var models
On Multivariate Time Series Model Selection Involving Many Candidate VAR Models
... subset models where some coefficient matrices Φ i rather than individual coefficients are entirely set to ...subset VAR models to be compared with each other to 2 P ...subset VAR model even ...
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A Simulation Study on the Performances of Classical Var and Sims Zha Bayesian Var Models in the Presence of Autocorrelated Errors
... classical VAR model tend to forecast where there is no autocorrelation while the Bayesian VAR models with harmonic decay forecast better for both negative and positive autocorrelation ...
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On the Performances of Classical VAR and Sims Zha Bayesian VAR Models in the Presence of Collinearity and Autocorrelated Error Terms
... [5] Adenomon, M.O. and Oyejola, B.A. (2014) Forecasting Performances of the Reduced Form VAR and Sims-Zha Baye- sian VAR Models When the Multiple Times Series Are Jointly Influence by Collinearity ...
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Forecasting VARMA processes using VAR models and subspace based state space models
... order VAR formulation is not as general: a finite-order VAR model can only be an approximation of an underlying VARMA process, while a finite-order State Space (SS) model can provide an exact ...SS ...
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Financial Risk Measurement for Turkish Insurance Companies Using VaR Models
... management models first define and classify the ...management models is to measure and assess the risks while the third step involves the creation of a risk map in order to reduce these ...
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Short Term Forecasting Performances of Classical VAR and Sims Zha Bayesian VAR Models for Time Series with Collinear Variables and Correlated Error Terms
... The choice of Normal-inverse Wishart prior for the BVAR models follow the work of [42] that Normal Wi- shart prior tends to performed better when compared to other priors. In addition [38] proposed Normal-Inverse ...
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An Empirical Study on the Development of the Shadow Banking in Hubei Province Based on VAR Models
... the VAR model is stable and the result is ...the VAR model is feasible. This VAR model could be used to analyze the impulse re- sponse between shadow banks in Hubei province, GDP in Hubei and social ...
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Long run recursive VAR models and QR decompositions
... Long-run recursive identi…cation schemes are very popular in the struc- tural VAR literature. This note suggests a two-step procedure based on QR decompositions as a solution algorithm for this type of ...
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Bayesian VAR Models for Forecasting Irish Inflation
... performance will not be optimal. Hence, an “extreme Bayesian” who is too certain of his/her priors will forecast poorly. In the case of both the Augmented SOE (BVAR2) and the Monetary Models (BVAR3), in Figures 2 ...
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Backtesting VaR Models: A Τwo Stage Procedure
... the models, which are rejected by univariate VaR backtests, are discarded, whereas, in the second step a multivariate superior predictive ability test is run taking as a benchmark model each of the ...
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Analytical Estimation of Value at Risk Under Thick Tails and Fast Volatility Updating
... of VaR models used by banks to predict extreme ...conclusion, VaR based volatility method (like the forecast evaluation method) do not give distinctive proof for the accuracy of banks VaR ...
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Forecasting Chinese inflation and output: A Bayesian vector autoregressive approach
... (VAR) models for forecasting price inflation and output growth in ...that models with shrinkage and model selection priors, that restrict some VAR coefficients to be close to zero, perform ...
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Return-Volatility Interactions in the Nigerian Stock Market
... dataset utilized include All-share index, narrow money, broad money, interest rate, exchange rate, industrial production index, and Federal Funds Rate. The link of monetary policies volatility to stock returns volatility ...
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The Possibilities of Application of the Parametric and Nonparametric VaR Daily Returns Estimation – Regional Perspective
... nonparametric VaR models is ...tested models in various markets that are in different conjuncture ...tested models in observed markets in different markets conditions, with the goal to provide ...
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Predictive Performance of Conditional Extreme Value Theory and Conventional Methods in Value at Risk Estimation
... (VaR) models such as GARCH-normal, GARCH-t, EGARCH, TGARCH models, variance-covariance method, historical simulation and filtred Historical Simulation, EVT and conditional EVT ...EVT models to ...
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Evaluating the Precision of Estimators of Quantile Based Risk Measures
... on models that forecast the risks associated with financial ...(VaR) models, these models in fact can and sometimes do forecast the complete density functions of prospective financial losses ...
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Forecasting Stock Prices With Linear And Nonlinear Settings: A Comparison
... each VAR model. Although, linear models are considered a good estimation technique to study time series processes, it is important to test the non-linearity and non-gaussianity of the ...the VAR ...
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Optical Properties of In1 xGaxN Epilayers Grown by HPCVD
... per VAR models are mainly negative. The predictions based on VAR reach the lowest point in third quarter ...and VAR fail to exactly catch the huge deterioration in the second quarter of 2008, ...
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Investment Environment Problems Analysis and Evaluation: An Ex Post Empirical Analysis and Performance Implications
... forecasting models (Dowd & Blake, 2006). Hence, none of the VaR models have superior performance than others in all circumstances and in all ...single VaR model cannot be recommended ...
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MODEL FOR MINIMUM AND MAXIMUM TEMPERATURE OF THE UPPER EAST REGION OF GHANA
... series models developed for univariate and multi variate time series data. VAR models are a specific case of more general VARMA ...VARMA models for multivariate time series include the ...
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