BSE Communications – Page 15

Financial Knowledge and Behavioral Economics as Means to a Wealthier Life

By Orestis Vravosinos (Economics ’18) – This article first appeared on the blog of Nudge Unit Greece.

In recent decades, financial literacy has been gaining more and more research interest with experts emphasizing that it can be conducive to more efficient financial decision-making. Given the tough economic situation in many countries around the world after the global financial crisis, the need for financial literacy has become even more imperative. Through financial literacy people can both achieve higher levels of wealth and better allocate it in order to make the most out of it or, as an economist would say, maximize their utility.

Before we start to examine the ways, in which financial literacy can inform our decisions, we first need to define financial literacy. An integrative definition stressing both financial knowledge and the ability to put it in practice has been proposed by Remund (2010, p. 284), who defines it as

“a measure of the degree to which one understands key financial concepts and possesses the ability and confidence to manage personal finances through appropriate, short-term decision-making and sound, long-range financial planning, while mindful of life events and changing economic conditions.”

Financial literacy and behavioral economics enhancing financial decision-making

Financial knowledge can enhance decision-making by raising awareness about some common behavioral errors people are susceptible to, when making financial decisions. Estelami (2009) argues that financial literacy programs could fight typical financial decision-making errors, such as hyperbolic discounting, short-term memory overload, anchoring effects, inaccurate risk perceptions and mental accounting. Similarly, Loerwald and Stemmann (2016) suggest that, when people become aware of some common human decision-making errors, they can better resist to making them, while the importance of financial education and financial literacy is also stressed by Altman (2012) and Shen (2014), who addresses overconfidence, anchoring and framing effects.

Another major mistake people do is that they often hold under-diversified portfolios. The importance of portfolio diversification in mitigating risk and achieving optimal return and risk combinations has long been acknowledged both in academia and by investment professionals. This crucial role of portfolio diversification has become even better acknowledged, since Harry Markowitz’s –recipient of The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel in 1990– seminal paper Portfolio Selection(1952)*. However, studies have shown that many households and individual investors hold under-diversified portfolios.

Fortunately, based on empirical evidence it appears that individuals with higher financial knowledge possess better diversified portfolios (Goetzmann and Kumar, 2008; Calvet, Campbell and Sodini, 2007; Guiso and Jiappelli, 2009; Abreu and Mendes, 2010; Kimball and Shumway, 2010).

Struggling against bad financial decisions

Nevertheless, as Estelami (2009) underlines, knowledge of financial matters cannot guarantee success in our financial decisions, as behavioral errors and biases in these decisions are often found to affect even the most financially knowledgeable. The father of Modern Portfolio Theory, whom we met just above, Harry Markowitz, admitted that he had used the 1/N heuristics or naive diversification; that is, he simply assigned equal weights to all assets in his portfolio without for example looking at the correlations among the included assets. He attributed this decision to regret aversion: “My intention was to minimize my future regret, so I split my retirement plan contributions 50/50 between bonds and equities.” (Mitra, 2003; Pompian, 2012).

What’s the takeaway?

Financial literacy and knowledge of our psychological and cognitive biases and errors are key factors that can help us lead a wealthier life. However, the battle against bad financial decisions is not a piece of cake for any of us. The best we can do is to start this learning journey in financial literacy and behavioral economics -which hopefully you have already done by clicking on these eye-catching hyperlinks in the text. That way, the next time we are buying a new 20.000€ car and are presented with these gorgeous 2.000€ accessories to buy with (because “come on, it’s peanuts, I’m already spending 20.000€ on the car”), we will know that we may be falling for mental accounting and “bundling”. Therefore, we need to think twice if we value these accessories that much; maybe these 2.000€ spent on something else would finally prove to be much more useful and make us a lot happier!

Note: The idea for the last example comes from the lecture Mental Accounting and Expenditures of the free online course Behavioral Finance, which you may well want to enjoy by clicking here.

*The concept of diversification had been known for many years before, but Markowitz’s work provided a solid theoretical framework and helped lay the foundations of Modern Portfolio Theory. For an assessment of the early history of portfolio theory, see Markowitz (1999).

