Multiple Volatility Components Research Articles

Effect of storage after heating on odor of muscles of yellowtail (Seriola quinqueradiata).

Effects of storage after heating on the odor of yellowtail Seriola quinqueradiata muscle were investigated. Sensory evaluation demonstrated odor degradation during storage of ordinary muscle as well as dark muscle (DM). First, different volatile profiles between OM (dorsal and ventral) and DM were found; their profiles were also different between non-stored samples (raw samples and just-heated samples) and stored samples except for a part of stored OM. Although the dorsal and ventral OMs differed in lipid content, their volatile profiles were similar to each other. The aforementioned differences were due to increased levels of lipid oxidation compounds (eg, aldehydes and alcohols) during storage after heating. However, none of the muscle parts showed significant changes in the intensity of each odor perceived by gas chromatography-olfactometry and trimethyl amine during storage. These findings suggested multiple volatile components may contribute to the odor deterioration of heated yellowtail muscle during cold storage.

Volatility Bursts: A Discrete-Time Option Model with Multiple Volatility Components

Abstract I propose an affine discrete-time model, called Vector Autoregressive Gamma with volatility Bursts (VARG-B) in which volatility experiences, in addition to frequent and small changes, periods of sudden and extreme movements generated by a latent factor which evolves according to the Autoregressive Gamma Zero process. A key advantage of the discrete-time specification is the possibility of estimating the model using Kalman Filter techniques. Moreover, the VARG-B model leads to a fully analytic conditional Laplace transform which boils down to a closed-form option pricing formula. When estimated on S&P500 index options and returns, the new model provides more accurate option pricing and modeling of the IV surface compared with some alternative models.

Volatility Bursts: A Discrete-time Option Model with Multiple Volatility Components

I propose an affine discrete-time model, called Vector Autoregressive Gamma with volatility Bursts (VARG-B) in which volatility experiences, in addition to frequent and small changes, periods of sudden and extreme movements generated by a latent factor which evolves according to the Autoregressive Gamma Zero process. A key advantage of the discrete-time specification is that it makes it possible to estimate the model via the Extended Kalman Filter. Moreover, the VARG-B model leads to a fully analytic conditional Laplace transform, resulting in a closed-form option pricing formula. When estimated on S&P500 index options and returns the new model provides more accurate option pricing and modelling of the IV surface compared with some alternative models.

Implications of Return Predictability for Consumption Dynamics and Asset Pricing

Two broad classes of consumption dynamics—long-run risks and rare disasters—have proven successful in explaining the equity premium puzzle when used in conjunction with recursive preferences. We show that bounds a-là Gallant, Hansen, and Tauchen that restrict the volatility of the stochastic discount factor by conditioning on a set of return predictors constitute a useful tool to discriminate between these alternative dynamics. In particular, we document that models that rely on rare disasters meet comfortably the bounds independently of the forecasting horizon and the asset returns used to construct the bounds. However, the specific nature of disasters is a relevant characteristic at the 1-year horizon: disasters that unfold over multiple years are more successful in meeting the predictors-based bounds than one-period disasters. Instead, at the 5-year horizon, the sole presence of disasters—even if one-period and permanent—is sufficient for the model to satisfy the bounds. Finally, the bounds point to multiple volatility components in consumption as a promising dimension for long-run risk models.

Studies of competing evaporation rates of multiple volatile components from a single binary-component aerosol droplet.

The simultaneous evaporation and condensation of multiple volatile components from multicomponent aerosol droplets leads to changes in droplet size, composition and temperature. Measurements and models that capture and predict these dynamic aerosol processes are key to understanding aerosol microphysics in a broad range of contexts. We report measurements of the evaporation kinetics of droplets (initially ∼25 μm radius) formed from mixtures of ethanol and water levitated within a electrodynamic balance over timescales spanning 500 ms to 6 s. Measurements of evaporation into a gas phase of varied relative humidity and temperature are shown to compare well with predictions from a numerical model. We show that water condensation from the gas phase can occur concurrently with ethanol evaporation from aqueous-ethanol droplets. Indeed, water can condense so rapidly during the evaporation of a pure ethanol droplet in a humid environment, driven by the evaporative cooling the droplet experiences, that the droplet becomes pure water within 0.4 s.

Analysis and Comparison for Main Volatile Compounds of Pericarpium Arecae in Different Drying Methods

The main volatile compounds of Pericarpium Arecae (PA) dried by different methods were analysed via SPME-GC-MS combined with retention index (RI) method and standard. There were multiple volatile components of the PA dried by the five methods respectively identified; meanwhile, each one was quantified by area normalization method. The results indicated that the content of arecoline, the index constituent, was low in sample F (5.11%) and sample BD (6.97%), but high in sample D (38.88%), sample MFMD (26.45%) and sample HFMD (35.64%). It can be seen that drying has less damage to the known active ingredients such as arecoline. The microwave drying method of middle fire (MFMD) had better effect to get comprehensive ingredients, due to less damage to volatile components. In conclusion, different drying methods have great influence on the volatile components of PA. And the drying method (D) has a great advantage on the active components such as arecoline. However, the method (MFMD) has less time consuming and can preserve more complete volatile components.

Volatility Bursts: A Discrete-Time Option Model with Multiple Volatility Components

I propose an affine discrete-time model, called Vector Autoregressive Gamma with volatility Bursts (VARG-B) in which volatility experiences, in addition to frequent and small changes, periods of sudden and extreme movements generated by a latent factor which evolves according to the Autoregressive Gamma Zero process. A key advantage of the discrete-time specification is that it makes it possible to estimate the model via the Extended Kalman Filter. Moreover, the VARG-B model leads to a fully analytic conditional Laplace transform, resulting in a closed-form option pricing formula. When estimated on S&P500 index options and returns the new model provides more accurate option pricing and modelling of the IV surface compared with some alternative models.

