Journal articles: 'VARMA model'

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Al-Nasser, A. H., and L. T. Abdullah. "The Estimators of Vector Autoregressive Moving Avarege Model VARMA of Lower Order: VARMA (0,1), ARMA (1,0), VARMA (1,1), VARMA (1,2), VARMA (2,1), VARMA (2,2) with Forecasting." Journal of Physics: Conference Series 1818, no. 1 (March 1, 2021): 012145. http://dx.doi.org/10.1088/1742-6596/1818/1/012145.

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Sfetcu, Răzvan-Cornel, Sorina-Cezarina Sfetcu, and Vasile Preda. "Ordering Awad–Varma Entropy and Applications to Some Stochastic Models." Mathematics 9, no. 3 (January 31, 2021): 280. http://dx.doi.org/10.3390/math9030280.

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We consider a generalization of Awad–Shannon entropy, namely Awad–Varma entropy, introduce a stochastic order on Awad–Varma residual entropy and study some properties of this order, like closure, reversed closure and preservation in some stochastic models (the proportional hazard rate model, the proportional reversed hazard rate model, the proportional odds model and the record values model).

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Wei, Ching-Chun. "Empirical Analysis of “Volatilitysurprise” between Dollar Exchange Rate and CRB Commodity Future Markets." International Journal of Economics and Finance 8, no. 9 (August 24, 2016): 117. http://dx.doi.org/10.5539/ijef.v8n9p117.

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This paper used the five multivariate GARCH models (including BEKK, CCC, DCC, VARMA-CCC and VARMA-DCC) to analyze the mean and volatility interaction of volatility surprise between US dollar exchange and CRB future index (including agricultural, energy, commodity and precious metal equity index). The empirical findings exhibit that significant own short and long-term persistence effects and the cross-markets volatility surprise spillover short and long-term persistence effects between dollar exchange rate and CRB commodity future equity index markets in five multivariate GARCH models. Besides that, the residual diagnostic test indicated that VARMA-DCC models is the best suitable model to modeling the dollar exchange rate with CRB commodity equity index.

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Olusola-Makinde, Olubukola O., and Olusola S. Makinde. "COVID-19 incidence and mortality in Nigeria: gender based analysis." PeerJ 9 (February 12, 2021): e10613. http://dx.doi.org/10.7717/peerj.10613.

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Background Coronavirus Disease 2019 (COVID-19) has been surging globally. Risk strata in medical attention are of dynamic significance for apposite assessment and supply distribution. Presently, no known cultured contrivance is available to fill this gap of this pandemic. The aim of this study is to develop a predictive model based on vector autoregressive moving average (VARMA) model of various orders for gender based daily COVID-19 incidence in Nigeria. This study also aims to proffer empirical evidence that compares incidence between male and female for COVID-19 risk factors. Methods Wilcoxon signed-rank test is employed to investigate the significance of the difference in the gender distributions of the daily incidence. A VARMA model of various orders is formulated for the gender based daily COVID-19 incidence in Nigeria. The optimal VARMA model is identified using Bayesian information criterion. Also, a predictive model based on univariate autoregressive moving average model is formulated for the daily death cases in Nigeria. Fold change is estimated based on crude case-fatality risk to investigate whether there is massive underreporting and under-testing of COVID-19 cases in Nigeria. Results Daily incidence is higher in males on most days from 11 April 2020 to 12 September 2020. Result of Wilcoxon signed-rank test shows that incidence among male is significantly higher than female (p-value < 2.22 × 10−16). White neural network test shows that daily female incidence is not linear in mean (p-value = 0.00058746) while daily male incidence is linear in mean (p-value = 0.4257). McLeod-Li test shows that there is autoregressive conditional heteroscedasticity in the female incidence (Maximum p-value = 1.4277 × 10−5) and male incidence (Maximum p-value = 9.0816 × 10−14) at 5% level of significance. Ljung-Box test (Tsay, 2014) shows that the daily incidence cases are not random (p-value=0.0000). The optimal VARMA model for male and female daily incidence is VARMA (0,1). The optimal model for the Nigeria’s daily COVID-19 death cases is identified to be ARIMA (0,1,1). There is no evidence of massive underreporting and under-testing of COVID-19 cases in Nigeria. Conclusions Comparison of the observed incidence with fitted data by gender shows that the optimal VARMA and ARIMA models fit the data well. Findings highlight the significant roles of gender on daily COVID-19 incidence in Nigeria.

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USMAN, Mustofa, N. INDRYANI, WARSONO A., and AMANTO WAMILIANA. "DYNAMIC MODELING OF TIME SERIES DATA USING BEKK-GARCH MODEL." Periódico Tchê Química 17, no. 36 (December 20, 2020): 1186–98. http://dx.doi.org/10.52571/ptq.v17.n36.2020.1202_periodico36_pgs_1186_1198.pdf.

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The Vector Autoregressive Moving Average (VARMA) model is one of the models that is often used in modeling multivariate time series data. In time-series data of economics, especially data return, they usually have high fluctuations in some periods, so the return volatility is unstable. In modeling data return of share prices ADRO and ITMG, the behavior of high volatility will be considered. This study aims to find the best model that fits the data return of share price of the energy companies of PT Adaro Energy Tbk (ADRO) and PT Indo Tambangraya Megah Tbk (ITMG), to analyze the behavior of impulse response of the variables data return ADRO and ITMG, to analyze the granger causality test, and to forecast the next 12 periods. Based on the selection of the best model using the criteria of AICC, HQC, AIC, and SBC, it was found that the VARMA (2.2) -GARCH (1.1) model is the best one for the data in this study. The model VARMA(2,2)-GARCH (1,1) is then written as a univariate model. For the univariate ADRO model, the test statistics F = 4,73 and P-value = 0,0084, which indicates the model is very significant; and for the univariate ITMG model, the test statistics is F = 5,82 and P-value 0,0001, which indicates the model is significant. Based on the best model selected, the impulse response, Granger causality test, and forecasting for the next 12 periods are discussed.

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Raghavan, Mala, George Athanasopoulos, and Param Silvapulle. "Canadian monetary policy analysis using a structural VARMA model." Canadian Journal of Economics/Revue canadienne d'économique 49, no. 1 (February 2016): 347–73. http://dx.doi.org/10.1111/caje.12200.