References

Altman, M. (2012). Implications of behavioural economics for financial literacy and public policy. The Journal of Socio-Economics, 41(5), pp.677-690.

Estelami, H. (2009). Cognitive drivers of suboptimal financial decisions: Implications for financial literacy campaigns. Journal of Financial Services Marketing, 13(4), pp.273-283.

Loerwald, D. and Stemmann, A. (2016). Behavioral Finance and Financial Literacy: Educational Implications of Biases in Financial Decision Making. In: C. Aprea, E. Wuttke, K. Breuer, N. Koh, P. Davies, B. Greimel-Fuhrmann and J. Lopus, ed., International Handbook of Financial Literacy, 1st ed. Springer Singapore, pp.25-38.

Markowitz, H. (1999). The Early History of Portfolio Theory: 1600-1960. Financial Analysts Journal, 55(4), 5-16.

Rasiel, E. & Forlines, J. (2016). Mental Accounting and Expenditures. Lecture, Behavioral Finance by Duke University on coursera.org.

Shen, N. (2014). Consumer rationality/irrationality and financial literacy in the credit card market: Implications from an integrative review. Journal of Financial Services Marketing, 19(1), pp.29-42.

BGSE Data Talks: Professor Piotr Zwiernik

The Barcelona GSE Data Science student blog has a new post featuring an interview with Piotr Zwiernik (UPF and BGSE), Data Science researcher and professor in the BGSE Data Science Master’s Program.

Hello and welcome to the second edition of the „Data Talks“ segment of the Data Science student blog. Today we have the honor to interview Piotr Zwiernik, who is assistant professor at Universitat Pompeu Fabra. Professor Zwiernik was recently awarded the Beatriu de Pinós grant from the Catalan Agency for Management of University and Research Grants. In the Data Science Master’s Program he teaches the maths brush-up and the convex optimization part of the first term class „Deterministic Models and Optimization“. Furthermore, he is one of the leading researchers in the field of Gaussian Graphical Models and algebraic statistics. We discuss his personal path, the fascination for algebraic statistic as well as the epistemological question of low-dimensional structures in nature…

Read the full interview on the Barcelona GSE Data Scientists blog

Five lessons from a one-week meeting with 18 Nobel Laureates

By Fernando Fernández (Economics ’13, GPEFM)

Photo credit: Lindau Nobel Laureate Meeting

By Fernando Fernández (Economics ’13, GPEFM) ^[1]

“Just when we thought we had all the answers, all the questions changed.” Mario Benedetti

That was my reaction when the 6th Lindau Meeting in Economic Sciences concluded. This meeting occurs every two years and gathers several Nobel Laureates and young economists (graduate students and assistant professors) from around the world. This meeting is certainly the most inspiring academic event I have ever attended.

The meeting took place in the beautiful town of Lindau, next to Lake Constance, in southern Germany between August 22nd and August 27th. During these days, we attended lectures from 18 Nobel laureates in Economics on a wide range of topics: bounded rationality, investment management, pension design, monetary policy, labor markets, morality and markets, political systems, innovation, and econometrics. I will not attempt to summarize these great lectures but all of them were recorded and are available on this link.

I would rather focus these lines on the interactions that occurred outside the “classroom”. Every day the program included lectures, lunch, seminar presentation panel discussions, and dinner.

The first lecture was given by Daniel McFadden ^[2], and besides the content, something really caught my attention. In the first row of the room (it was actually a theater) you could see the other Nobel Laureates. All were carefully listening to the speaker! They seemed like young students paying attention to an important professor. So the first lesson from this meeting was that we, as researchers, should actively embrace our academic curiosity.

Over lunch, I had the first opportunity to talk to a Nobel Laureate. I was sitting with some friends I just met and were talking about each others’ research. At some point, Bengt Holmstrom asked: “Would you mind if I join you?” We welcomed him, and seconds later he started asking us about our research interests. He soon realized that all of us were doing empirical work and said: “I am the only theorist in this table!”

