Volatility Factors Research Articles

Research on Resource Scheduling in Cloud Computing Based on Firefly Genetic Algorithm

Cloud computing resource scheduling efficiency is the direction of the current research. In this paper, we first set up a resource scheduling model in cloud computing environment, and put the individual and the cloud computing node resources in the firefly algorithm. Secondly, in individual initialization algorithm introduced genetic algorithm optimize the initial solution, on location algorithm update setting sensory threshold used to adjust the individual probability of selecting the optimal path. Finally, the improvement of the volatile factor makes the value of the fluorescence to be updated. Simulation results show that the proposed algorithm can effectively improve the performance of resource scheduling in cloud computing, shorten the time to complete the task, and improve the overall processing capacity of the system.

Double-jump diffusion model for VIX: evidence from VVIX

This paper studies the continuous-time dynamics of VIX with stochastic volatility and jumps in VIX and volatility. Built on the general parametric affine model with stochastic volatility and jumps in the logarithm of VIX, we derive a linear relationship between the stochastic volatility factor and the VVIX index. We detect the existence of a co-jump of VIX and VVIX and put forward a double-jump stochastic volatility model for VIX through its joint property with VVIX. Using the VVIX index as a proxy for stochastic volatility, we use the MCMC method to estimate the dynamics of VIX. Comparing nested models of VIX, we show that the jump in VIX and the volatility factor are statistically significant. The jump intensity is also stochastic. We analyse the impact of the jump factor on VIX dynamics.

Company and Asset Specific Risk Factors: Short Overview on High Yield Bonds

This paper discusses the theory that risk factors divide to the company specific and asset specific risk factors. The first group affects to the expected value of an equity of a company whereas the second only to the positive cash outflows for a specific asset. I find that equity market, value, and quality factors are indeed possible company specific risk factors with influence on an expected equity of a company and dividend and volatility factors are possible stock specific risk factors affecting positively to dividends and other cash payments from a company to shareholders. These results are statistically significant and important for our understanding of risk factors and their characteristics.

A delayed stochastic volatility correction to the constant elasticity of variance model

The Black-Scholes model does not account non-Markovian property and volatility smile or skew although asset price might depend on the past movement of the asset price and real market data can find a non-flat structure of the implied volatility surface. So, in this paper, we formulate an underlying asset model by adding a delayed structure to the constant elasticity of variance (CEV) model that is one of renowned alternative models resolving the geometric issue. However, it is still one factor volatility model which usually does not capture full dynamics of the volatility showing discrepancy between its predicted price and market price for certain range of options. Based on this observation we combine a stochastic volatility factor with the delayed CEV structure and develop a delayed hybrid model of stochastic and local volatilities. Using both a martingale approach and a singular perturbation method, we demonstrate the delayed CEV correction effects on the European vanilla option price under this hybrid volatility model as a direct extension of our previous work [12].

Research of Resource Scheduling based on ACA-GA in the Cloud Computing

How to better conduct research resource scheduling has long been a research direction of cloud computing. This paper, aiming at slow convergence and easiness of falling local optimum of ant colony algorithm,has integrated genetic algorithm into the ant colony algorithm and obtained hybrid algorithm (ACA -GA); in the initial solution of the ant colony algorithm, it has adopted selection, crossover and mutation operations of genetic algorithm to obtain an effective initial solution; secondly, it has used the perception threshold of ant colony algorithm path setting to regulate individual selection optimal path; finally, it has improved volatile factor so as to significantly improve the updating efficiency of pheromone. The algorithm in the paper proved that the performance of the algorithm has been also significantly improved through classical test functions. Cloudsim platform shows that, the algorithm above mentioned reduces the time and cost spent in resource scheduling of, hence has some promotional value.

Integrating risk literacy into brand management

Purpose The purpose of this paper is to integrate brand equity into companies’ overall risk assessment by suggesting a methodology of evaluating the relevant aspects of brand risk. Design/methodology/approach Based on a theoretical framework which discriminates between brand assets, brand strength and brand value, this paper set two alternative directions that can be followed to assess brand risk: a financial direction, which accounts on brand value, and a marketing direction, which stresses on brand assets and brand strength. Following the latter one, this paper provides mathematical formulas which contain specific factors of volatility that can be integrated in a future scoring system. Findings This paper proposes four major risks that need to be considered when evaluating brand assets risk: reputational risk, presence risk, loyalty risk and halo effect risk. This paper provides an evaluation methodology for each one. In addition, a 12-step implementation model is proposed as a managerial guideline for integrating brand risk in the companies’ risk management. Originality/value This paper emphasizes the importance of considering brand equity as a potential source of risk and thus integrating it into risk management. Also, we continue Abrahams’ pioneer work on adding risk literacy into brand management.