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Kamarianakis, Yiannis, and Poulicos Prastacos. "Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches." Transportation Research Record: Journal of the Transportation Research Board 1857, no. 1 (January 2003): 74–84. http://dx.doi.org/10.3141/1857-09.

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Several univariate and multivariate models have been proposed for performing short-term forecasting of traffic flow. Two different univariate [historical average and ARIMA (autoregressive integrated moving average)] and two multivariate [VARMA (vector autoregressive moving average) and STARIMA (space–time ARIMA)] models are presented and discussed. A comparison of the forecasting performance of these four models is undertaken with data sets from 25 loop detectors located in major arterials in the city of Athens, Greece. The variable under study is the relative velocity, which is the traffic volume divided by the road occupancy. Although the specification of the network’s neighborhood structure for the STARIMA model was relatively simple and can be further refined, the results obtained indicate a comparable forecasting performance for the ARIMA, VARMA, and STARIMA models. The historical average model could not cope with the variability of the data sets at hand.

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Shukla, P. K., and L. Stenflo. "Linear and nonlinear coupled Alfvén-Varma modes." Journal of Plasma Physics 40, no. 3 (December 1988): 473–79. http://dx.doi.org/10.1017/s0022377800013441.

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Starting from the particle continuity equation and the guiding-centre model for the particle velocity, using an equation of state in the form of the conservation of the ion magnetic moment, as well as Ohm's and Ampère's laws, a set of four coupled nonlinear equations has been derived to investigate the properties of magnetic turbulence in plasmas with hot ions and cold electrons. It is shown that finite-β effects can cause a linear coupling between the Alfvén and Varma modes in a weakly non-uniform plasma immersed in an inhomogeneous magnetic field. In the nonlinear regime the stationary solutions of the four field equations are found to be double vortices. Implications of our results for enhanced magnetic fluctuation and anomalous transport in a mirror reactor are pointed out.

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Wei, Ching-Chun. "Modeling and Analyzing the Mean and Volatility Relationship between Electricity Price Returns and Fuel Market Returns." International Journal of Economics and Finance 8, no. 7 (June 23, 2016): 55. http://dx.doi.org/10.5539/ijef.v8n7p55.

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<p>This paper has two objectives. First, we apply the symmetric and asymmetric VAR(1)-BEKK-MGARCH(1.1), VAR(1)-CCC-MGARCH(1,1), VAR(1)-DCC-MGARCH, VAR(1)-VARMA-CCC-MGARCH and VAR(1)- VARMA-DCC-MGARCH models to explore the return and volatility interactions among electricity and other fuel price markets(oil, natural gas, and coal). Second, this paper investigates the importance of not only volatility spillover among energy markets, but also the asymmetric effects of negative and positive shockson the conditional variance of modeling one energy market’s volatility upon the returns of future prices within and across other energy markets. The empirical results display that these models do capture the dynamic structure of the return interactions and volatility spillovers and exhibit statistical significance for own past mean and volatility short-and long-run persistence effects, while there are just a few cross-market effects for each model.</p>

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Wu, Edward Ming-Yang, and Shu-Lung Kuo. "VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan." Atmosphere 11, no. 10 (October 14, 2020): 1096. http://dx.doi.org/10.3390/atmos11101096.

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This study adopted the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to analyze seven air pollutants (or the seven variables in this study) from ten air quality monitoring stations in the Kaohsiung–Pingtung Air Pollutant Control Area located in southern Taiwan. Before the verification analysis of the EGARCH model is conducted, the air quality data collected at the ten air quality monitoring stations in the Kaohsiung–Pingtung area are classified into three major factors using the factor analyses in multiple statistical analyses. The factors with the most significance are then selected as the targets for conducting investigations; they are termed “photochemical pollution factors”, or factors related to pollution caused by air pollutants, including particulate matter with particles below 10 microns (PM10), ozone (O3) and nitrogen dioxide (NO2). Then, we applied the Vector Autoregressive Moving Average-EGARCH (VARMA-EGARCH) model under the condition where the standardized residual existed in order to study the relationships among three air pollutants and how their concentration changed in the time series. By simulating the optimal model, namely VARMA (1,1)-EGARCH (1,1), we found that when O3 was the dependent variable, the concentration of O3 was not affected by the concentration of PM10 and NO2 in the same term. In terms of the impact response analysis on the predictive power of the three air pollutants in the time series, we found that the asymmetry effect of NO2 was the most significant, meaning that NO2 influenced the GARCH effect the least when the change of seasons caused the NO2 concentration to fluctuate; it also suggested that the concentration of NO2 produced in this area and the degree of change are lower than those of the other two air pollutants. This research is the first of its kind in the world to adopt a VARMA-EGARCH model to explore the interplay among various air pollutants and reactions triggered by it over time. The results of this study can be referenced by authorities for planning air quality total quantity control, applying and examining various air quality models, simulating the allowable increase in air quality limits, and evaluating the benefit of air quality improvement.

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Hallin, Marc, and Davy Paindaveine. "Rank-based optimal tests of the adequacy of an elliptic VARMA model." Annals of Statistics 32, no. 6 (December 2004): 2642–78. http://dx.doi.org/10.1214/009053604000000724.

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Oral, Emrah, and Gazanfer Unal. "Co-movement of precious metals and forecasting using scale by scale wavelet transform." International Journal of Financial Engineering 04, no. 01 (March 2017): 1750007. http://dx.doi.org/10.1142/s2424786317500074.

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In this paper, a new approach is proposed to improve forecasting performances. We analyze the co-movement of precious metals (daily data of gold, silver and platinum starting from July, 2011) using multiple wavelet coherence and determine the movement dependencies on frequency–time space. The data is split into frequencies using scale by scale continuous wavelet transform. All three time series retaining the same frequency scale are (i) selected, (ii) inversed and (ii) forecasted using multivariate model, Vector Auto Regressive Moving Average (VARMA). We conclude that the efficiency of VARMA forecasting is substantially increased because of same frequency highly correlated time series obtained by using scale by scale wavelet transform. Moreover, the direction of price shift (increasing/decreasing trend) is prospected to an adequately distinguishable degree.