He listened to all of us, asked some questions (some of them were hard to answer) and even gave us some advice. I was able to confirm that these brilliant economists have a special talent to listen to others, even if they are PhD students struggling with their papers. He was very generous with his time and recommended us to work hard but only on topics that we really cared about. He also advised us not to focus on publishing papers but instead on gaining respect from our peers through our work.

Hours later, I had the chance to sit on the table with Eric Maskin for dinner. He told us about the day he received the call from Stockholm and found out he won the Nobel prize. Then, we talked about US politics, big data, increasing co-authorship in economic journals, and other current issues in academia. As you can imagine, when you are sitting next to a Nobel Laureate you get the feeling that you can ask him any question. Well, these questions (some of them unrelated to economics) arrived and Maskin, very modestly, said : “I know very little about this particular topic, so I cannot have an informed opinion. In fact, you should know that one wins the Nobel prize, not because you know everything, but because you specialize in certain specific topics”. His reaction really impressed me but he was right. He could not be an expert in every topic and he acknowledged it. How many times do we feel the need to have an opinion on everything? The second lesson from this meeting is that we must always acknowledge our limitations and be humble enough to don’t give uninformed opinions.

One of the big questions most PhD students have is the following: where do great ideas come from? Tirole, Hart and Holmstrom provided some light on this issue and their advice was the third lesson. Tirole said two great sources of ideas were talking to people around you (his office was next to Hart’s) and to people outside the academia (practitioners, policy makers and business men). He encouraged us to talk to practitioners because they are facing the real problems we must address, that they have many important questions that remained unanswered and deserve our attention. Holmstrom said that the idea of his well-known model of career concerns (one of the reasons he was awarded with the Nobel prize) came when he has working in a plant in Finland, and had some problems with his manager. He then went to do his PhD and wrote a model to explain the behavior of this manager. In addition, he recommended us to become experts in the literature of our field of interest, not to follow it but to depart from it. After this, Hart said that working with Holmstrom and Tirole was a great way to find ideas. He also suggested us that when doing theoretical work, we should keep models as simple as possible.

James Heckman’s lecture was about the identification problem in econometrics. He was the most enthusiastic person I have ever seen giving an econometrics lecture. And this enthusiasm was quite contagious. Even though he was talking about highly technical and complex conditions for a new interpretation of Instrumental Variable (IV) estimates, I was surprisingly able to follow his lecture and understand the contribution he was making. Or, at least that’s the impression I had. That same day, we had a Bavarian dinner at night, with traditional music, food, and of course, beer. This was the last night of the event and the time to say good-bye to other fellow economists.

The coolest table at the Bavarian dinner

After some drinks, I decided to walk back to my hotel, located around 50-minutes away from the place we had dinner. On my way, I ran into Heckman, who seemed a bit confused. He had been walking with other young economists and then he was not sure where to go. I approached him and we realized we had to walk in the same direction. This was quite a unique and unexpected opportunity to talk about his lecture. So I started with my questions and he replied to all of them with great patience and enthusiasm. I could confirmed I had actually understood his lecture. Then, we started talking about the rapid increase in data availability and how big data should influence econometrics. He also told me good stories about his last trip to Barcelona and Peru. Eventually, we arrived at the hotel and said good-bye. This great conversation was the fourth lesson: we should remain enthusiastic even after years of dealing (doing research or teaching) with the same subject.

The fifth lesson is that these people seem very happy doing their jobs. Yes, I know, they are Nobel Laureates, they have already accomplished important professional goals. But it is still surprising how much they enjoy doing research. During lunch time or dinner, when we were able to talk to them more informally, people would usually ask: Which are the questions we should tackle? What fields are relevant now? Most Nobel Laureates seemed to share the view that the relevant questions are the ones you really care about. And if they actually work according to this view, it is not that hard to understand why they look like if they were having fun all the time.

