The goal of this paper is to assess quantitatively the impact that the emergence of China in the international markets during the 1990s had on the U.S. economy (i.e. the so-called China Shock). To do so, I build a model with two sectors producing two final goods, each of them using as the only input of production an intermediate good specific to each sector. Final goods are produced in a perfectly competitive environment. The intermediate goods are produced in a frictional environment with labor as the only input. First I calibrate the close economy model to match some salient stylized facts from the 1980s in the U.S. Then to assess the China Shock I introduce a new country (China) in the international scene. I proceed with two calibration strategies: (i) calibrate China such that it matches the variation in the price of imports relative to the price of exports for the U.S. between the average of the 1980s and the average of 2005-2007, (ii) Calibrate China such that variation in allocations are close to the ones observed in data, for the same window of time. I found that under calibration (i) the China Shock in the model explains 26.38% of the variation in the share of employment in the manufacturing sector, 16.28% of the variation in the share of manufacturing production and 27.40% of the variation in the share of wages of the manufacturing sector. Finally, under calibration (ii) I found that the change in relative price needed to match between 80 to 90 percent of the variation in allocations is around 3.47 times the one observed in data.
Conclusions and key results:
According to the model, the China Shock explains 26.35% of the variation in the share of manufacture employment, 16.28% of the variation in the share of manufacturing production and 27.44% of the variation in the share of wages of the manufacturing sector. The first of these results is consistent with findings in Autor et al. (2013). On the other hand, the variation in the unemployment rate of the economy is not matched, neither for the first nor the second calibration of the open economy. I also found that as a consequence of the China Shock, real wages increase when measuring them in terms of the price of the import good, and decrease when measured in terms of the price of the export good. This result is not in line with findings in Autor et al. (2013). The optimal unemployment insurance in the open economy is 6.13% of average wages higher than in the close economy because the unemployment rate of the open economy is higher than in the close economy (0.9% difference). Finally, the model generates a non-traditional source of comparative advantage, arising from differences in the relative bargaining power of workers.
In this paper we study the dynamics and drivers of 10 year’s sovereign bond yields using a panel of the original 11 Eurozone countries (excluding Luxembourg). The interest of this study relies on the fact that despite very different macroeconomic policy stances in the variables that we believe determine interest rates among these countries, 10 years Eurozone bond yields almost perfectly converged during the 2000’s, before they suffered a sudden disconnection in the aftermath of the Great Financial Crisis.
To this end, we apply two different methodologies. A Panel Data approach (that we end discarding) and a Time Varying Coefficients model using the Kalman Filter, which allows us for capturing changes in the pricing mechanism of bond yields over time. Initially, by using the latter methodology without controlling for the volatility of the interest rates (which dramatically increased after 2008), we obtain very noisy results that are barely explainable, since the coefficients seem to be capturing these changes in volatility. Once we introduce in the filter a GARCH process for the variance-covariance matrix of the interest rates that we use in the Time Varying Coefficients approach, we manage to obtain much more meaningful and explicative results.
One of our key contributions is the inclusion of new fiscal and macroeconomic variables as determinants of yields in the different Eurozone countries, which were discarded by other studies in the field. We also contribute by controlling by common determinants to all the Eurozone countries, which we obtained by applying a common component approach. Furthermore, our findings confirm that after the period of divergence in interest rates, started in the aftermath of the Great Financial Crisis, and caused by a refocus on fundamentals, Eurozone interest rates have converged again under the effect of a normalization of bond yield drivers, similarly to their pre-crisis levels, although not to the same extent. Another implication that we find is that in times of economic uncertainty and financial hysteresis, when default risk becomes an issue, the effects of government policy on interest rates can significantly lead to accentuated crowding out effects.
Conclusions and key results:
Our work indicates that there has been a significant break in the way sovereign debt was priced after the Great Financial Crisis of 2008, indicating a return to fundamentals as main drivers of sovereign yields. We find that several factors reflective of fiscal and macroeconomic stances became increasingly important during the crisis, after having been ignored in previous years. As such, Debt to GDP, Deficit to GDP, GDP growth and Current Account balances to GDP, among others, started to play important roles in the determination of long term interest rates for Eurozone government bonds. In line with previous research, our findings confirm the existence of 3 distinct phases in the euro bond market. A period of high integration, a period of disintegration, and a phase of partial reintegration (Adam and Lo Duca (2017)).