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GÜLERCE, MUSTAFA, and GAZANFER ÜNAL. "FORECASTING OF OIL AND AGRICULTURAL COMMODITY PRICES: VARMA VERSUS ARMA." Annals of Financial Economics 12, no. 03 (September 2017): 1750012. http://dx.doi.org/10.1142/s2010495217500129.

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The aim of this paper is to show that the estimates made with vector autoregressive–moving-average (ARMA) models based on the coherent time intervals of the multiple time series give more precise results than the univariate case. The previous literature on dynamic correlations (co-movement) in between food and energy prices has mixed results and mainly based on parametric approaches. Therefore, partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) methods are used, respectively, to uncover the coherency simultaneously for time and frequency domains. In our study; world oil, corn, soybeans, wheat and sugar prices are examined instead of the return and volatility relationship between oil and agricultural commodities due to model-free approach of wavelet analysis.

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Gouriéroux, Christian, Alain Monfort, and Jean-Paul Renne. "Identification and Estimation in Non-Fundamental Structural VARMA Models." Review of Economic Studies 87, no. 4 (May 17, 2019): 1915–53. http://dx.doi.org/10.1093/restud/rdz028.

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Abstract The basic assumption of a structural vector autoregressive moving average (SVARMA) model is that it is driven by a white noise whose components are uncorrelated or independent and can be interpreted as economic shocks, called “structural” shocks. When the errors are Gaussian, independence is equivalent to non-correlation and these models face two identification issues. The first identification problem is “static” and is due to the fact that there is an infinite number of linear transformations of a given random vector making its components uncorrelated. The second identification problem is “dynamic” and is a consequence of the fact that, even if a SVARMA admits a non-invertible moving average (MA) matrix polynomial, it may feature the same second-order dynamic properties as a VARMA process in which the MA matrix polynomials are invertible (the fundamental representation). The aim of this article is to explain that these difficulties are mainly due to the Gaussian assumption, and that both identification challenges are solved in a non-Gaussian framework if the structural shocks are assumed to be instantaneously and serially independent. We develop new parametric and semi-parametric estimation methods that accommodate non-fundamentalness in the MA dynamics. The functioning and performances of these methods are illustrated by applications conducted on both simulated and real data.

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Khodaparasti, Ramin Bashir, and Samad Moslehi. "Application of the Varma Model for Sales Forecast: Case of Urmia Gray Cement Factory." Timisoara Journal of Economics and Business 7, no. 1 (June 1, 2014): 89–101. http://dx.doi.org/10.2478/tjeb-2014-0005.

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Abstract To forecast sales as reliably as possible is one of the most important issues in every business trade. Therefore, in recent years different models have been suggested to deal with this issue. One efficient model is the time series model. This study applies a multivariate time series model to forecast Urmia Gray Cement Factory's sales volume and more importantly, to propose an effective model to be used by other cement factories to predict their sales volume. The two independent variables of costs and revenues and the dependent variable of sales were used in the present study. Results of the study indicated the two independent variables had a positive and direct relationship with sales volume forecast.

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MASOOD, W., and H. SALEEM. "Coupling of the Okuda–Dawson model with a shear current-driven wave and the associated instability." Journal of Plasma Physics 79, no. 6 (December 2013): 1129–31. http://dx.doi.org/10.1017/s0022377813001037.

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AbstractIt is pointed out that the Okuda–Dawson mode can couple with the newly proposed current-driven wave. It is also shown that the Shukla–Varma mode can couple with these waves if the density inhomogeneity is taken into account in a plasma containing stationary dust particles. A comparison of several low-frequency electrostatic waves and instabilities driven by shear current and shear plasma flow in an electron–ion plasma with and without stationary dust is also presented.

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Love, Barnaby S., Adrian J. Matthews, and Gareth J. Janacek. "Real-Time Extraction of the Madden–Julian Oscillation Using Empirical Mode Decomposition and Statistical Forecasting with a VARMA Model." Journal of Climate 21, no. 20 (October 15, 2008): 5318–35. http://dx.doi.org/10.1175/2008jcli1977.1.

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Abstract A simple guide to the new technique of empirical mode decomposition (EMD) in a meteorological–climate forecasting context is presented. A single application of EMD to a time series essentially acts as a local high-pass filter. Hence, successive applications can be used to produce a bandpass filter that is highly efficient at extracting a broadband signal such as the Madden–Julian oscillation (MJO). The basic EMD method is adapted to minimize end effects, such that it is suitable for use in real time. The EMD process is then used to efficiently extract the MJO signal from gridded time series of outgoing longwave radiation (OLR) data. A range of statistical models from the general class of vector autoregressive moving average (VARMA) models was then tested for their suitability in forecasting the MJO signal, as isolated by the EMD. A VARMA (5, 1) model was selected and its parameters determined by a maximum likelihood method using 17 yr of OLR data from 1980 to 1996. Forecasts were then made on the remaining independent data from 1998 to 2004. These were made in real time, as only data up to the date the forecast was made were used. The median skill of forecasts was accurate (defined as an anomaly correlation above 0.6) at lead times up to 25 days.

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Li, Yuanyuan, and Dietmar Bauer. "Modeling I(2) Processes Using Vector Autoregressions Where the Lag Length Increases with the Sample Size." Econometrics 8, no. 3 (September 17, 2020): 38. http://dx.doi.org/10.3390/econometrics8030038.

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In this paper the theory on the estimation of vector autoregressive (VAR) models for I(2) processes is extended to the case of long VAR approximation of more general processes. Hereby the order of the autoregression is allowed to tend to infinity at a certain rate depending on the sample size. We deal with unrestricted OLS estimators (in the model formulated in levels as well as in vector error correction form) as well as with two stage estimation (2SI2) in the vector error correction model (VECM) formulation. Our main results are analogous to the I(1) case: We show that the long VAR approximation leads to consistent estimates of the long and short run dynamics. Furthermore, tests on the autoregressive coefficients follow standard asymptotics. The pseudo likelihood ratio tests on the cointegrating ranks (using the Gaussian likelihood) used in the 2SI2 algorithm show under the null hypothesis the same distributions as in the case of data generating processes following finite order VARs. The same holds true for the asymptotic distribution of the long run dynamics both in the unrestricted VECM estimation and the reduced rank regression in the 2SI2 algorithm. Building on these results we show that if the data is generated by an invertible VARMA process, the VAR approximation can be used in order to derive a consistent initial estimator for subsequent pseudo likelihood optimization in the VARMA model.