When I was heading to this meeting, I had a lot of questions in my mind and thought the meeting would be an ideal place to get answers. During the meeting, some of my questions were being answered but later I realized that getting answers was not so important. Once the meeting was over, I realized all the lessons I took from it were unexpected. I had misunderstood the purpose of this meeting. I should have not come to the meeting looking for answers. I should have come looking for questions. These highly talented economists are Nobel Laureates precisely because they are extremely good at raising questions. Questions that open new streams of work. Questions that people had overlooked but that deserve careful thinking and attention. Now, two months after the meeting, I realize that all the questions raised by these Nobel Laureates are the reason why this event was so inspiring. Because in research that’s what keeps us working: Questions!

^[1] I am thankful to the Marie Sklodowska-Curie Fellowship (through the PODER network) for sponsoring my participation in the meeting.

^[2]Before McFadden’s lecture, there was a keynote address by Mario Draghi, president of the European Central Bank.

BGSE represented by “Just Peanuts” at Data Science Game finals in Paris

Class of 2017 Data Science graduates Roger Garriga, Javier Mas, Saurav Poudel, and Jonas Paul Westermann qualified for the final round of the Data Science Game in Paris this fall. Here is their account of the event.

Data Science Game is an annual competition organized by an association of volunteers from France. After competing in a tough online classificatory phase during the master we classified to the finals in Paris where we would be presented with a new problem to solve in a 2 days hackathon.

The hackathon was held in a palace property of Capgemini called Les Fontaines. It was an amazing building that made the experience even better.

The problem presented was to estimate the demand of 1.500 different products on 4 different countries using historic orders from 100.000 customers during the past 5 years by forecasting the three subsequent months. This was a well defined challenge that could be tackled with a large variety of solutions and for us specially the time constrain was one of the main challenges, since at the end we could be only 3 instead of 4.

We started by exploring the data and we realised that there were a lot of missing values due to a cross of databases done by the company who provided the data. So we spent some time by cleaning up the data and filling some of the missing values, to later on apply our models. After all the cleaning the key element to solve the challenge was later on to engineer good features that would represent well the data and then apply a simple model to predict the 3 months ahead.

The hackathon can be summed up in a day and a half coding, modeling and discussing without sleeping surrounded by 76 other participants from all across the world that were basically doing exactly the same, with short pauses to eat pizza, hamburgers and Indian food. So, a pretty good way to spend a weekend.

Why more educated individuals are not always healthier

Caleb Hia (Economics ’18) wrote the following article on health economics from his research for his undergraduate dissertation at the University of Edinburgh.

Caleb Hia ’18 wrote the following article on health economics from his research for his undergraduate dissertation at the University of Edinburgh.

From 2006 to 2007, almost half of the UK’s National Health Service’s (NHS) costs were attributed to behavioural risk factors: diet-related sickness, sedentary lifestyles, smoking, alcohol and obesity cost more than £15 billion (Scarborough et al., 2011). This mammoth sum, deemed an economic burden on public resources, attracted the government’s attention. In the recent Budget, the Chancellor introduced a tax on the sugar content of soft drinks from 2018 to tackle childhood obesity aimed at compelling individuals to consider external costs associated with its consumption which they do not bear such as the publicly-funded health costs of treating diet-related diseases. The effectiveness of this or any further government intervention in an attempt to correct this “externality” will influence the way the NHS allocates its limited resources in healthcare provision.

Beyond this political issue runs an underlying discussion of the social determinants of health which have long been studied (Wilkinson and Marmot, 2003; Adams et al., 2003). In particular, the effects of education on health has been of interest since the inception of Grossman’s (1972) health model. Grossman’s model suggests health can be maintained by health investments, depending on goods and activity consumption, which affect health although health depreciates as individuals age. As better health gives an individual more time to work and enjoy consumption, more educated individuals are expected to demand more health and invest more in their health. This implies more educated individuals are also more efficient health producers.

A possible causal link between education and health exists possibly because higher productivity from more education directly translates to a higher level of health production through allocative efficiency (Kenkel, 1991; Rosenzweig, 1995) and productive efficiency (Grossman, 1972). For example, low literacy is associated with a poor understanding of hospitals’ discharge instructions (Spandorfer et al., 1995) while higher educated individuals are more likely to follow medical treatments (Goldman and Smith, 2002). Relatedly, higher educated people spend more time on health-related activities because they are better at allocating inputs (Grossman, 1972). Additionally, higher educated individuals use their higher earnings to purchase healthier lifestyles (Glied and Lleras-Muney, 2003) which entail more expensive medical treatments, healthier food consumption and living in healthier areas.