Our findings suggest that during periods of economic uncertainty characterized by high volatility in the financial markets, investors tend to focus on fundamentals, while in times of economic boom they do not discriminate too much among the different stance of these macroeconomic determinants. This finding has important policy implications since it suggests that during economic crises interest rates react much more to unsustainable fiscal policies and macroeconomic imbalances than during calmer times, causing a great private sector crowding out effect (Laubach (2011)).
Therefore, our results suggest that governments should pay closer attention to their fiscal stances during times of economic turbulence in order to avoid the detrimental effects of high interest rates on activity in a period of economic agent´s lack of confidence. As argued before by De Grauwe and Ji (2013), this former effect is exacerbated by the fact that Eurozone governments have no control over monetary policy, making impossible for them to reduce interest rates by no other means than sound fiscal policies. In line with this result, we notice that the ECB’s unconventional monetary policy (we obtain that the impact of short term interest rates -one of our common determinants obtained by principal components- on long yields has diminished over time) helped to bring down European bond yields after 2014. This fact contributed to put the fiscal stances of these countries, and other essential macroeconomic variables, back to sustainable levels, that along with the structural reforms carried out (which in addition to the former effect, have also contributed to bring back economic confidence and dynamism) have had by its own another loosening impact in the interest rates that these countries have been facing in every debt issuance.
Regarding the methodologies used to address our research question, we were able to obtain robust results and determine which method was the most appropriate to investigate the drivers of 10 year’s sovereign bond yields. We found that panel data approaches, which are widely used in the literature, lead to unstable and unsatisfactory results, causing us to attach limited credibility to the outcomes of such analysis. However, the Time Varying Coefficients approach seems more reliable and yields more robust and plausible results after we model the changes in volatility appropriately. We believe that having a larger sample (we use the forecasts released twice a year by the IMF in its World Economic Outlook and by the OECD in their Economic Outlook in order to control by the effect of the market´s forward looking in current levels of interest rates, as well as by reverse causality) would have allowed us to obtain more reliable results on this approach as well.
A suggestion for further research would be to apply Bayesian techniques to estimate our model. Indeed, given the limited amount of data available and the complexity of our models, these methods seem to suit better in this kind of estimation, where the great amount of parameters, as well as the possible presence of non-linearities, can make the optimization process very costly. Consequently, this methodology would have allowed us to also model the variance of the Time Varying Parameters, and not only the ones of the interest rates (our observables) with another GARCH or stochastic volatility process, since we expect that these variances could also follow a conditional process, which might have an impact on our estimation results.
We’ve just come across some articles written by several Barcelona GSE Alumni who are now Research Assistants and Economists at Caixabank Research in Barcelona. New articles are published each month on a range of topics.
Below is a list of all the alumni we found listed as article contributors, as well as their most recent publications in English (click each author to view his or her full list of articles in English, Catalan, and Spanish).
If you’re an alum and you’re also writing about Economics, let us know where we can find your stuff!
Gerard Arqué (Master’s in Macroeconomic Policy and Financial Markets ’09)
By Cox Bogaards, Marceline Noumoe Feze, Swasti Gupta, Mia Kim Veloso
Almost a year since the UK voted to leave the EU, uncertainty still remains elevated with the UK’s Economic Policy Index at historical highs. With Theresa May’s snap General Election in just under two weeks, the Labour party has narrowed the gap from Conservative lead to five percentage points, which combined with weak GDP data of only 0.2 per cent growth in Q1 2017 released yesterday, has driven the pound sterling to a three-week low against the dollar. Given potentially large repurcussions of market sentiment and financial market volatility on the economy as a whole, this series of events has further emphasised the the need for policymakers to implement effective forecasting models.
In this analysis, we contribute to ongoing research by assessing whether the uncertainty in the aftermath of the UK’s vote to leave the EU could have been predicted. Using the volatility of the Pound-Euro exchange rate as a measure of risk and uncertainty, we test the performance of one-step ahead forecast models including ARCH, GARCH and rolling variance in explaining the uncertainty that ensued in the aftermath of the Brexit vote.
