Analysing inflation by the fractionally integrated ARFIMA-GARCH model

Baillie, Richard T.; Chung, Ching-Fan; Tieslau, Margie

doi:10.1002/(sici)1099-1255(199601)11:1<23::aid-jae374>3.0.co;2-m

Cited by 463 publications

(344 citation statements)

References 50 publications

Supporting

Mentioning

309

Contrasting

Unclassified

Order By: Relevance

“…6 Note, that we can easily control for lagged level effects by adding the respective terms to equation (3). An alternative approach to introducing level effects in an exponential fashion as in equation (3) would be by adding the lagged inflation rates either linearly (see Conrad et al, 2010b) or quadratically (see Brunner and Hess, 1993).…”

Section: The Bivariate Garch Modelmentioning

confidence: 99%

“…If the weight on growth is sufficiently large, the central bank has an incentive to increase inflation in the presence of higher nominal uncertainty (see Cukierman and Meltzer, 1986). The in-mean parameter estimate in equation (4), (3) , suggests that -with a lag of three inflation.…”

Section: Baseline Specificationmentioning

confidence: 99%

“…Brunner and Hess (1993) was one of the first papers to employ a univariate GARCH model in order to test for the first leg of the Friedman hypothesis (see also Baillie et al, 1996). During the last decade researchers have employed various bivariate GARCH-inmean models to investigate the relation between the two uncertainties (see, for example, Conrad et al, 2010b) and/or to examine their impact on the levels of inflation and growth (see, for example, Elder, 2004 andGrier et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Thus, we can examine in a unified empirical framework all the possible causal relations among the four variables that are predicted by economic theory. 3 In short, our main results can be summarized as follows. First, inflation is a negative determinant of real growth.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modeling the Link between US Inflation and Output: The Importance of the Uncertainty Channel

Conrad

Karanasos

2010

SSRN Journal

View full text Add to dashboard Cite

This paper employs an augmented version of the UECCC GARCH specification proposed in Conrad and Karanasos (2010) which allows for lagged in-mean effects, level effects as well as asymmetries in the conditional variances. In this unified framework we examine the twelve potential intertemporal relationships between inflation, growth and their respective uncertainties using US data. We find that high inflation is detrimental to output growth both directly and indirectly via the nominal uncertainty. Output growth boosts inflation but mainly indirectly through a reduction in real uncertainty. Our findings highlight that macroeconomic performance affects nominal and real uncertainty in many ways and that the bidirectional relation between inflation and growth works to a large extend indirectly via the uncertainty channel.Keywords: Bivariate GARCH process, volatility feedback, inflation uncertainty, output variability.JEL Classification: C32, C51, E31. * We would like to thank James Davidson, Marika Karanassou and Ruey Tsay for their valuable suggestions. We have also benefited from the comments received from the participants of the Annual Meeting of the Verein für Socialpolitik (Kiel, 2010), the Annual Meeting of the Society for Nonlinear Dynamics and Econometrics (Novara, 2010)

show abstract

Section: The Bivariate Garch Modelmentioning

confidence: 99%

Section: Baseline Specificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling the Link between US Inflation and Output: The Importance of the Uncertainty Channel

Conrad

Karanasos

2010

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…For example, Brunner and Hess (1993) (who suggested to take into account the asymmetry in the influence of shocks on the volatility), Baillie, Chung, and Tieslau (1996) (who used the ARFIMA-GARCH model featuring long memory), Kontonikas (2004), Fountas, Karanasos, and Kim (2006), Henry, Olekalns, and Suardi (2007). Probably the most representative study of this kind in terms of the number of countries is Daal, Naka, and Sanchez (2005).…”

Section: Inflation-uncertainty Literature: Empirical Evidencementioning

confidence: 99%

The Links between Inﬂation and Inﬂation Uncertainty at the Longer Horizon

Tsyplakov

2010

SSRN Journal

View full text Add to dashboard Cite

In this paper I examine the Okun-Friedman hypothesis of the link between inflation and inflation uncertainty using historical international data on the monthly CPI. An indicator of inflation uncertainty at the two-years-ahead horizon is derived from a time-series model of inflation with time-varying parameters by means of Monte Carlo simulations. This indicator is compared to other uncertainty measures, with the short forecast horizon and based on simpler GARCH-type models. The analysis convincingly demonstrates that both the longer horizon and changing parameters are important for the regularity. The evidence obtained strongly supports the Okun-Friedman hypothesis both in the time dimension for most countries and across countries.

show abstract

Fractional cointegration and futures hedging

Lien

Tse

1999

J. Fut. Mark.

View full text Add to dashboard Cite

This article examines the performance of various hedge ratios estimated from different econometric models: The FIEC model is introduced as a new model for estimating the hedge ratio. Utilized in this study are NSA futures data, along with the ARFIMA‐GARCH approach, the EC model, and the VAR model. Our analysis identifies the prevalence of a fractional cointegration relationship. The effects of incorporating such a relationship into futures hedging are investigated, as is the relative performance of various models with respect to different hedge horizons. Findings include: (i) Incorporation of conditional heteroskedasticity improves hedging performance; (ii) the hedge ratio of the EC model is consistently larger than that of the FIEC model, with the EC providing better post‐sample hedging performance in the return–risk context; (iii) the EC hedging strategy (for longer hedge horizons of ten days or more) incorporating conditional heteroskedasticty is the dominant strategy; (iv) incorporating the fractional cointegration relationship does not improve the hedging performance over the EC model; (v) the conventional regression method provides the worst hedging outcomes for hedge horizons of five days or more. Whether these results (based on the NSA index) can be generalized to other cases is proposed as a topic for further research. © 1999 John Wiley & Sons, Inc. Jrl Fut Mark 19: 457–474, 1999

show abstract

Analysing inflation by the fractionally integrated ARFIMA-GARCH model

Cited by 463 publications

References 50 publications

Modeling the Link between US Inflation and Output: The Importance of the Uncertainty Channel

Modeling the Link between US Inflation and Output: The Importance of the Uncertainty Channel

The Links between Inﬂation and Inﬂation Uncertainty at the Longer Horizon

Fractional cointegration and futures hedging

Contact Info

Product

Resources

About