Reasons for Journal Impact Factor Changes: Influence of Changing Source Items.

Both the concept and the application of the impact factor (IF) have been subject to widespread critique, including concerns over its potential manipulation. This study provides a systematic analysis of significant journal Impact Factor changes, based on the relative contribution of either one or both variables of the IF equation (i.e. citations / articles as the numerator / denominator of the quotient). A cohort of JCR-listed journals which faced the most dramatic absolute IF changes between 2013 and 2014 (ΔIF ≥ 3.0, n = 49) was analyzed for the causes resulting in IF changes that theses journals have experienced in the last five years. Along with the variation by number of articles and citations, this analysis includes the relative change of both variables compared to each other and offers a classification of `valid`and `invalid`scenarios of IF variation in terms of the intended goal of the IF to measure journal quality. The sample cohort features a considerable incidence of IF increases (18%) which are qualified as `invalid`according to this classification because the IF increase is merely based on a favorably changing number of articles (denominator). The results of this analysis point out the potentially delusive effect of IF increases gained through effective shrinkage of publication output. Therefore, careful consideration of the details of the IF equation and possible implementation of control mechanisms versus the volatile factor of number of articles may help to improve the expressiveness of this metric.

Task-free MRI predicts individual differences in brain activity during task performance.

When asked to perform the same task, different individuals exhibit markedly different patterns of brain activity. This variability is often attributed to volatile factors, such as task strategy or compliance. We propose that individual differences in brain responses are, to a large degree, inherent to the brain and can be predicted from task-independent measurements collected at rest. Using a large set of task conditions, spanning several behavioral domains, we train a simple model that relates task-independent measurements to task activity and evaluate the model by predicting task activation maps for unseen subjects using magnetic resonance imaging. Our model can accurately predict individual differences in brain activity and highlights a coupling between brain connectivity and function that can be captured at the level of individual subjects.

Constructing Structural Equation Model Rule-Based Fuzzy System with Genetic Algorithm

The present study uses the structural equation model (SEM) to analyze the correlations between various economic indices pertaining to latent variables, such as the New Taiwan Dollar (NTD) value, the United States Dollar (USD) value, and USD index. In addition, a risk factor of volatility of currency returns is considered to develop a risk-controllable fuzzy inference system. The rational and linguistic knowledge-based fuzzy rules are established based on the SEM model and then optimized using the genetic algorithm. The empirical results reveal that the fuzzy logic trading system using the SEM indeed outperforms the buy-and-hold strategy. Moreover, when considering the risk factor of currency volatility, the performance appears significantly better. Remarkably, the trading strategy is apparently affected when the USD value or the volatility of currency returns shifts into either a higher or lower state.

Does idiosyncratic volatility predict future growth of the Australian economy?

Purpose – This paper aims to examine whether idiosyncratic volatility and other asset pricing factors predict growth rates of the ten Australian economic indicators. Design/methodology/approach – The authors use the Liew and Vassalou (2000) model augmented with an idiosyncratic volatility factor to investigate the issue. Findings – Using regression analysis, the authors find that the asset pricing factors can be used to predict the growth rates for eight out of the ten economic indicators. Moreover, using portfolio performance analysis, the authors find that high returns of size factor and a book-to-market factor portfolios precede periods of good macroeconomic states, whereas high returns of HIMLI portfolios precede periods of bad macroeconomic states. Originality/value – To the authors’ knowledge, the relationship between idiosyncratic volatility and Australian economic growth has not been investigated explicitly in the literature.