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Liu, Chang, Chuo Chang, and Zhe Chang. "Maximum Varma Entropy Distribution with Conditional Value at Risk Constraints." Entropy 22, no. 6 (June 16, 2020): 663. http://dx.doi.org/10.3390/e22060663.

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It is well known that Markowitz’s mean-variance model is the pioneer portfolio selection model. The mean-variance model assumes that the probability density distribution of returns is normal. However, empirical observations on financial markets show that the tails of the distribution decay slower than the log-normal distribution. The distribution shows a power law at tail. The variance of a portfolio may also be a random variable. In recent years, the maximum entropy method has been widely used to investigate the distribution of return of portfolios. However, the mean and variance constraints were still used to obtain Lagrangian multipliers. In this paper, we use Conditional Value at Risk constraints instead of the variance constraint to maximize the entropy of portfolios. Value at Risk is a financial metric that estimates the risk of an investment. Value at Risk measures the level of financial risk within a portfolio. The metric is most commonly used by investment bank to determine the extent and occurrence ratio of potential losses in portfolios. Value at Risk is a single number that indicates the extent of risk in a given portfolio. This makes the risk management relatively simple. The Value at Risk is widely used in investment bank and commercial bank. It has already become an accepted standard in buying and selling assets. We show that the maximum entropy distribution with Conditional Value at Risk constraints is a power law. Algebraic relations between the Lagrangian multipliers and Value at Risk constraints are presented explicitly. The Lagrangian multipliers can be fixed exactly by the Conditional Value at Risk constraints.

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Gong, Xiao-Li, Xi-Hua Liu, Xiong Xiong, and Xin-Tian Zhuang. "Non-Gaussian VARMA model with stochastic volatility and applications in stock market bubbles." Chaos, Solitons & Fractals 121 (April 2019): 129–36. http://dx.doi.org/10.1016/j.chaos.2019.01.040.

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Gupta, Shakti Kumar, Jitendar Sharma, Vikas Varma, and BS Anand. "Designing and Application of a Renewable Energy Model for a Tertiary Care Research Hospital." International Journal of Research Foundation of Hospital and Healthcare Administration 2, no. 1 (2014): 57–61. http://dx.doi.org/10.5005/jp-journals-10035-1016.

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ABSTRACT Renewable energy can be harnessed from Solar and Wind energy to augment and substitute for the conventional energy for Healthcare institutes. This is economical and plentily available. This case study aims to design a Renewable energy model. Here various structural data and available expenditure has been utilized from a tertiary care hospital. How to cite this article Gupta SK, Sharma J, Varma V, Anand BS. Designing and Application of a Renewable Energy Model for a Tertiary Care Research Hospital. Int J Res Foundation Hosp Healthc Adm 2014;2(1):57-61.

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Wu, Edward Ming-Yang, and Shu-Lung Kuo. "Study on Air Pollution Behavior of VOCs with Photochemical Monitoring Stations Using EGARCH Model in Southern Taiwan." Atmosphere 12, no. 9 (September 10, 2021): 1167. http://dx.doi.org/10.3390/atmos12091167.

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This study adopted the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model to examine the 10 ozone precursors of the highest concentrations among the 54 that were assessed over a number of years at the four photochemical assessment monitoring stations (PAMSs) in the Kaohsiung–Pingtung Area in Taiwan. First, the 10 ozone precursors, which were all volatile organic compounds (VOCs), were analyzed using the factor analyses in multiple statistical analyses that had the most significant impact on the area’s ozone formation: mobile pollution factor, which included 1,2,4-Trimethylbenzene (C9H12), toluene (C7H8), and Isopropyl benzene (C9H12). Then, taking into consideration that the number sequences might be affected by standardized residuals, this study applied the vector autoregressive moving average-EGARCH (VARMA-EGARCH) model to analyze the correlation between the three VOCs under different polluting activities. The VARMA-EGARCH model in this research included dummy variables representing changing points of variance structures in the variance formula to predict the conditional variance. This process proved able to effectively estimate the relevant coefficients of the three VOCs’ dynamic conditions that changed with time. The model also helped to prevent errors from occurring when estimating the conditional variance. Based on the testing results, this study determined the VARMA(2,1)-EGARCH(1,0) as the most suitable model for exploring the correlation between the three VOCs and meteorological phenomena, as well as the interplay between them in regard to interaction and formation. With the most representative of the three, toluene (TU), as the dependent variable and 1,2,4-Trimethylbenzene (TB) and Isopropyl benzene (IB) as the independent variables, this study found it impossible to calculate the TU concentration with TB and IB concentrations in the same period; estimations of TB and IB concentrations with a period of lag time were required because TU was mainly contributed by automobiles and motorcycles in Kaohsiung. TB and IB resulted from other stationary pollution sources in the region besides cars and motorbikes. When TU was evenly distributed and stayed longer in the atmosphere, the TB and IB concentrations were lower, so distribution conditions and concentrations could not be used to effectively estimate the concentration of toluene. This study had to wait until the next period, or when stationary pollution sources started producing TB and IB of higher concentrations during the daytime, in order to estimate the TU concentrations in a better photochemical situation.

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Petrali, J. P., M. Henein, A. H. Ali, P. S. Devamanoharan, T. A. Hamilton, and S. D. Varma. "Morphological Correlates of the Protection Afforded By Varma Mixture In Rat Cornea Exposed to Half Mustard (Cees)." Microscopy and Microanalysis 5, S2 (August 1999): 1178–79. http://dx.doi.org/10.1017/s1431927600019218.

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Whole body exposure to the chemical warfare agent, mustard gas, bis-(2-chloroethyl) sulfide, or its laboratory model compound, half mustard, 2-chloroethyl ethyl sulfide (CEES), induces cutaneous, respiratory and ocular impairments. Of these, ocular damage causes the most immediate incapacitation with initial symptoms evident within minutes. This incapacitation is a result of irritation and edema of eyelids, conjunctiva and especially cornea. Development of corneal epithelial lesions and edema leads to deterioration of corneal transmissive and refractive properties with untoward effects on visual acuity. Heretofore, there has been no specific pretreatment, or antidotal therapy for mustard gas-induced ocular impairment. In the present study, we describe morphological correlates of the apparent attenuation of such damage by a mixture compound developed by Varma et al. Varma mixture (VM) consists of compounds known to provide bio-energetic support, prevent oxidative stress, modulate membrane permeability and support tissue metabolism. The mustard agent used in this study was CEES.