I use a natural experiment in England, the increase in compulsory schooling laws from fifteen to sixteen years old following the Raising of School Leaving Age Order in 1972, and an instrumental variable (IV) regression model to examine the relationship between education and health in greater detail. My sample incorporates additional years of data from Health Survey England between 1991 and 1993 which were not analysed before. I measure various health-related measures and behaviours including Body Mass Index (BMI) which has not been considered before. I run Ordinary Least Squares (OLS) and two-stage least squares (2SLS) regressions in a sample containing all individuals and a discontinuity sample comprising individuals born only in January and February using February-born individuals as my instrument. I show education has no causal effect on various health-related measures and behaviours.

A possible explanation for this lies in time inconsistent preferences supported by behavioural economics. Quasi-hyperbolic discounting (Phelps and Pollak, 1968; Laibson, 1997) induces dynamically inconsistent preferences contrary to geometric discounting. The following payoff matrices models a hypothetical situation where an individual fails to quit smoking due to quasi-hyperbolic discounting:

Under geometric discounting where ∝ ≈ 1 and β ≈ 0.8,

he makes time consistent choices regardless of when benefits to those choices are delayed. Since he gets more utility from quitting in both periods, he quits immediately.

However, under Quasi-hyperbolic discounting where ∝ ≈ 1 and β ≈ 0.8,

he changes his choices based on his distance in the future. Unlike geometric discounting, he gets more utility from quitting only in future and not at present and hence do not quit.

The empirical evidence from Gruber and Köszegi’s (2001) addictive behaviour model which incorporates time-inconsistent preferences to the standard “rational addiction” model (Becker et al., 1994) suggests smokers exhibit forward-looking behaviour with time inconsistent preferences concerning smoking. Thus, individuals start smoking often as adolescents when they are most present biased (Hammond, 2005) and do not anticipate the difficulty of quitting.

Therefore, lifestyle habits may not be correlated with education. In the case of smoking, individuals who quit smoking successfully may have used commitment devices (Ashraf et al., 2006; Kaur et al., 2010; Beshears et al., 2011) like quitting with friends to constrain their own future choices by deciding ahead of time to make future deviations costly. Increasing the education budget may be a sound way to promote public health but understanding behaviours and exploring policies to incentivise individuals to adopt healthy habits may be more effective in the long-run.

Download the full paper:

The causal relationship between education and health-related measures and behaviours: Evidence from England

Advice for new master’s students from Marc de la Barrera ’17

Marc de la Barrera ’17 shares some advice from his recent experience as a student in the Barcelona GSE Economics Program.

At the welcome event for new students on September 26, alum Marc de la Barrera ’17 shared some advice from his recent experience as a student in the Barcelona GSE Economics Program.

alumni speech — Marc de la Barrera (Economics ’17, GPEFM)

Here is the text of his speech (see if you can spot all the references to a certain television series…)

Dear BGSE students, staff, professors and friends,

I am very happy to be here giving this speech, remembering myself just one year ago sitting in your place. By that time I was an engineer starting an Economics Master, both amused but nervous for digging in a new field. “You know nothing, Marc Barrera”, I keept saying to myself. One year later, at least I can say I know something.

In the Economics Master, I learnt to play with macroeconomic models, how to gather valuable information from data, and to understand how we take decisions. Also that asking the right question is almost as important as finding the answer. I remember me having troubles understanding the “risk free rate” concept. How is it possible that you get a return on your money for sure? Then someone told me that America always pays its debts. Well, they assume they do, I don’t know if now they are so confident with its new administration. Those in data science will learn that information is power, while these of you taking political economy classes will argue that power is power. For competition ones… well, competition is lack of power. And no matter which master you are enrolled in, you are going to meet, John Maynard Keynes, 1st Baron Keynes, Companion of the Order of the Bath, Fellow of the British Academy and father of modern economics.

