Central bank intervention and foreign exchange rates: new evidence from FIGARCH estimations

Beine, Michel; Bénassy‐Quéré, Agnès; Lecourt, Christelle

doi:10.1016/s0261-5606(01)00040-7

Cited by 136 publications

(108 citation statements)

References 26 publications

Supporting

Mentioning

100

Contrasting

Unclassified

Order By: Relevance

“…This result is robust to the use of any of the three main measures of asset price volatility: univariate GARCH models (Baillie and Osterberg, 1997;Dominguez, 1998;Beine, Bénassy-Quéré, and Lecourt, 2002); implied volatilities extracted from option prices (Bonser-Neal and Tanner, 1996;Dominguez, 1998;Galati and Melick, 1999); and realized volatility (Beine, Laurent, and Palm, 2009;Dominguez, 2006).…”

mentioning

confidence: 75%

Econometric Modeling of Exchange Rate Volatility and Jumps

2012

View full text Add to dashboard Cite

This chapter reviews the rapid advances in foreign exchange volatility modeling made in the last three decades. Academic researchers have sought to fit the three major characteristics of foreign exchange volatility: intraday periodicity, autocorrelation and discontinuities in prices. Early research modeled the autocorrelation in daily and weekly squared foreign exchange returns with ARCH/GARCH models. Increased computing power and availability of high-frequency data allowed later researchers to improve volatility and jumps estimates. Researchers also found it useful to incorporate information about periodic volatility patterns and macroeconomic announcements in their calculations. This article details these volatility and jump estimation methods, compares those methods empirically and provides some suggestions for further research.

show abstract

mentioning

confidence: 75%

Econometric Modeling of Exchange Rate Volatility and Jumps

2012

View full text Add to dashboard Cite

show abstract

“…This is possible if their orders are filed through a broker, which keeps the identity of its client anonymous. Some studies, notably Dominguez (1998) and Beine, Bénassy-Quéré, and Lecourt (2002), have found that usually reported FX intervention presents a different impact from unreported one. However, one may wonder whether unreported intervention operations are actually conducted with the intention of altering currency values and hence whether they correspond to genuine FX intervention.…”

Section: The Secrecy Puzzlementioning

confidence: 99%

An assessment of some open issues in the analysis of foreign exchange intervention

Vitale

2007

Int. J Fin Econ

View full text Add to dashboard Cite

We discuss some fundamental aspects of the study of foreign exchange intervention. In particular, we analyze some unresolved issues, such as the secrecy puzzle, the relevance of the signalling and portfolio-balance effect hypotheses, the identification of the impact of foreign exchange intervention on currency values. We argue that: i) the unresolved nature of these issues is partly due to the absence of a market microstructure perspective in most of the existing analysis of foreign exchange intervention; and ii) that recent promising advances in this strand of research have not fully exploited the potential of such perspective. JEL Nos.: D82, G14 and G15.

show abstract

“…It implies that asymmetries can become inverted, with positive innovations inducing more volatility than negative innovations of the same size when the observed value of the conditional variance is lower than expected. Fornari and Mele [31] built a mixture of the GJR and the VSARCH models, named it asymmetric volatility-switching ARCH, or AVSARCH(p,q), model and estimated it for : [219], proposed the smooth transition ARCH model. In the STAR model, the conditional mean is a non-linear function of lagged realizations of the series introduced via a transition function.…”

Section: I= Qmentioning

confidence: 99%

“…Baillie et al [24] provided a simulation experiment that provides considerable support of this line of argument. Beine et al [31] applied the FIGARCH (1,d,1) model in order to investigate the effects of official interventions on the volatility of exchange rates. One of their interesting remarks is that measuring the volatility of exchange rates through the FIGARCH model instead of a traditional ARCH model leads to different results.…”

Section: H =mentioning

confidence: 99%

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

Degiannakis

Xekalaki

2004

Quality Technology & Quantitative Management

View full text Add to dashboard Cite

Autoregressive Conditional Heteroscedasticity (ARCH) models have successfully been employed in order to predict asset return volatility. Predicting volatility is of great importance in pricing financial derivatives, selecting portfolios, measuring and managing investment risk more accurately. In this paper, a number of univariate and multivariate ARCH models, their estimating methods and the characteristics of financial time series, which are captured by volatility models, are presented. The number of possible conditional volatility formulations is vast. Therefore, a systematic presentation of the models that have been considered in the ARCH literature can be useful in guiding one's choice of a model for exploiting future volatility, with applications in financial markets.

show abstract

Central bank intervention and foreign exchange rates: new evidence from FIGARCH estimations

Cited by 136 publications

References 26 publications

Econometric Modeling of Exchange Rate Volatility and Jumps

Econometric Modeling of Exchange Rate Volatility and Jumps

An assessment of some open issues in the analysis of foreign exchange intervention

Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review

Contact Info

Product

Resources

About