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Hallin, Marc, and Davy Paindaveine. "Affine-invariant aligned rank tests for the multivariate general linear model with VARMA errors." Journal of Multivariate Analysis 93, no. 1 (March 2005): 122–63. http://dx.doi.org/10.1016/j.jmva.2004.01.005.

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Kim, Woncheol, and Kongkyun Ro. "A casual VARMA model analysis with an application to Canadian money and income data." Applied Economics 20, no. 9 (September 1988): 1167–83. http://dx.doi.org/10.1080/00036848800000122.

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SERLETIS, APOSTOLOS, and AKBAR SHAHMORADI. "VELOCITY AND THE VARIABILITY OF MONEY GROWTH: EVIDENCE FROM A VARMA, GARCH-M MODEL." Macroeconomic Dynamics 10, no. 5 (June 13, 2006): 652–66. http://dx.doi.org/10.1017/s1365100506050309.

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This paper uses recent advances in financial econometrics to test the Friedman hypothesis that money supply volatility Granger-causes velocity. Comparisons are made among simple-sum and Divisia velocity series at the M1 and M2 levels of monetary aggregation, using quarterly data from 1959:1 to 2004:3. The conclusion is that the Friedman hypothesis cannot be rejected if money supply volatility is modeled explicitly, using models that capture important volatility effects that previous work has ignored.

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Chen, Zhi Qing, and You Shen Xia. "A Fast Algorithm for Vector ARMA Parameter Estimation." Advanced Materials Research 433-440 (January 2012): 4475–81. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4475.

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In this paper, a fast algorithm for vector autoregressivemoving-average (ARMA) parameter estimation under noise environments is proposed. Based on an equivalent AR parameter model technique and a Yule-Walker equation technique, solving the parameter estimation problem of the VARMA model is well converted into solving linear equations. Therefore, the proposed algorithm has a lower computational complexity and a faster speed than conventional algorithms. Application examples with application to Lorenz systems confirm that the proposed algorithm can obtain a good solution.

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Abdolmaleki, Hamed, Hossein Asgharpur, and Jafar Hghighat. "Examination of Friedman’s Monetary Volatility Hypothesis in Iran: Asymmetric Approach From Extended VARMA, GARCHM Model." Journal of Research in Economic Modeling 7, no. 28 (September 1, 2017): 75–102. http://dx.doi.org/10.29252/jemr.7.28.75.

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Menéndez García, Luis Alfonso, Fernando Sánchez Lasheras, Paulino José García Nieto, Laura Álvarez de Prado, and Antonio Bernardo Sánchez. "Predicting Benzene Concentration Using Machine Learning and Time Series Algorithms." Mathematics 8, no. 12 (December 11, 2020): 2205. http://dx.doi.org/10.3390/math8122205.

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Benzene is a pollutant which is very harmful to our health, so models are necessary to predict its concentration and relationship with other air pollutants. The data collected by eight stations in Madrid (Spain) over nine years were analyzed using the following regression-based machine learning models: multivariate linear regression (MLR), multivariate adaptive regression splines (MARS), multilayer perceptron neural network (MLP), support vector machines (SVM), autoregressive integrated moving-average (ARIMA) and vector autoregressive moving-average (VARMA) models. Benzene concentration predictions were made from the concentration of four environmental pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particulate matter (PM10) and toluene (C7H8), and the performance measures of the model were studied from the proposed models. In general, regression-based machine learning models are more effective at predicting than time series models.

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Kudryavtseva, Tatiana, Evgeniia Kozlova, and Angi Skhvediani. "Empirical analysis of security papers of high-technology companies on the basis of a VARMA model." IOP Conference Series: Materials Science and Engineering 497 (April 2, 2019): 012045. http://dx.doi.org/10.1088/1757-899x/497/1/012045.

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Lung Kuo, Shu, and Ching Lin Ho. "The Assessment of Time Series for an Entire Air Quality Control District in Southern Taiwan Using GARCH Model." International Journal of Engineering & Technology 7, no. 3.19 (September 7, 2018): 119. http://dx.doi.org/10.14419/ijet.v7i3.19.16999.

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The General Autoregressive Conditional Heteroskedastic (GARCH) model and 10 ordinary air quality monitoring stations in the entire air quality control district in Kaohsiung-Pingtung were used in this study. First, the factor analysis results within multivariate statistics were employed to select the main factor that affects air pollution, namely, the photochemical pollution factor. The characteristics of the GARCH model were discussed in terms of asymmetric volatility among the three air pollutants (PM10, NO2, and O3) within the factor. In addition, this study also combined the multiple time series model VARMA to explore changes in the time series of the three air pollutants and to discuss their predictability.The results showed that, although the coefficient of the GARCH model was negative when estimating the variance equation, the conditional variance would always be positive after taking the logarithm. The results also suggested that the GARCH model was quite capable of capturing the asymmetric volatility. In other words, if the condition that pollution factors might be subject to seasonal changes or outliers generated by the human contamination is not considered, the GARCH model had very good ability to verify the results and make predictions, regardless of whether it adopted any of the three risk concepts: normal distribution, t-distribution, and generalized error distribution. For example, under the trend of time series temporal and spatial distribution in various pollution concentrations of photochemical factors, the optimal model VARMA(2,0,0)-GARCH(1,1) selected in this study was used to conduct time series predictability after the verification procedure. After capturing the last 50 entries of data on O3 concentrations in the sequence, the results showed that the predictability correlation (r) was 0.812, the predictability of NO2 was 0.783 and the predictability of PM10 was 0.759. It can be learned from the results that under the sequence of the GARCH model with strong asymmetric volatility, the residual values of these three sequences as white noise were quite evident, and there was also a high degree of correlation in predictability.