I hope you are enjoying your time here, nice weather, meeting new people every day, no pressure… But summer will not last forever. Soon you will realize that winter is coming, and with them, exams. And remember that when exams come and problem sets appear, the lone student dies but the pack survives. Everyone has its studying style, but I deeply encourage you form teams and work altogether. You are here, hence you are all very intelligent, I have no doubt about that, but there is a problem… Your professors more. You will need to merge several minds to solve one problem. You have different backgrounds, someone will be very strong in formal math, others might excel at economic intuition, and others will know coding. These three aspects, and many others, are needed to succed all the masters at BGSE.

But it is not only what I learnt that made last year special, it was the experiences I lived and more importantly, the people I meet. I want to make use of this privilaged attention I have, to encourage BGSE to do more activities outside the academic environment, at the same time that I congratulate them for the ones they are currently performing. Butifarrada, football tournamen, sky trip, fideuà… Go to as many events as you can, if not all. Defying all economic laws, this events provide one thing that economists belief do not exist: “free lunch” (just ignore the tuition fees).

Then the people. You will get in touch with many people from many nationalities, such opportunity must be taken. But is not only the cultural exchange what matters. Feelings, frienship will arise. Some cuples will form with probability one. Networking to get opportunities, information or new jobs is fine, but spending time with people you like and appreciate, is better.

>And finally the faculty. Their level is extraordinary, make the most of them. Not only during the class, they are here to help and guide you. I might have abused of their kindness last year, but every professor and staff member I asked to see, whether for a technical doubt regarding the notes, to more fundamental and vital questions like “should I do a PhD”, received me and helped me as much as they could. Luckily you don’t have to send a raven, although we have more pidgeons here, an e-mail should work. The objective of the faculty is to make the most of you, so let them help.

Whether you stay in Bellaterra at UAB or in the Citadel Campus at UPF, it is time to go beyond the wall. After the master the research frontier will be near, and some of you, like me, will opt to go further, to the unexplored. Those who opt for a professional career, maybe we will make it to the World Economic Forum in Davos.

Congratulations for being admitted to your program. This year will be a great year: you will learn economics, meet people, and discover cultures. I hope that the first weeks have been pleasant, and get ready to work hard, because as bodybuilders say, “no pain, no gain”.

Original post and more from Marc de la Barrera on his personal blog. Connect with Marc on Twitter and LinkedIn

Videos from welcome events for the Class of 2018:

A Study on the Measurement of the Compensating Wage Differentials in European Countries

Yusuf Aguş (Economics student ’18) shares a summary of his bachelor’s thesis on the measurement of the compensating wage differentials in European countries.

According to the economic theory, the differences of working conditions are compensated by wage differentials at the equilibrium in a perfect competition setting. In other words, if a worker is working in a job with undesirable characteristics, he or she needs to have a higher wage than his or her counterparts.

Earlier studies failed to find significant results for the effect of most of the working conditions on wages, which could be possibly caused by several different biases, and focused on the effect of the risk of fatal or non-fatal accident. These biases can be summarized as the effect of unobserved characteristics, survey errors, heterogeneity of individual preferences on job characteristics and endogeneity of job riskiness. In this direction, the effect of risk perception on wages has been tried to be estimated by using 2010 and 2015 waves of the data set of European Working Conditions Survey (EWCS) which includes a wide set of data from 25 countries. As it is a very wide set of data, it allows us to control for a lot of heterogeneities across the individuals. A three-staged estimation strategy has been used in order to show the cross-national differences clearly. Firstly, the estimation is done for all the countries. Secondly, the models are estimated only for Turkey. In the third and the last stage the estimation is done separately for two different country groups, which are constructed according to their GDP levels. For the sake of simplicity, countries with higher GDPs are addressed as the developed countries and the rest as less developed countries. The lists of country groups can be found in the following table:

The estimation gave insignificant results for most of the cases. However, the most salient result has appeared in the estimation for the less developed countries. A negative and significant effect of risk perception on wages has been received for the group of less developed countries, which can be the sign of a segmented labor market across European countries in terms of compensation of working conditions.

