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Search Results: 1 - 10 of 1659 matches for " VAR "
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Research on the Influencing Effect between CHVA and CPI in China Based on VAR Models  [PDF]
Jinge Zhou, Juan Chen, Xiuli Yu, Yifan Li, Qifeng Lin
American Journal of Industrial and Business Management (AJIBM) , 2013, DOI: 10.4236/ajibm.2013.34044
Abstract:

The cointegration test, granger causality test, VAR model, impulse response function and other econometric methods are used in this paper to analyze the influencing effect between commercial housing vacancy rate and CPI and its delay impact. The results show that there is a long-term equilibrium relationship between commercial housing vacancy rate and CPI in China. There are at least one cointegration relationship between CHVR and CPI. The past values of the CPI appear to contain information which is useful for forecasting changes in the CHVR. CPI has a significant effect on CHVR and CPI rising drives CHVR.

Investigating Influential Factors on Improving Poverty Conditions in Latin America  [PDF]
Keisuke Mitsumoto, Koichi Yamaura
Journal of Human Resource and Sustainability Studies (JHRSS) , 2018, DOI: 10.4236/jhrss.2018.62035
Abstract: The objective of this study is to determine the spillover effects of various international supports for poverty in Paraguay. A vector autoregressive model is used to investigate a dynamic linkage among five components: prevalence of undernourishment, food intake, gross domestic products (GDP) per capita, primary school completion rate, and unemployment rate. We found that the primary school completion rate has the largest spillover effects for reducing poverty except GDP per capita. Supporting international agencies such as the Inter-American Development Bank can keep or invest more money in early education sectors, and Paraguay can obtain not only direct supports but also larger indirect effects.
Stationary Vector Autoregressive Representation of Error Correction Models  [PDF]
Yun-Yeong Kim
Theoretical Economics Letters (TEL) , 2012, DOI: 10.4236/tel.2012.22027
Abstract: The paper introduces a stationary vector autoregressive (VAR) representation of the error correction model (ECM). This representation explicitly regards the cointegration error a dependent variable, making the direct implementation of standard dynamic analyses using standard VAR models possible, particularly with respect to the cointegration error. Of course, an ECM does not have an explicit VAR form, and thus, it is not convenient for conducting such dynamic analyses. In this regard, we transform the original nonstationary VAR model into a VAR model with the cointegration error and stationary variables. Finally, we employ the model to dynamically analyze the real exchange rate between the US dollar and the Japanese yen.
CVaR度量在极值理论中的应用
Application of CVaR Metric in Extreme Value Theory
 [PDF]

姚竟, 李永明
Pure Mathematics (PM) , 2016, DOI: 10.12677/PM.2016.62014
Abstract:
近半个世纪以来,随着经济的全球化和多元化,金融风险的度量逐渐受到金融界以及经济学者的关注。90年代后,新型风险管理工具VaR(在险价值)测量方法逐步发展起来,以它能够科学、准确、综合的度量风险值而倍受国际金融界的青睐。但在极端事件发生期,VaR的度量准确性不如CVaR(条件在险价值)。本文意在研究CVaR度量在极值理论上的应用。
Since the last half a century, with the globalization and diversification of economy, the financial risk measurement has gradually been concerned by the financial and economic scholars. After the 1990s, the new risk management tool, VaR (value at risk) measurement method has been devel-oped gradually, which can measure risk value scientifically, accurately and comprehensively, and it is welcomed in the international financial community, but in extreme event, the accuracy of VaR is less than that of CVaR (conditional value at risk). This paper is intended to study the application of CVaR measure in extreme value theory.
A Research on Interbank Loan Interest Rate Fluctuation Characteristics and the VaR Risk of China’s Commercial Banks  [PDF]
Baoqian Wang, Cheng Wang, Xikun Zhang
Modern Economy (ME) , 2012, DOI: 10.4236/me.2012.36097
Abstract: According to the historical time series data of commercial interbank, this paper examines the interest rate fluctuation distribution characteristics, indicating that EGARCH Model can better fit the rate volatility of the interbank market interest. This paper calculates the value at risk (VaR) of five major commercial banks using EGARCH Model with such a conclusion that the difference that major commercial banks face is various. The interest risk of state-owned commercial banks and other financial institutions is more serious than the city commercial banks and foreign banks. The interest risk of rural credit cooperatives is the least serious.
Money Supply and Inflation in Nigeria: Implications for National Development  [PDF]
Olorunfemi Sola, Adeleke Peter
Modern Economy (ME) , 2013, DOI: 10.4236/me.2013.43018
Abstract:

The study examines money supply and inflation rate in Nigeria. Secondary data that ranged between 1970-2008 were sourced from the CBN Statistical Bulletin. The study used Vector Auto Regressive (VAR) model. The stationary properties of the model were also explored. The results revealed that money supply and exchange rate were stationary at the level while oil revenue and interest rate were stationary at the first difference. Results from the causality test indicate that there exists a unidirectional causality between money supply and inflation rate as well as interest rate and inflation rate. The causality test runs from money supply to inflation, from the interest rate to inflation and from interest rate to money supply. The paper concludes that government should use the level of inflation as an operational guide in measuring the effectiveness of its monetary policy.

