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Full-Text Articles in Econometrics
A Closer Look At The Impact Of Quantitative Easing On The Capital Markets: Garch Analysis Of The Exchange Traded Funds Market, Nicholas R. Duafala
A Closer Look At The Impact Of Quantitative Easing On The Capital Markets: Garch Analysis Of The Exchange Traded Funds Market, Nicholas R. Duafala
Undergraduate Economic Review
This paper analyzes the effects of quantitative easing (QE) on the capital markets by modeling exchange traded funds (ETFs) returns using a generalized autoregressive conditional heteroskedasticity (GARCH) methodology. The results show that the 10-Year Treasury yields are significant in the returns of some sectors of the economy more so than others, and the Federal Funds Futures trading volume is significant in all ETFs return volatility. The implications of these results not only provide information about the reaction of the ETF market and QE, but also provide insight for developing investment strategies.
Estimation Of Hyperbolic Diffusion Using Mcmc Method, Yiu Kuen Tse, Jun Yu, X. B. Chang
Estimation Of Hyperbolic Diffusion Using Mcmc Method, Yiu Kuen Tse, Jun Yu, X. B. Chang
Research Collection School of Economics
In this paper we propose a Bayesian method for estimating hyperbolic diffusion models. The approach is based on the Markov Chain Monte Carlo (MCMC) method after discretization via the Milstein scheme. Our simulation study shows that the hyperbolic diffusion exhibits many of the stylized facts about asset returns documented in the financial econometrics literature, such as a slowly declining autocorrelation function of absolute returns. We demonstrate that the MCMC method provides a useful tool to analyze hyperbolic diffusions. In particular, quantities of posterior distributions obtained from MCMC outputs can be used for statistical inferences
Adaptive Testing In Arch Models, Douglas G. Steigerwald, Oliver Linton
Adaptive Testing In Arch Models, Douglas G. Steigerwald, Oliver Linton
Douglas G. Steigerwald
Specification tests for conditional heteroskedasticity that are derived under the assumption that the density of the innovation is Gaussian may not be powerful in light of the recent empirical results that the density is not Gaussian. We obtain specification tests for conditional heteroskedasticity under the assumption that the innovation density is a member of a general family of densities. Our test statistics maximize asymptotic local power and weighted average power criteria for the general family of densities. We establish both first-order and second-order theory for our procedures. Simulations indicate that asymptotic power gains are achievable in finite samples.