scholarly journals Is Foreign Exchange Intervention a Panacea in Diversified Circumstances? The Perspectives of Asymmetric Effects

2020 ◽  
Vol 12 (7) ◽  
pp. 2913
Author(s):  
Wenbo Wang ◽  
Dieu Thanh Le ◽  
Hail Park
Keyword(s):  
Economic Development ◽  
Impulse Response ◽  
Foreign Exchange ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Foreign Exchange Intervention ◽  
Asymmetric Effects ◽  
Growing Body ◽  
Volatility Regimes

Owing to the country’s heavy reliance on exports, the role of foreign exchange intervention in South Korea’s economic development is self-evident. The effectiveness of the intervention is what we are concerned with in this paper. Recently, a growing body of literature has engaged in exploring the asymmetric effects of foreign exchange intervention both theoretically and empirically. Against this background, we employ a threshold vector autoregression (TVAR) model in parallel with its generalized impulse response functions (GIRFs) to show that there are asymmetric effects of the Bank of Korea (BOK)-led interventions regardless of the volatility regimes.

THE ASYMMETRIC EFFECTS OF OIL PRICE SHOCKS

2011 ◽  
Vol 15 (S3) ◽  
pp. 437-471 ◽  
Author(s):  
Sajjadur Rahman ◽  
Apostolos Serletis
Keyword(s):  
Impulse Response ◽  
Forecast Error ◽  
Price Change ◽  
The United States ◽  
Output Growth ◽  
Oil Price ◽  
Impulse Response Functions ◽  
Price Uncertainty ◽  
Response Functions ◽  
Asymmetric Effects

In this paper we investigate the effects of oil price uncertainty and its asymmetry on real economic activity in the United States, in the context of a bivariate vector autoregression with GARCH-in-mean errors. The model allows for the possibilities of spillovers and asymmetries in the variance–covariance structure for real output growth and the change in the real price of oil. Our measure of oil price uncertainty is the conditional variance of the oil price–change forecast error. We isolate the effects of volatility in the change in the price of oil and its asymmetry on output growth and employ simulation methods to calculate generalized impulse response functions and volatility impulse response functions to trace the effects of independent shocks on the conditional means and the conditional variances, respectively, of the variables. We find that oil price uncertainty has a negative effect on output, and that shocks to the price of oil and its uncertainty have asymmetric effects on output.

Impulse-response functions and anthropogenic CO2

1995 ◽  
Vol 22 (4) ◽  
pp. 413-416 ◽  
Author(s):  
Francesco N. Tubiello ◽  
Michael Oppenheimer
Keyword(s):  
Impulse Response ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Anthropogenic Co2

IMPROVING EXTRACTION OF IMPULSE RESPONSE FUNCTIONS USING STATIONARY WAVELET SHRINKAGE IN TERAHERTZ REFLECTION IMAGING

2010 ◽  
Vol 09 (04) ◽  
pp. 387-394 ◽  
Author(s):  
YANG CHEN ◽  
YIWEN SUN ◽  
EMMA PICKWELL-MACPHERSON
Keyword(s):  
Experimental Data ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Terahertz Imaging ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Inverse Filtering ◽  
Wavelet Shrinkage ◽  
Gaussian Filtering

In terahertz imaging, deconvolution is often performed to extract the impulse response function of the sample of interest. The inverse filtering process amplifies the noise and in this paper we investigate how we can suppress the noise without over-smoothing and losing useful information. We propose a robust deconvolution process utilizing stationary wavelet shrinkage theory which shows significant improvement over other popular methods such as double Gaussian filtering. We demonstrate the success of our approach on experimental data of water and isopropanol.

A Comparison of Approaches to Select the Informativeness of Priors in BVARs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan Prüser ◽  
Christoph Hanck
Keyword(s):  
Impulse Response ◽  
Prior Information ◽  
Hierarchical Modeling ◽  
Small Samples ◽  
Model Parameters ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressions ◽  
The Rich ◽  
Bayesian Vars

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.

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