Quantitative Risk Analysis of the Futures Company’s Own Business Based on VaR Model  [PDF]
Jianfei Len, Xu Gao, Guorong Jia
Journal of Financial Risk Management (JFRM) , 2014, DOI: 10.4236/jfrm.2014.34012
Abstract: In this paper, we use the futures exchange copper trading data of Shanghai as a sample for the VaR quantitative analysis. Through empirical analysis, the results showed that VaR method based on GARCH model can be a good fit in the insurance value of copper futures. Therefore, we can consider it as an important means of futures risk management in our country, and with reference t to establish corresponding risk warning system.
Application of Copula-GARCH to Estimate VaR of a Portfolio with Credit Default Swaps  [PDF]
Jhe-Jheng Huang, Leh-Chyan So
Journal of Mathematical Finance (JMF) , 2018, DOI: 10.4236/jmf.2018.82025
Abstract: Credit Default Swaps (CDSs) provide an efficient way for commercial banks to hedge their portfolios’ exposure to credit risk. Following Patton (2006), Huang, Lee, Liang, and Lin (2009), and Fei, Fuertes, and Kalotychou (2013), we proposed a way to estimate Value-at-Risk (VaR) of portfolios containing CDSs that is better than the traditional methods mentioned in financial textbooks. Markit’s North American Investment Grade CDX Index (CDX.NA.IG) is a combination index of 125 North American entities with investment-grade credit ratings that trade in the CDS market. Each of the S & P 500 index and VIX are used with CDX.NA.IG to construct portfolios. This paper uses 2,477 daily data items from December 2004 to October 2014 covering the period of the subprime mortgage crisis and the European debt crisis. We chose six constant and two time-varying copula models combined with GARCH skewed Student-t innovation (GARCH-skt) to form eight copula-GARCH models to capture the joint distribution of the two assets in the portfolio. We then computed corresponding 1-day VaRs. According to our findings, the time-varying symmetrized Joe-Clayton (SJC) copula model combined with the GARCH-skt (tvSJC-copula–GARCH-skt) performed best, regardless of the market situation. Not surprisingly, this result stems mainly from this model’s consideration of the serial correlation in the individual index return and the time-varying nonlinear dependency between indices.
Measuring and Comparing the Value-at-Risk Using GARCH and CARR Models for CSI 300 Index  [PDF]
Chunchou Wu
Theoretical Economics Letters (TEL) , 2018, DOI: 10.4236/tel.2018.86078
Abstract: This article is the first to provide a detailed method for range-based CARR model to estimate the VaR and its out-of-sample prediction. In this paper, we use GARCH and CARR volatility models to compare the VaR’s out-of-sample forecasting performance. Using the historical simulation method as benchmark for VaR estimation, we found that the historical simulation approach for VaR measurement is more conservative than GARCH and CARR methods. The mean violation rate for the CARR VaRs is lower than that of the GARCH VaRs. Meanwhile, the CARR VaR is able to deliver lower required capital levels without producing bigger violations. This paper argued that the CARR VaR valuation approach is suitable as an internal model method for financial institution in VaR forecasting.
A Simulation Study on the Performances of Classical Var and Sims-Zha Bayesian Var Models in the Presence of Autocorrelated Errors  [PDF]
M. O. Adenomon, V. A. Michael, O. P. Evans
Open Journal of Modelling and Simulation (OJMSi) , 2015, DOI: 10.4236/ojmsi.2015.34016
Abstract: It is well known that a high degree of positive dependency among the errors generally leads to 1) serious underestimation of standard errors for regression coefficients; 2) prediction intervals that are excessively wide. This paper set out to study the performances of classical VAR and Sims-Zha Bayesian VAR models in the presence of autocorrelated errors. Autocorrelation levels of (-0.99, -0.95, -0.9, -0.85, -0.8, 0.8, 0.85, 0.9, 0.95, 0.99) were considered for short term (T = 8, 16); medium term (T = 32, 64) and long term (T = 128, 256). The results from 10,000 simulation revealed that BVAR model with loose prior is suitable for negative autocorrelations and BVAR model with tight prior is suitable for positive autocorrelations in the short term. While for medium term, the BVAR model with loose prior is suitable for the autocorrelation levels considered except in few cases. Lastly, for long term, the classical VAR is suitable for all the autocorrelation levels considered except in some cases where the BVAR models are preferred. This work therefore concludes that the performance of the classical VAR and Sims-Zha Bayesian VAR varies in terms of the autocorrelation levels and the time series lengths.
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