The UK’s referendum on EU membership is a prime example of an event which perpetuated financial market volatility and wider uncertainty. On 20th February 2016, UK Prime Minister David Cameron announced the official referendum date on whether Britain should remain in the EU, and it was largely seen as one of the biggest political decisions made by the British government in decades.
Assessment by HM Treasury (2016) on the immediate impacts suggested “a vote to leave would cause an immediate and profound economic shock creating instability and uncertainty”, and in a severe shock scenario could see sterling effective exchange rate index depreciate by as much as 15 percent. This was echoed in responses to the Centre for Macroeconomics’ (CFM) survey (25th February 2016), where 93 percent of respondents agreed that the possibility of the UK leaving the EU would lead to increased volatility in financial markets and the broader economy, expressing uncertainty about the post-Brexit world.
Resonating these views, the UK’s vote to leave the EU on 23rd June 2016 indeed led to significant currency impacts including GBP devaluation and greater volatility. On 27th June 2016, the Pound Sterling fell to $1.315, reaching a 31-year low against the dollar since 1985 and below the value of the Pound’s “Black Wednesday” value in 1992 when the UK left the ERM.
In this analysis, we assess whether the uncertainty in the aftermath of the UK’s vote to leave the EU could have been predicted. Using the volatility of Pound-Euro exchange rate as a measure of risk and uncertainty, we test the performance of one-step ahead forecast models including ARCH, GARCH and rolling variance. We conduct an out-of-sample forecast based on models using daily data pre-announcement (from 1st January 2010 until 19th February 2016) and test performance against the actual data from 22nd February 2016 to 28th February 2017.
Descriptive Statistics and Dynamic Properties
As can be seen in Figure 1, the value of the Pound exhibits a general upward trend against the Euro over the majority of our sample. The series peaks at the start of 2016, and begins a sharp downtrend afterwards. There are several noticeable movements in the exchange rate, which can be traced back to key events, and we can also comment on the volatility of exchange rate returns surrounding these events, as a proxy for the level of uncertainty, shown in Figure 2.
Figure 1: GBP/EUR Exchange Rate
Source: Sveriges Riksbank and authors’ calculations
Notably, over our sample, the pound reached its lowest level against the Euro at €1.10 in March 2010, amid pressure from the European Commission on the UK government to cut spending, along with a bearish housing market in England and Wales. The Pound was still recovering from the recent financial crisis in which it was severely affected during which it almost reached parity with the Euro at €1.02 in December 2008 – its lowest recorded value since the Euro’s inception (Kollewe 2008).
However, from the second half of 2011 the Pound began rising against the Euro, as the Eurozone debt crisis began to unfold. After some fears over a new recession due to consistently weak industrial output, by July 2015 the pound hit a seven and a half year high against the Euro at 1.44. Volatility over this period remained relatively low, except in the run up to the UK General elections in early 2015.
However, Britain’s vote to leave the EU on 23rd June 2016 raised investors’ concerns about the economic prospects of the UK. In the next 24 hours, the Pound depreciated by 1.5 per cent on the immediate news of the exit vote and by a further 5.5 per cent over the weekend that followed, causing volatility to spike to new record levels as can be seen in Figure 2.
Figure 2: Volatility of GBP/EUR Exchange Rate
Source: Sveriges Riksbank and authors’ calculations
As seen in Figure 1, the GBP-EUR exchange rate series is trending for majority of the sample, and this may reflect non-stationarity in which case standard asymptotic theory would be violated, resulting in infinitely persistent shocks. We conduct an Augmented Dickey Fuller test on the exchange rate and find evidence of non-stationarity, and proceed by creating daily log returns in order to de-trend the series. Table 1 summarises the first four moments of the log daily returns series, which is stationary.
Table 1: Summary Statistics
Source: Sveriges Riksbank and authors’ calculations
The series has a mean close to zero, suggesting that on average the Pound neither appreciates or depreciates against the Euro on a daily basis. There is a slight negative skew and significant kurtosis – almost five times higher than that of the normal distribution of three – as depicted in the kernel density plot below. This suggests that the distribution of daily returns for the GBP-EUR, like many financial time series, exhibits fat tails, i.e. it exhibits a higher probability of extreme changes than the normal distribution, as would be expected.