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Serletis, Apostolos, and Libo Xu. "THE ZERO LOWER BOUND AND CRUDE OIL AND FINANCIAL MARKETS SPILLOVERS." Macroeconomic Dynamics 22, no. 3 (May 23, 2016): 654–65. http://dx.doi.org/10.1017/s1365100516000365.

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We investigate mean and volatility spillovers between the crude oil market and the debt, stock, and foreign exchange markets. In doing so, we estimate a four-variable VARMA–GARCH model with a BEKK representation and also examine the possible effects of monetary policy at the zero lower bound by including a dummy variable in both the conditional mean and variance equations. We find that the crude oil market and the financial markets are tightly interconnected and that monetary policy at the zero lower bound has strengthened their linkages.

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Aftab, Hira, and A. B. M. Rabiul Alam Beg. "Does Time Varying Risk Premia Exist in the International Bond Market? An Empirical Evidence from Australian and French Bond Market." International Journal of Financial Studies 9, no. 1 (January 4, 2021): 3. http://dx.doi.org/10.3390/ijfs9010003.

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The presence of risk premium is an issue that weakens the rational expectation hypothesis. This paper investigates changing behavior of time varying risk premium for holding 10 year maturity bond using a bivariate VARMA-DBEKK-AGARCH-M model. The model allows for asymmetric risk premia, causality and co-volatility spillovers jointly in the global bond markets. Empirical results show significant asymmetric partial co-volatility spillovers and risk premium exist in the bond markets. The estimates of the bivariate risk premia show bi-directional causality exist between the Australia and France Bond markets. Overall results suggest nonexistence of pure rational expectation theory in the risk premium model. This information is useful for the agents’ strategic policy decision making in global bond markets.

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Gülerce, Mustafa, and Gazanfer Ünal. "Electricity price forecasting using multiple wavelet coherence method: Comparison of ARMA versus VARMA." International Journal of Financial Engineering 05, no. 01 (March 2018): 1850004. http://dx.doi.org/10.1142/s2424786318500044.

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The aim of this paper is to bring out a new perspective for Electricity price forecasting. Numerous studies have focused on forecasting the day-ahead or long-term price forecasting of electricity, rather than examine the relationship between energy commodities, by using various methods. Therefore, this study proposes a model-free approach for electricity price forcasting (EPF). The proposed approach is based on Partial Wavelet Coherency (PWC) and Multiple Wavelet Coherency (MWC) method. These methods are capable of uncovering the coherent time intervals simultaneously for time and frequency domains between the examined time series. VARMA uses the coherent time intervals and outperforms its univariate counterpart (ARMA), both in point and interval forecasting.

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Rahman, Sajjadur, and Apostolos Serletis. "Oil price uncertainty and the Canadian economy: Evidence from a VARMA, GARCH-in-Mean, asymmetric BEKK model." Energy Economics 34, no. 2 (March 2012): 603–10. http://dx.doi.org/10.1016/j.eneco.2011.08.014.

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Oral, Emrah, and Gazanfer Unal. "Dynamic correlation of Eastern and Western markets and forecasting: Scale-by-scale wavelet-based approach." International Journal of Financial Engineering 04, no. 04 (December 2017): 1750040. http://dx.doi.org/10.1142/s2424786317500402.

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In this paper, dynamic four-dimensional (4D) correlation of eastern and western markets is analyzed. A wavelet-based scale-by-scale analysis method has been introduced to model and forecast stock market data for strongly correlated time intervals. The daily data of stock markets of SP500, FTSE and DAX (western markets) and NIKKEI, TAIEX and KOSPI (eastern markets) are obtained from 2009 to the end of 2016 and their co-movement dependencies on time–frequency space using 4D multiple wavelet coherence (MWC) are determined. Once the data is detached into levels of different frequencies using scale-by-scale continuous wavelet transform, all of the time series possessing the same frequency scale are selected, inversed and forecasted using multivariate model, vector autoregressive moving average (VARMA). It is concluded that the efficiency of forecasting is increased substantially using the same-frequency highly correlated time series obtained by scale-by-scale wavelet transform. Moreover, the increasing or decreasing trend of prospected price shift is foreseen fairly well.

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Siddiqui, Saif, and Preeti Roy. "Predicting Volatility and Dynamic Relation Between Stock Market, Exchange Rate and Select Commodities." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 67, no. 6 (2019): 1597–611. http://dx.doi.org/10.11118/actaun201967061597.

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Commodities play a vital role in the development of emerging economies, like India. From this perspective, the study presents dynamic correlation in the prices of gold, crude oil, exchange rate and Indian stock market from April 01, 2014 to March 28, 2018. VARMA-BEKK-GARCH model is estimated for return and volatility spillovers across markets. Bidirectional returns spillover was found between Nifty and WTI and WTI and Gold pair. Whereas the bidirectional volatility spillover between Nifty and Gold pair. From the DCC-GARCH correlational analysis, Gold was found to be effective hedging commodity for Indian stock investors than Crude Oil. The asymmetric impact of shocks in covariance is observed between Nifty 50 and all other variables. The study focuses to aid investors and portfolio diversifiers while taking investment decisions.

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Poloni, Federico, and Giacomo Sbrana. "MULTIVARIATE TREND–CYCLE EXTRACTION WITH THE HODRICK–PRESCOTT FILTER." Macroeconomic Dynamics 21, no. 6 (September 9, 2016): 1336–60. http://dx.doi.org/10.1017/s1365100515000887.

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The Hodrick–Prescott filter represents one of the most popular methods for trend–cycle extraction in macroeconomic time series. In this paper we provide a multivariate generalization of the Hodrick–Prescott filter, based on the seemingly unrelated time series approach. We first derive closed-form expressions linking the signal–noise matrix ratio to the parameters of the VARMA representation of the model. We then show that the parameters can be estimated using a recently introduced method, called “Moment Estimation Through Aggregation (META).” This method replaces traditional multivariate likelihood estimation with a procedure that requires estimating univariate processes only. This makes the estimation simpler, faster, and better behaved numerically. We prove that our estimation method is consistent and asymptotically normal distributed for the proposed framework. Finally, we present an empirical application focusing on the industrial production of several European countries.

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Lesser, Kathrin, Felix Rößle, and Christian Walkshäusl. "International socially responsible funds: financial performance and managerial skills during crisis and non-crisis markets." Problems and Perspectives in Management 14, no. 3 (September 27, 2016): 461–72. http://dx.doi.org/10.21511/ppm.14(3-2).2016.02.