Analysis of the essential oils of Alpiniae Officinarum Hance in different extraction methods

It was developed for the analysis of the essential oils of Alpiniae Officinarum Hance extracted by steam distillation (SD), ultrasonic assisted solvent extraction (UAE) and supercritical fluid extraction (SFE) via gas chromatography mass spectrometry (GC-MS) combined with retention index (RI) method. There were multiple volatile components of the oils extracted by the three above-mention methods respectively identified; meanwhile, each one was quantified by area normalization method. The results indicated that the content of 1,8-Cineole, the index constituent, by SD was similar as SFE, and higher than UAE. Although UAE was less time consuming and consumed less energy, the oil quality was poorer due to the use of organic solvents was hard to degrade. In addition, some constituents could be obtained by SFE but could not by SD. In conclusion, essential oil of different extraction methods from the same batch of materials had been proved broadly similarly, however, there were some differences in composition and component ratio. Therefore, development and utilization of different extraction methods must be selected according to the functional requirements of products.

Option Valuation with Volatility Components, Fat Tails, and Nonmonotonic Pricing Kernels*

We nest multiple volatility components, fat tails and a U-shaped pricing kernel in a single option model and compare their contribution to describing returns and option data. All three features lead to statistically significant model improvements. A second volatility factor is economically most important and improves option fit by 18% on average. A U-shaped pricing kernel improves the option fit by 17% on average, and more so for two-factor models. Fat tails improve option fit by just over 3% on average, and more so when a U-shaped pricing kernel is applied. Our results suggest that the three features we investigate are complements rather than substitutes.

Option Valuation with Volatility Components, Fat Tails, and Nonlinear Pricing Kernels

We nest multiple volatility components, fat tails and a U-shaped pricing kernel in a single option model and compare their contribution to describing returns and option data. All three features lead to statistically significant model improvements. A U-shaped pricing kernel is economically most important and improves option fit by 17% on average and more so for two-factor models. A second volatility component improves the option fit by 9% on average. Fat tails improve option fit by just over 4% on average, but more so when a U-shaped pricing kernel is applied. Overall these three model features are complements rather than substitutes: the importance of one feature increases in conjunction with the others.

Term Structure of Variance Risk Premium in Multi-Component GARCH Models

We derive an analytic relation between equity risk premium and the term structure of variance risk premium (VRP). Motivated by this result, we estimate the VRP term structure using a general and fully analytical discrete-time option pricing framework featuring multiple volatility components and multiple risk premia. We confirm the importance of VRP in improving option pricing performances and show the ability of multi-component GARCH models to produce realistic hump-shaped VRP term structure. We finally uncover the strong predictive power of the shape of the VRP term structure, summarized by its slope, on future stock-index returns.

Statistical Rate Theory Insight into Evaporation and Condensation in Multicomponent Systems

Current approaches to mathematically modeling liquid-vapor mass transport (e.g., film theory, penetration theory, boundary layer theory) treat bulk phase transport accurately with diffusion models, but leave the transport across the interface to be described by empirically determined mass transfer coefficients. In multicomponent systems, this requires empirical mixing rules for the single-component mass transfer coefficients. Such approaches can only give estimates of net rates at the interface but cannot examine the movement of individual components. Here we use statistical rate theory to provide new physical insight into evaporation and condensation at interfaces in systems containing multiple volatile components. In contrast to the traditional multicomponent mass transfer approach, we show ranges where one component evaporates while the other condenses even when the net transport is unidirectional.

Long-memory and heterogeneous components in high frequency Pacific-Basin exchange rate volatility

Recent research examining high-frequency financial data has suggested that volatility dynamics may be confounded by the existence of an intra-day periodic pattern and multiple sources of volatility. This paper examines whether these dynamics are present in the US Dollar exchange rates of five Pacific Basin economies. Using 30-min sampled returns, evidence of a ‘U’-shape intra-day pattern in volatility for regional markets is reported and controlled for using a Flexible Fourier transform. Supportive evidence for the existence of multiple volatility components is offered by semi-parametric fractional difference estimates of the long-memory properties of absolute exchange rate returns at various intra-day data sampling frequencies. Further parametric evidence of an explicit component structure in such high frequency exchange rate volatility is offered by the estimates of a component-GARCH model which comprises both a long-run volatility component exhibiting slow shock decay and a short-run volatility component exhibiting far more rapid decay, and provides a generally superior fit to the data. Further application of these C-GARCH models in the analysis of high frequency volatility spillovers between the currencies considered also reveals that such spillovers are predominantly transitory rather than highly persistent in nature, but that where volatility spillovers do impact on the long-run component of exchange rate volatility the Australian Dollar plays a pivotal role in the localised causality transmission mechanism.

Correlations of flavor score with volatiles of vegetable oils

AbstractA simple, direct, gas chromatograph (GC) technique is described for eluting flavor‐related volatile components from commercially produced vegetable oils. A sample of oil was placed onto glass wool contained in a GC liner, and the liner was inserted in the heated inlet of the GC. Volatiles from the oils were rapidly eluted by heat and carrier gas onto the GC column. Profiles of the volatiles were obtained by temperature‐programmed gas chromatography. Flavor score was highly correlated with individual volatile components considered separately, and very highly correlated with multiple volatile components considered together, indicating that reliable flavor characteristics of vegetable oils may be obtained rapidly and efficiently by instrumentation.