To determine whether there is any dependence in our series, we assess the autocorrelation in the returns. Carrying out a Ljung-Box test using 22 lags, as this corresponds to a month of daily data, we cannot reject the null of no autocorrelation in the returns series, which is confirmed by an inspection of the autocorrelograms. While we find no evidence of dependence in the returns series, we find strong autocorrelations in the absolute and squared returns.
The non-significant ACF and PACF of returns, but significant ACFs of absolute and squared returns indicate that the series exhibits ARCH effects. This suggests that the variance of returns is changing over time, and there may be volatility clustering. To test this, we conduct an ARCH-LM test using four lag returns and find that the F-statistic is significant at the 0.05 level.
For the in-sample analysis we proceed using the Box-Jenkins methodology. Given the evidence of ARCH effects and volatility clustering using an ARCH-LM test but lack of any leverage effects in line with economic theory, we proceed to estimate models which can capture this: ARCH (1), ARCH (2), and the GARCH (1,1). Estimation of ARCH (1) suggests low persistence as captured by α1 and relatively fast mean reversion. The ARCH(2) model generates greater persistence measured by sum of α1 and α2 and but still not as large as the GARCH(1,1) model, sum of α1 and β as shown in table 2.
Table 2: Parameter Estimates
We proceed to forecast using the ARCH(1) as it has the lowest AIC and BIC in-sample, and GARCH (1,1) which has the most normally distributed residuals, no dependence in absolute levels, and the largest log-likelihood. We compare performance against a baseline 5 day rolling variance model.
Figure 3 plots the out of sample forecasts of the three models (from 22nd February 2016 to 28th February 2017). The ARCH model is able to capture the spike in volatility surrounding the referendum, however the shock does not persist. In contrast, the effect of this shock in the GARCH model fades more slowly suggesting that uncertainty persists for a longer time. However neither of the models fully capture the magnitude of the spike in volatility. This is in line with Dukich et al’s (2010) and Miletic’s (2014) findings that GARCH models are not able to adequately capture the sudden shifts in volatility associated with shocks.
Figure 3: Volatility forecasts and Squared Returns (5-day Rolling window)
We use two losses traditionally used in the volatility forecasting literature namely the quasi-likelihood (QL) loss and the mean-squared error (MSE) loss. QL depends only on the multiplicative forecast error, whereas the MSE depends only on the additive forecast error. Among the two losses, QL is often more recommended as MSE has a bias that is proportional to the square of the true variance, while the bias of QL is independent of the volatility level. As shown in table 3, GARCH(1,1) has the lowest QL, while the ARCH (1) and rolling variance perform better on the MSE measure.
Table 3: QL & MSE
Table 4: Diebold- Mariano Test (w/5-day Rolling window)
Employing the Diebold-Mariano (DM) Test, we find that there is no significance in the DM statistics of both the QL and MSE. Neither the GARCH nor ARCH are found to perform significantly better than the 5-day Rolling Variance.
In this analysis, we tested various models to forecast the volatility of the Pound exchange rate against the Euro in light of the Brexit referendum. In line with Miletić (2014), we find that despite accounting for volatility clustering through ARCH effects, our models do not fully capture volatility during periods of extremely high uncertainty.
We find that the shock to the exchange rate resulted in a large but temporary swing in volatility but this did not persist as long as predicted by the GARCH model. In contrast, the ARCH model has a very low persistence, and while it captures the temporary spike in volatility well, it quickly reverts to the unconditional mean. To the extent that we can consider exchange rate volatility as a measure of risk and uncertainty, we may have expected the outcome of Brexit to have a long term effect on uncertainty. However, we observe that the exchange rate volatility after Brexit does not seem significantly higher than before. This may suggest that either uncertainty does not persist (unlikely) or that the Pound-Euro exchange rate volatility does not capture fully the uncertainty surrounding the future of the UK outside the EU.