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Nofsinger and Varma (2014) provide evidence that U.S. socially responsible funds outperform conventional funds during periods of market turmoil and, therefore, grant some crisis insurance. To investigate whether the U.S.-based evidence can be transferred to international markets, the authors analyze a comprehensive sample of internationally-investing socially responsible equity funds in a period from 2000 to 2012. As abnormal returns are model-specific, the authors apply standard and q-theory based performance measurement models. At first glance, the authors observe no crisis protection for internationally-investing socially responsible funds. However, splitting their sample in funds domiciled in North America, Europe, and Asia-Pacific to account for biases due to the origin of a fund, the authors find that socially responsible funds from North America outperform their peers in crisis periods irrespective of the applied performance evaluation model. The authors suggest that the U.S.-based evidence is restricted to internationally-investing funds domiciled in North America, and discover that this outperformance seems to be owed to the stock-picking abilities of North American fund managers and their advantage due to the nature of the North American market. Keywords: socially responsible investments, mutual funds, international markets, performance evaluation, managerial abilities. JEL Classification: G11, G12, G15, G23, M14

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Serletis, Apostolos, and Sajjadur Rahman. "THE CASE FOR DIVISIA MONEY TARGETING." Macroeconomic Dynamics 17, no. 8 (September 7, 2012): 1638–58. http://dx.doi.org/10.1017/s1365100512000247.

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In this paper we investigate the relationship between money growth uncertainty and the level of economic activity in the United States. We pay explicit attention to the Divisia monetary aggregates. In doing so, we use the new vintage of the data [called MSI (monetary services indices) by the St. Louis Fed], together with the simple sum monetary aggregates, over the period from 1967:1 to 2011:3. In the context of a bivariate VARMA, GARCH-in-mean, asymmetric BEKK model, we show that increased Divisia money growth volatility (irrespective of the level of aggregation and the method of calculation) is associated with a lower average growth rate of real economic activity. However, there are no effects of simple-sum money growth volatility on real economic activity, except with the Sum M1 and perhaps Sum M2M aggregates. We conclude that monetary policies that focus on the Divisia monetary aggregates and target their growth rates will contribute to higher overall economic growth.

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Oygur, Tunc, and Gazanfer Unal. "Evidence of Large Fluctuations of Stock Return and Financial Crises from Turkey: Using Wavelet Coherency and Varma Modeling to Forecast Stock Return." Fluctuation and Noise Letters 16, no. 02 (May 25, 2017): 1750020. http://dx.doi.org/10.1142/s0219477517500201.

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Shocks, jumps, booms and busts are typical large fluctuation markers which appear in crisis. Models and leading indicators vary according to crisis type in spite of the fact that there are a lot of different models and leading indicators in literature to determine structure of crisis. In this paper, we investigate structure of dynamic correlation of stock return, interest rate, exchange rate and trade balance differences in crisis periods in Turkey over the period between October 1990 and March 2015 by applying wavelet coherency methodologies to determine nature of crises. The time period includes the Turkeys currency and banking crises; US sub-prime mortgage crisis and the European sovereign debt crisis occurred in 1994, 2001, 2008 and 2009, respectively. Empirical results showed that stock return, interest rate, exchange rate and trade balance differences are significantly linked during the financial crises in Turkey. The cross wavelet power, the wavelet coherency, the multiple wavelet coherency and the quadruple wavelet coherency methodologies have been used to examine structure of dynamic correlation. Moreover, in consequence of quadruple and multiple wavelet coherence, strongly correlated large scales indicate linear behavior and, hence VARMA (vector autoregressive moving average) gives better fitting and forecasting performance. In addition, increasing the dimensions of the model for strongly correlated scales leads to more accurate results compared to scalar counterparts.

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Ekeocha, Patterson C., and Elias A. Udeaja. "Spillover effect of United States Monetary Policy on Nigeria’s Financial and Macro Fundamentals." Central Bank of Nigeria Journal of Applied Statistics, Vol. 11 No. 1 (September 9, 2020): 111–45. http://dx.doi.org/10.33429/cjas.11120.5/5.

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This paper examines spillover effects of U.S monetary policy on macroeconomic fundamentals in Nigeria from January 1985 to December 2018. The study period is partitioned to account for conventional monetary policy (CMP) period, January 1985 to August 2007 and unconventional monetary policy (UMP) period, September 2007 to December 2018. Guided by relevant pre-tests, we find BEKK-VARMA-CCCMGARCH as the most appropriate model. The study finds significant spillover effects of U.S CMP and UMP on interest rate, exchange rate and inflation rate in Nigeria. We, however, observe that while CMP may be a significant accelerator of shocks persistence on interest rates and exchange rates, the extent to which the UMP accelerate shocks in inflation rate tends to vary for different measures of quantitative easing. Thus, in addition to past own shocks and past own conditional variance of these macro fundamentals, understanding their dynamics cannot be in isolation of their vulnerability to external shocks and volatility due to spillover effects of monetary actions in other economies. In formulating monetary policy, it is therefore, imperative for the Central Bank of Nigeria to monitor the monetary policy process of the US to hedge against shocks spillovers.

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Simionescu, Mihaela. "The Use of Varma Models in Forecasting Macroeconomic Indicators." ECONOMICS & SOCIOLOGY 6, no. 2 (November 20, 2013): 94–102. http://dx.doi.org/10.14254/2071-789x.2013/6-2/9.

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Sawant, Shukla. "The Trace Beneath: The Photographic Residue in the Early Twentieth-century Paintings of the “Bombay School”." BioScope: South Asian Screen Studies 8, no. 1 (June 2017): 1–29. http://dx.doi.org/10.1177/0974927617700768.

