Research articles for the 2019-11-28
Exchange of information and bank deposits in international financial centres
RePEC
This paper assesses the impact of exchange of information on foreign-owned bank deposits in international financial centres (IFCs). Based on a dataset with extended jurisdiction coverage and sample length, foreign-owned IFC deposits declined globally by 24% or USD 410 billion during the period from 2008 to 2019. The commencement of automatic exchange of information is associated on average with a statistically significant 22% reduction in IFC bank deposits held by non-IFC counterparty jurisdictions. The results show that exchange of information on request was associated with a reduction of around 10% during the early years of implementation. Robustness checks show that voluntary disclosure programmes do not drive the results. These findings highlight the effectiveness of the expansion of automatic exchange of informationand provide further evidence of the success of a comprehensive multilateral approach towards international tax transparency.
RePEC
This paper assesses the impact of exchange of information on foreign-owned bank deposits in international financial centres (IFCs). Based on a dataset with extended jurisdiction coverage and sample length, foreign-owned IFC deposits declined globally by 24% or USD 410 billion during the period from 2008 to 2019. The commencement of automatic exchange of information is associated on average with a statistically significant 22% reduction in IFC bank deposits held by non-IFC counterparty jurisdictions. The results show that exchange of information on request was associated with a reduction of around 10% during the early years of implementation. Robustness checks show that voluntary disclosure programmes do not drive the results. These findings highlight the effectiveness of the expansion of automatic exchange of informationand provide further evidence of the success of a comprehensive multilateral approach towards international tax transparency.
Forecasting Emerging Market FX Spot Rates: An AR(1) approach
SSRN
This paper outlines a method to forecast FX spot rates. The data set consists of the Bloomberg FX spot rates for emerging markets as defined by Bloomberg. The in-sample data set consisted of weekly FX spot rates for ten Emerging markets, from August 2013 to March 2019. The out sample spanned March to November 2019. PACF tests revealed that the most appropriate model would be an AR(1). After applying the AR(1) model to the data a combination of AIC and Log-Likelihood criteria as well as a sigma squared measure were applied to determine the spot rates with the best fit. 3 spot rates remained that had the best fit relative to the other spot rates given that the sample sizes were identical. Applying the relevant AR(1) models to the out-sample data highlighted that using a long-only approach, to avoid short side risk, produced negative returns in all three FX spot rates. The use of an out-sample to test the applicability of the AR(1) forecast supplements the within model criteria: AIC, Log-likelihood and sigma squared. The out-sample results highlight that in practice an AR(1) model may not necessarily produce positive returns in Emerging FX markets.
SSRN
This paper outlines a method to forecast FX spot rates. The data set consists of the Bloomberg FX spot rates for emerging markets as defined by Bloomberg. The in-sample data set consisted of weekly FX spot rates for ten Emerging markets, from August 2013 to March 2019. The out sample spanned March to November 2019. PACF tests revealed that the most appropriate model would be an AR(1). After applying the AR(1) model to the data a combination of AIC and Log-Likelihood criteria as well as a sigma squared measure were applied to determine the spot rates with the best fit. 3 spot rates remained that had the best fit relative to the other spot rates given that the sample sizes were identical. Applying the relevant AR(1) models to the out-sample data highlighted that using a long-only approach, to avoid short side risk, produced negative returns in all three FX spot rates. The use of an out-sample to test the applicability of the AR(1) forecast supplements the within model criteria: AIC, Log-likelihood and sigma squared. The out-sample results highlight that in practice an AR(1) model may not necessarily produce positive returns in Emerging FX markets.