Abdalla S.Z.S (2012), “Modelling Exchange Rate Volatility using GARCH Models: Empirical Evidence from Arab Countries”, International Journal of Economics and Finance, 4(3), 216-229
Allen K.and Monaghan A. “Brexit Fallout – the Economic Impact in Six Key Charts.” www.theguardian.com. Guardian News and Media Limited, 8 Jul. 2016. Web. Accessed: March 11, 2017
Brownlees C., Engle R., and Kelly B. (2011), “A Practical Guide to Volatility Forecasting Through Calm and Storm”, The Journal of Risk, 14(2), 3-22.
Centre for Macroeconomics (2016), “Brexit and Financial Market Volatility”. Accessed: March 9, 2017.
Cox, J. (2017) “Pound sterling falls after Labour slashes Tory lead in latest election poll”, independent.co.uk. Web. Accessed May 26, 2017
Diebold F. X. (2013), “Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests”. Dukich J., Kim K.Y., and Lin H.H. (2010), “Modeling Exchange Rates using the GARCH Model”
HM Treasury (2016), “HM Treasury analysis: the immediate economic impact of leaving the EU”, published 23rd May 2016.
Sveriges Riksbank, “Cross Rates” www.riksbank.se. Web. Accessed 16 Feb 2017
Taylor, A. and Taylor, M. (2004), “The Purchasing Power Parity Debate”, Journal of Economic Perspectives, 18(4), 135-158.
Van Dijk, D., and Franses P.H. (2003), “Selecting a Nonlinear Time Series Model Using Weighted Tests of Equal Forecast Accuracy”, Oxford Bulletin of Economics and Statistics, 65, 727–44.
Tani, S. (2017), “Asian companies muddle through Brexit uncertainty” asia.nikkei.com. Web. Accessed: May 26, 2017
In this post, Sascha Buetzer ’11 (Macro) shares his experience of giving a talk at TEDx Gothenburg.
Barcelona GSE Macroeconomics and Financial Markets alum Sascha Buetzer ’11 is currently a PhD candidate in Economics at the University of Munich. After starting his career at the European Central Bank, he has been working as an economist in the International Monetary Affairs Division at the Deutsche Bundesbank, primarily in an advisory role for the German executive director at the International Monetary Fund.
In this post, Sascha shares his experience of giving a talk at TEDx Gothenburg in November 2016. The video is included below.
Last November I was given the unique opportunity to present my ideas to an audience that usually doesn’t get to hear much about the inner workings of the financial system and central banking. At the TEDx Conference “Spectrum” in Gothenburg, Sweden, I was one of 10 speakers who were given not more than 20 minutes to convey an “idea worth spreading.”
My talk centered around how money is created in a modern economy and what can be done to improve upon this process, in particular in the context of the euro crisis. Quite a challenge, as it turned out, given the short amount of time and the need to keep things understandable and entertaining since most of the audience did not have an economic background.
It was, however, an extremely exciting and rewarding experience.
After getting in touch with the organizers through a chance encounter at the Annual Meeting of the Asian Development Bank (ADB) last year, all speakers got an individually assigned coach who provided excellent feedback and recommendations for the talk.
The day of the conference itself was thoroughly enjoyable (at least after the initial rush of adrenaline had subsided). It was fascinating to interact with people from widely varying backgrounds that all shared a natural curiosity and desire to learn from each other. And at the end of the day it felt great for having been able to contribute to this, by providing people with insight into a topic so important, yet so little-known.
Our project focuses on the assessment of a potential relationship between monetary policy actions and economic inequality in form of the Gini index. The analysis is conducted for the United States on a country- as well as on a state-level. Lack of quality and comparability of inequality data is a major empirical challenge in the research on inequality in general. Thus, we use different methodology for the data on the Gini index to ensure the robustness of the main results on the country level. Moreover, the main contribution of this work is the analysis on a more dis-aggregated level, i.e. the state level and a regional level. The state- and region- level analysis provide a further line of investigation with respect to the relationship between monetary policy and inequality. When considering monetary policy effects on inequality on an aggregated country level, one cannot be sure whether potentially heterogeneous reactions of inequality within the country are washed away by the aggregation over states. Therefore, this paper does not only analyse the relation between monetary policy and inequality on the aggregate country level, but also on the state level. Furthermore, as a first step in the direction of investigating potential transmission channels of monetary policy into inequality, further tests with regards to the initial wealth levels across states are conducted.