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This essay examines the interface between the indexical and the gestural, through the practice of early twentieth-century painters active in the Bombay Presidency and adjoining princely states such as Kolhapur and Aundh. It draws upon archival materials such as biographies, memoirs, and photographs documenting artists at work in the studio, as well as remains of posed photographs that were produced as aide-mémoire for paintings. It throws light on the fraught place of photography as aesthetic practice in the art academy, its association with colonial protocols of scientific accuracy, capture and control, and its use to construct suggestive representational hybrids of the anatomical and the painterly outside the academy. The article explores patterns of patronage and of the use of photography in the practices of art production, publication, and exhibition, looking, in particular, at the role of the photographic basis of the portrait painting, and how photography became a supplement to “life-study” or the practice of drawing from nude models. The gendered politics of this interface, between artist, technology, and female model is a recurrent thread of analysis, drawing on critical debates that were published in Marathi periodicals of the time. The article explores the braiding of technologies in artistic practice in different sites, from the academy and the artist’s studio through to publication and exhibition in galleries, and illustrated magazines. While the essay considers a number of artists, including Ravi Varma, Durandhar, and Thakur Singh, it focuses, in particular, on Baburao Painter for his engagement with photography and painting in a career which traversed theater, painting, photography, and film production.

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Monfort, A., and F. Pegoraro. "Switching VARMA Term Structure Models." Journal of Financial Econometrics 5, no. 1 (November 18, 2006): 105–53. http://dx.doi.org/10.1093/jjfinec/nbl009.

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Morris, Stephen D. "VARMA representation of DSGE models." Economics Letters 138 (January 2016): 30–33. http://dx.doi.org/10.1016/j.econlet.2015.11.027.

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Tuncer, Murat, and Melih Dikmen. "The effect of cooperative learning on academic achievement: A meta-analysis on the relationship between the study group size and effect size İşbirlikli öğrenmenin başarıya etkisi: Çalışma grubu ile etki büyüklüğü arasındaki ilişkiye dair bir meta analiz çalışması." Journal of Human Sciences 14, no. 1 (February 20, 2017): 473. http://dx.doi.org/10.14687/jhs.v14i1.4314.

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The purpose of this study was to determine the effect of cooperative learning on achievement and the relationship between the study group and effect size by means of meta-analysis. Meta-analysis is the method employed in order to statistically analyze the quantitative data collected in independent and multiple studies carried out on similar topics, and to reach a general judgment regarding the results of these studies. Certain criteria were used in order to decide which researches would be included in the meta-analysis. Based on these criteria, it was decided to include 6 experimental studies in the meta-analysis. As a result, for the six studies, the effect size calculated within 95 % confidence interval has proved to be .518 based on random effects model. When two studies with the largest learning groups are excluded from the research respectively, firstly this effect size increases to .528, later it is calculated as .817 without any model discrimination. When effect size intervals in literature (≥ 0.5: strong, ≥ 0.3: moderate and ≥ .01 weak) is taken into consideration, the effect of cooperative learning on achievement has been observed as “Strong”. In addition to this, contrary to some views in literature, it has been seen that large learning groups have higher standard errors and a bigger effect size has been achieved when such studies have been excluded from the analysis. What is more, even the limitation of fixed effects model has disappeared, and the effect size calculated in fixed effects and random effects model has been balanced. ÖzetBu araştırmanın amacı, işbirlikli öğrenmenin başarı üzerindeki etkisini ve çalışma grubu ile etki büyüklüğü arasındaki ilişkiyi meta-analiz yöntemiyle belirlemektir. Meta-analiz, benzer konularda yapılmış birbirinden bağımsız ve çok sayıda çalışmadan elde edilmiş sayısal verileri istatistiksel olarak analiz etme ve bu çalışmaların sonuçları hakkında genel bir yargıya varma yöntemidir. Hangi araştırmaların meta-analize dâhil edileceğine yönelik seçimlerin yapılmasında belirli ölçütler kullanılmıştır. Bu ölçütlere dayalı olarak 6 adet deneysel araştırmanın meta-analize dâhil edilmesine karar verilmiştir. Sonuç olarak; altı çalışma için %95 güven aralığında hesaplanan etki büyüklüğü rastgele etkiler modeline göre .518’dir. En büyük çalışma grubuna sahip iki araştırma sırasıyla araştırma dışında tutulduğunda bu etki büyüklüğü önce .528’ e çıkmakta, sonrasında ise model ayrımı olmaksızın .817 olarak hesaplanmaktadır. Alan yazındaki etki büyüklüğü aralıkları (≥ 0.5: güçlü, ≥ 0.3: orta düzey ve ≥ .01 zayıf) dikkate alındığında işbirlikli öğrenmenin başarı üzerindeki etkisi “Güçlü” bir etki olarak gözlenmiştir. Ayrıca alan yazındaki bazı görüşlerin aksine büyük çalışma gruplarının standart hatalarının daha yüksek olduğu, bu araştırmaların analiz dışında tutulması ile daha büyük etki büyüklüklerine ulaşıldığı, hatta sabit etki modelinin sınırlılığının bile ortadan kalktığı, sabit etki ve rastgele etki modelinde hesaplanan etki büyüklüğünün eşitlendiği görülmüştür.

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Amaro Baldeón, Roberto, and Paulo Gardel Kurka. "IDENTIFICACIÓN DE PARÁMETROS MODALES DE ESTRUCTURAS VIBRANTES CON EXCITACIÓN ESTOCÁSTICA O DESCONOCIDA." Industrial Data 8, no. 2 (March 22, 2014): 052. http://dx.doi.org/10.15381/idata.v8i2.6186.

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El Modelo multivariado autoregresivo con medias móviles (VARMA) se emplea para identificar características dinámicas de un sistema estructural ante la presencia de ruido. Para estimar los parámetros del Modelo VARMA, se emplea el algoritmo de Spliid. Para determinar los parámetros modales, la matriz compañera se forma con los coeficientes de la parte autoregresiva del Modelo VARMA. La eficiencia de este modelo, discutida aquí, se analiza por medio de un sistema vibratorio de tres grados de libertad.

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Lütkepohl, Helmut, D. S. Poskitt, and Helmut Lutkepohl. "Specification of Echelon-Form VARMA Models." Journal of Business & Economic Statistics 14, no. 1 (January 1996): 69. http://dx.doi.org/10.2307/1392100.

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Kascha, Christian, and Karel Mertens. "Business cycle analysis and VARMA models." Journal of Economic Dynamics and Control 33, no. 2 (February 2009): 267–82. http://dx.doi.org/10.1016/j.jedc.2008.05.006.

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Journal articles on the topic 'VARMA model'

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