Crop Insurance Decisions for 2016

The Risk Management Agency (RMA) has now concluded its price discovery period used to determined final prices and volatility factors for federally sponsored corn and soybean crop insurance products for 2016. For the majority of the Cornbelt, the approved Projected Price (PP) for corn is expected to be $3.86 and the volatility factor is .18. For soybeans, the Projected Price is expected to be $8.85 with a volatility factor of .12. For comparison, the 2015 prices and (volatility factors) were $4.15 (.21) and $9.73 (.16) for corn and soybeans respectively. The Projected Prices are used to determine a guarantee revenue based on futures prices, and do not reflect local basis. The Projected Price for corn is determined by averaging the closing December futures price during the trading days of February, and for soybeans by averaging the November Futures closing prices during February. The volatility factors are determined by an average of the most recent five trading days' implied volatility estimates, scaled for the interval of time from now until the middle of October -the month during which average prices are used to determine Harvest Prices. For both corn and soybeans, the projected prices are lower than in 2015 which in turn were lower than 2014 and so forth, resulting in the lowest average guarantees for the past 6 years for corn and for 4 years for soybeans. The volatility factor summarizes the market’s estimates of the likelihood for price movements of various magnitudes, and has corresponding impacts on premiums paid for Revenue and Harvest Price related products. The relatively low volatilities will result in lower premiums in 2016, all else equal.

Particulate matter indices using fuel smoke point for vehicle emissions with gasoline, ethanol blends, and butanol blends

Recent studies have led to significant improvements in the prediction of particulate matter (PM) emissions from gasoline vehicles based on differences in fuel composition. This is an issue that has taken on increased importance recently given the introduction of gasoline direct injection (GDI) technology to improve fuel economy and the challenges it has meeting new stringent PM emissions standards. One productive effort in this area is the PM Index (PMI) developed by Aikawa and collaborators. This approach relates the effects of fuel composition on its PM forming potential. The present work explores the use of experimental smoke point measurements and various fuel volatility metrics to better characterize these effects. Smoke point (SP) values as scaled by the Oxygen Extended Sooting Index (OESI) incorporate all fuel molecular structural effects known to have an impact on soot formation, including branching, degree of saturation, carbon chain length, and oxygenate functional group effects. A strong correlation is found for vehicle-level particle number (PN) and PM emissions with SP, OESI and PMI. Fuel volatility effects are considered, including characteristics derived from molecular weight, distillation, vapor pressure, and heat of vaporization. For hydrocarbon blends, no volatility factor significantly improved the correlation as compared to PMI, SP, or OESI alone. However, inclusion of a heat of vaporization term with OESI better matched the emissions trend for ethanol–gasoline blends. Sooting tendencies for n-butanol- and isobutanol-gasoline blends exhibited trends similar to those with ethanol.

Forecasting the term structure of volatility of crude oil price changes

This is a pioneering effort to test the comparative performance of two competing models for out-of-sample forecasting the term structure of volatility of crude oil price changes employing both symmetric and asymmetric evaluation criteria. Under symmetric error statistics, our empirical model using the estimated growth factor of volatility through time is overall superior, and it beats in most cases the benchmark model of the square-root-of-time (T) for holding periods between one and 250 days. Under asymmetric error statistics, if over-prediction (under-prediction) of volatility is undesirable, the empirical (benchmark) model is consistently superior. Relative performance of the empirical model is much higher for holding periods up to fifty days.

The Implied Convexity of VIX Futures

An important component of theoretical CBOE Volatility Index (VIX) futures prices is a term correcting for the negative convexity of the square root function by subtracting from the forward-starting variance swap rate an estimate of the future volatility of VIX futures prices. In the same fashion that an index option’s traditional implied volatility can be viewed as an aggregate market consensus of future realized volatility, this convexity value can be viewed as an aggregate market consensus of future volatility of volatility. This article examines the predictive properties and features of this convexity adjustment needed to value VIX futures prices by extracting it from the relationship between observed VIX futures prices and the corresponding spot option market prices used to compute the forward-starting variance swap rate. The authors find that implied convexity levels can indeed be used to forecast the future volatility of VIX futures prices, even though implied convexity consistently underestimates future realized VIX futures variance. They also show that implied convexity can at times violate strict theoretical conditions by being negative, although we are able to rule out arbitrage opportunities. Finally, they examine the properties of this implied convexity adjustment, both as a time series and with respect to various market volatility factors with which they find positive and statistically significant relations.

Company and asset specific risk factors: Short overview on high yield bonds

This paper discusses the theory that risk factors divide to the company specific and asset specific risk factors. The first group affects to the expected value of an equity of a company whereas the second only to the positive cash outflows for a specific asset. I find that equity market, value, and quality factors are indeed possible company specific risk factors with influence on an expected equity of a company and dividend and volatility factors are possible stock specific risk factors affecting positively to dividends and other cash payments from a company to shareholders. These results are statistically significant and important for our understanding of risk factors and their characteristics.