In this paper we analyzed the implication of a monetary policy shock for inequality on a country-level, regional-level and (on a) state-level (as well). The results are largely consistent across different model specifications and considering different geographic levels, implying that a contractionary monetary policy shock seems to raise inequality on impact. Although the effect is small, it is consistently positive across states and regions. Already the fact that we find that there is an effect of monetary policy on inequality is a contribution in itself.
Our benchmark model using OLS leads to the conclusion that a contractionary monetary policy shock leads to an increase of inequality. This finding holds for richer as well as poorer states. The contemporaneous impact stays positive over the considered time horizon. We have run several robustness checks using different model specification for OLS as well as a VAR approach. These checks have confirmed the earlier findings.
However, we have stressed that there is room for further investigation to shed light in this area.One potential way to go would be to expand the analysis by using alternative inequality measures. We conducted our analyses using the Theil and Atkinson indices obtained from Frank’s website. However, since the results obtained by using these two measures do not add anything to the analysis, we have not included them in the paper.
Furthermore, some authors stress the relevance of the top income and low income distribution when analyzing the effects of inequality. Therefore, one might think about controlling for the share of top incomes within the states to asses to what extent the reaction to monetary policy would change. We already tried capturing parts of these potential dynamics using the simple mean comparison tests for wealth dependence.
We find evidence that (seems to) suggest that monetary policy has a stronger impact on wealthy states and regions compared to poorer states and regions. Those results are obtained using simple mean-comparison tests and should be viewed as preliminary, as further research on the issue is necessary to concretely conclude whether the initial wealth-level of states and regions is relevant for the transmission mechanism from monetary policy on inequality.
After our results show that there is an effect of monetary policy on inequality, the next step in this line of research could be the investigation of the actual transmission mechanism. One could include a more elaborate analysis of the potential channels through which monetary policy affects inequality.
As a last remark, we want to point out that we were originally interested in investigating this topic for the European Monetary Union member states. This, however, is not possible due to data limitations. Firstly, across European countries there is no uniform definition and measurement of inequality. Secondly, the monetary union is fairly new and the data of a unitary monetary policy shock would be too short to conduct our analysis. Nevertheless, we do believe this is an interesting path for future research and, as pointed out in the literature review, the finding of a robust relationship between monetary policy and inequality backs this claim.
Editor’s note: This post is part of a series showcasing Barcelona GSE master projects by students in the Class of 2016. The project is a required component of every master program.
Authors: Nagham Abdel Ahad and Gleb Bychkov
Macroeconomic Policy and Financial Markets
In the light of the Syrian crisis which erupted in March 2011 and which is still on-going, many outcomes were and are still being produced. In this paper, we show particular interest in the refugee crisis that developed after the fast-tracked evolution of events in Syrian. More precisely, we shed light on the case of Lebanon, the small country on the Mediterranean, having an estimated native population of 4.55 millionand hosting, at present, an estimated 1.15 million Syrian refugeeson its territories, that is, more than 25% of its original population.
Our study focuses more specifically on the negative spillovers of the Syrian refugee inflow on the Lebanese labor market. Our objective is to build a model which we use to determine both the steady state in the Lebanese labor market prior to the Syrian refugee crisis and the equilibrium in the Lebanese labor market post the escalation of the refugee crisis. We therefore approach the dynamics of the labor market observing its reaction to the positive labor supply shock generated by the refugee influx. After calibrating the model, we watch closely the changes in our main variables of interest, namely, unemployment rates and wage levels, before and after the crisis.
In addition, we compare the results and the values our model gives for our variables of interest with the actual figures and data published or predicted by international reputable institutions, such as The World Bank and the Food and Agriculture Organization of the United Nations, for these same variables. Accordingly, we evaluate our model showing how far it succeeds in reflecting the reality of the situation and thus in predicting and generating figures as close as possible to the actual and true ones.
Refugee inflows into host countries and communities can have significant impacts on these hosts on many levels. In our paper, we approach this issue from an economic perspective. More specifically, we focus on labor economics and labor market dynamics. In this context, we consider the case of the Lebanese labor market invaded by Syrian refugees who have fled to Lebanon because of the on-going war in Syria. We build a model, we calibrate it, we get the results, and we discuss them and use them to evaluate the performance of our model.
