowed to depend on the accuracy of the realized measure. This is achieved by letting the response coefficient of the lagged realized measure depend on the time-varying variance of the volatility measurement error, thus giving more weight to lagged volatilities when they are more accurately measured. Finally, a further extension is proposed by introducing an additional explanatory variable into the measurement equation, aiming to quantify the bias due to effect of jumps and measurement errors