Zmienność modelu biznesowego w kontekście innowacyjności organizacji / Business model volatility – context of the innovativeness of organization

The aim of the article is to present factors that enable to determine the degree of the business model volatility and synthetic discussion of these factors in the context of organizational innovation. The first part of the paper contains a brief justification for choosing A. Osterwalders business model framework as a basis for reflection on business model volatility. In the main part of the study, a proposal of both quantitative and qualitative factors for establishing the volatility of the business model is described, which is the result of empirical research. The article closes with a discussion on relations between the modification of the company's business model elements and organization innovativeness. The concept of business models volatility factors presented in the article should facilitate the planning and implementation of changes in the business model and increase innovativeness of that, as well as to contribute to improving the innovation within organization and its effectiveness.

Causality between sovereign, quasi-sovereign credit risks and global volatility: The case of Russia

The article examines causalities between sovereign, most important quasi-sovereign CDS prices (Gazprom, VTB, Sberbank) for Russia and the global volatility factor embedded in the VIX index dynamics. The analysis refers to the post-bailout period in this major emerging economy (May 2009–July 2013). The causalities are assessed in the time (the Hong test) and frequency (the Breitung–Candelon test) domains. The VIX index dynamics has a strong impact on all Russian CDS, but also receives a non-negligible feedback from them. The sovereign and quasi-sovereign CDS prices exhibit a strong causal connectedness in both domains, with the impact of the quasi-sovereigns (in particular, that of banks) getting more pronounced in the longer run, i.e. over longer time horizons and at lower frequencies. High foreign exposure of the quasi-sovereigns, explicit/implicit public guarantees and the holdings of the Russian sovereign debt on their balances underlie the causalities. Given the systemic role that the government-controlled entities play in the Russian economy, this may lead to the “too big to save” effect with negative implications for financial and fiscal stability.

How Smart is Smart Beta Investing? Evidence from Australia

"Smart beta" investing is an alternative to the traditional active and passive approaches to funds management, whereby investors adopt a systematic method that provides exposure to factors that are argued to be related with expected returns at low cost. Therefore, the question of how smart is smart beta investing can be empirically examined by testing the performance of those factors that underlie smart beta portfolios. We use a long time-series of data and show that the value, momentum, low volatility and quality factors all generate positive abnormal returns in the Australian equity market. Rather than ranking these factors based on relative performance, we argue that the optimal approach to smart beta investing is to diversify across these factors, given the low correlations between factor returns. Our results provide important implications for the Australian funds management industry. First, while this study does not examine the specific strategies applied by smart beta fund managers, the evidence presented provides a justification for the application of smart beta as a low cost alternative to active investment. Second, given evidence that multiple factors explain equity returns, multi-factor models should be used to measure active portfolio manager performance in order to distinguish pure alpha from abnormal returns generated due to smart beta exposure.

Amines as occupational hazards for visual disturbance

Various amines, such as triethylamine and N,N-dimethylethylamine, have been reported to cause glaucopsia in workers employed in epoxy, foundry, and polyurethane foam industries. This symptom has been related to corneal edema and vesicular collection of fluid within the corneal subepithelial cells. Exposure to amine vapors for 30 min to several hours leads to blurring of vision, a blue-grey appearance of objects, and halos around lights, that are probably reversible. Concentration-effect relationships have been established. The visual disturbance is considered a nuisance, as it could cause onsite accidents, impair work efficiency, and create difficulties in driving back home. Occupational exposure limits have been established for some amines, but there is shortage of criteria. Volatility factors, such as vapor pressure, should be considered in industrial settings to prevent human ocular risks, while trying to reduce levels of hazardous amines in the atmosphere.

The common factor in idiosyncratic volatility: Quantitative asset pricing implications

We show that firms׳ idiosyncratic volatility obeys a strong factor structure and that shocks to the common idiosyncratic volatility (CIV) factor are priced. Stocks in the lowest CIV-beta quintile earn average returns 5.4% per year higher than those in the highest quintile. The CIV factor helps to explain a number of asset pricing anomalies. We provide new evidence linking the CIV factor to income risk faced by households. Our findings are consistent with an incomplete markets heterogeneous agent model. In the model, CIV is a priced state variable because an increase in idiosyncratic firm volatility raises the average household׳s marginal utility. The calibrated model matches the high degree of co-movement in idiosyncratic volatilities, the CIV-beta return spread, and several other asset price moments.