While The World Bank estimates a 20 percent unemployment rate in Lebanon post-crisis (almost double the rate pre-crisis), our model estimates an approximate 6.68 percent.
We proceed afterwards with a thorough discussion centered around these contradictory observations and we also go over a couple of limitations our model has, all of which might be able to account to some extent to the inconsistency in figures. This idea is interesting as it opens horizons and broadens the scopes for this work as one might expand the model so as to include an informal sector or additional distinctions between Lebanese native workers and Syrian migrant workers. We did not engage in doing this activity given the time constraints that we had. However, such expansions of the model can add great value to this work and can pave the way for further understanding and more successful outcomes.
We view our paper as a first step towards developing a flexible quantitative model that integrates opposing forces and that allows for a proper welfare analysis. More analysis is clearly welcome.
Research concerning the linkage between global liquidity and domestic economic affairs is hardly new. Interestingly, however, it never gets old mainly because of its policy significance. The welfare impact of shocks to capital flows (may it be short or long-term) is by and large the bottom line of all the discussions. Inquiries about other pertinent issues such as global financial imbalance and asset price bubbles, international financial stability and global financial safety nets, and economic early warning systems are in one way or the other broadly tied with global liquidity. Indeed, the impact of shocks to global liquidity can be systemically disruptive. And because international financial landscape constantly changes (i.e. degree of linkages estimated in one period may not hold in the subsequent periods), regular spot checks are important. The aftershocks of the global financial crisis (GFC) in 2007/2008, for instance, re-emphasize the significance of understanding the consequences of fluxes in capital movement and the extent of these consequences in various settings and time periods.
The main motivation behind this study is to contribute to empirical literature on cross-border liquidity spillover effects on asset prices in light of broadening global economic integration. We decided to focus on the case of the Group of 7 (G7) economies (e.g. Canada, France, Germany, Italy, Japan, United Kingdom, and United States) and follow closely the earlier work of Darius and Radde (2010) – henceforth D&R. For the same set of economies, D&R looked at the relationship of global liquidity and asset prices before and after the “Great Moderation” period. In an attempt to provide an account of what happened after 2007, this study examines the behavior of the same variables until end of 2015 and checks whether there are significant changes to the magnitude of the pass though effects of global liquidity, particularly on the equity and property prices in recent years.
In light of the developments in the past decade that led central banks to flood the international financial system with liquidity, we deem it relevant to empirically re-examine the linkage between global liquidity and asset prices in large economies. To do the econometric analysis, we used available data from 1984q1 to 2015q4 and employed a VAR model following the specification suggested by D&R.
In the global analysis, we found that global liquidity has a positive significant impact on commodity prices using the sample from 1984q1 and 2015q4 but insignificant impact on equity prices. However, in our subsample analysis using the data from 1984q1 to 2007q4, our results showed that the impulse responses of both the CRB and the MSCI were positively significant and persistent while the impulse response function of house prices remained insignificant. Interestingly, D&R, which also used 1984q1 to 2007q4 as its Great Moderation subsample, found that the responses of commodity and equity prices to a liquidity shock were insignificant. In terms of the house prices, the results we obtained differed from those of D&R for periods from 1984q1 to 2007q4 in a sense that we found significantly negatively response to global liquidity albeit with a substantial lag of 16 quarters.
In our spillover analysis, we extended the model of D&R by adding the local stock prices to the model. For each economy, we ran the regression using data from 1984q1 to 2015q4 as well as from 1984q1 to 2007q4 (to serve as our pre-GFC subsample). The results of this exercise convey that the positive effect of a global liquidity shock on house prices in Japan obtained using data from 1984q1 to 2015q period disappears when only pre-GFC period is considered. In the full sample analysis both global and domestic liquidity did not affect stock prices in Japan, whereas the effect of global liquidity turned out to be positive for the pre-GFC period.
Notwithstanding the sample used (may it be full or pre-GFC), the effect of global liquidity on house prices in France stays significant and negative, while the negative impact of global liquidity on stock prices obtained using full sample disappears in pre-GFC subsample. In the case of the latter, the stock prices turned out to be significantly positively affected by local liquidity, while the inclusion of post crisis years made this response negatively significant with a substantial lag.
Lastly, the result of the pre-GFC subsample analysis involving the UK reveals that the effect of local liquidity shock on stock prices is not significant as opposed to full sample estimation when the effect was positive. Moreover, the variance decomposition dictates that global GDP growth rate explains the largest proportion of the volatility of stock prices in the UK.
Moving forward, one way to get a better understanding of the results would be to properly assess the country-level intertemporal idiosyncratic factors just like in the global analysis. Certainly, the nature and timing of these structural shifts can vary from one country to another. We likewise suggest trying different proxies for the global liquidity or run the model for the monthly data without house prices that are available only quarterly and the monthly proxy for GDP. Using monthly data would allow a closer analysis of dynamics in the post-GFC period. It would also be interesting to extend the scope of this exercise to emerging economies.
We want to know what the BGSE community is thinking and reading about the Brexit.
We invite all Barcelona GSE students and alumni to share their early reflections on the potential economic consequences of the UK’s recent vote to leave the EU. Did you focus on a related topic in your master project? Are you working at a think tank, central bank, or consulting firm where your projects will be impacted by this decision? Have you seen any articles or links that you found useful for understanding what lies ahead?
Here are a couple of pieces we’ve found to get the discussion going:
The BGSE participates in A Dynamic Economic and Monetary Union (ADEMU), a project of the EU Horizon 2020 Program. Last week, ADEMU researchers held a webinar to discuss the Brexit.
Europe has grown out of its crises when reason and solidarity have prevailed, but it has also been devastated by its crises when fear and nationalism have taken the lead. Brexit, in the aftermath of the euro crisis, brings this dichotomy back to the foreground. Since 2010 there have been important advances in the development of the Economic and Monetary Union (EMU) and flexible forms of participation have allowed other EU countries, reluctant to join the euro, to share the basic principles that define the EU and have a common presence in the interdependent global world.
According to the panelists, Brexit raises 3 crucial questions:
Should the EMU be accelerated to become a centre of gravity within the EU, or slowed down to avoid a centrifugal diaspora? If accelerated, how?
Should an ‘exit’ country be allowed free entry to the single market and other EU public goods without accepting freedom of movement?
Should the EU remain as it is, or increase its capacity to offer common public services (Banking Union, border security, research funding, environment, etc.), or limit its scope of activity to the EU single and integrated market?
– Joaquín Almunia (Former Vice-President of the European Commission, honorary president of the Barcelona GSE)
– Ramon Marimon (European University Institute and UPF – Barcelona GSE; ADEMU)
– Gorgio Monti (European University Institute; ADEMU)
– Morten Ravn (University College London; ADEMU)
Annika Zorn (European University Institute; Florence School of Banking & Finance)
Nobel Laureate and Barcelona GSE Scientific Council member Joseph Stiglitz shares some reflections in the wake of the Brexit decision
What are you thoughts on Brexit?
We want to know what the BGSE community is thinking and reading about the Brexit. Please share your ideas, favorite sources for analysis, or observations from economists you respect in the comments below.
Editor’s note: This post is part of a series showcasing Barcelona GSE master projects by students in the Class of 2015. The project is a required component of every master program.
Adrian Ifrim and Önundur Páll Ragnarsson
Macroeconomic Policy and Financial Markets
We propose a new identification method in open economy models by restricting both the systematic component of monetary policy and the IRFs to a monetary policy shock, at the same time remaining agnostic with respect to the effects of monetary policy shocks on output and open economy variables. We estimate the model for the U.S/U.K economies and find that a U.S monetary shock has a significant and permanent effect on output. Quantitatively a 0.4% annual increase in the interest rates causes output to contract by 1.2%. This contradicts the findings of Uhlig (2005) and Scholl and Uhlig (2008). We compute the long-run multipliers implied by the monetary policy reaction function and compare our identification with to the ones proposed by Uhlig (2005), Scholl and Uhlig (2008) and Arias et al. (2015). We argue that neither of the above schemes identify correctly the monetary policy shock since the latter overestimates the effects of the shock and the former implies a counterfactual behavior of monetary policy. We also find that the delayed overshooting puzzle is a robust feature of the data no matter what identification is chosen.
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