For the case of Turkey, It can be observed that Turkish workers receive a positive wage premium for being informed about risk, but they do not think that their wage is compensated for risk. Pooled results are quite confusing as well. Risk perception did not bring a significant wage premium, but workers who think that their wages are compensated for risk have higher wages than their counterparts. According to these somewhat controversial results, we might say that European workers are not perceiving the risk correctly.

The full article can be read here.

Inflation Expectations and Forward Guidance

Economics master project by Marc de la Barrera, Juraj Falath, Dorian Henricot and Jean-Alexandre Vaglio (Class of 2017)

Editor’s note: This post is part of a series showcasing Barcelona GSE master projects by students in the Class of 2017. The project is a required component of every master program.

Authors:

Marc de la Barrera, Juraj Falath, Dorian Henricot and Jean-Alexandre Vaglio

Master’s Program:

Economics

Paper Abstract:

Our paper empirically investigates the impact of forward guidance announcements on inflation expectations in the Eurozone. The ECB first resorted to forward guidance on July 4, 2013 thereby expanding its array of unconventional policy instruments in the vicinity of the zero-lower bound. We use an ARCH model and identify forward guidance shocks as changes in the 2-year nominal ECB yield on specific announcement days to measure changes in daily inflation swaps of different maturities. In the process, we also separately identify the effect of quantitative easing and interest rate change announcement shocks. We find that forward guidance was successful in reviving inflation expectations in the medium to long term. Analyzing the transmission channels of forward guidance, we find evidence that both a reanchoring channel and a portfolio effect might have been at play.

chart — Sources: authors’ calculations and Thomson Reuters Datastream

Conclusions:

Forward guidance shocks have a strong impact on inflation expectations with a one point decrease in 2-year nominal ECB yields pushing inflation expectations 37bps upwards five years ahead with high significance. Normalizing, a negative shock of one standard deviation in ECB yields had a 11bps positive impact. In Campbell’s terminology (Campbell et al. (2012)), market participants’ interpretation was Odyssean. Thereby, we broadly match the results found by Hubert & Labondance (2016) for the Eurozone. Since the impact persists at all horizons, albeit with decreasing amplitude, we suggest that a reanchoring channel à la Andrade et al. (2015) explains the bulk of the transmission. ECB forward guidance announcements have thus been effective in reducing the growing gap between agents’ beliefs in future monetary policy and ECB’s targets. Our results are also consistent with a portfolio effect à la Hanson & Stein (2015). We also document that QE announcements were more effective in amplitude than forward guidance announcements, probably through a reduction in the term premium.

In contrast, studies run by Nakamura & Steinsson (2013) or Campbell et al. (2012) suggested a larger Delphic channel was at play in the US. More precisely however, they found that their results were lower than those predicted by a New Keynesian model with sticky prices. Thus, a natural extension of this paper would be to explore how our results would compare to the predictions of a New Keynesian model. Another approach would be to build a counter-factual for inflation expectations in the absence of forward guidance. In any case, given that the ECB implemented forward guidance at a time of heightened uncertainty and while long-term inflation expectations were dropping, there are reasons to believe it could have been more efficient in the Eurozone than in the US.

On the theoretical side, it is important to understand the transmission mechanisms of forward guidance within a structural model. This would allow to understand the potential gap to empirical outcomes. A number of authors have already striven to embed forward guidance within New Keynesian models and it is still an active area of research. The objective is then to derive an optimal policy function for further times of monetary policy management under the ZLB constraint.

To complete the policy recommendation, one needs to weigh out the benefits of forward guidance against its undesirable side-effects. Poloz (2014) suggested that successful forward guidance could results in increased future volatility when restoring conventional communication. Campbell et al. (2012) highlighted that central bank commitment could have a cost in terms of inflation or credibility. It would then be interesting to assess the negative externalities of forward guidance.

References:

Andrade, P., Breckenfelder, J., De Fiore, F., Karadi, P. & Tristani, O. (2015), ‘The ECB’s asset purchase programme: an early assessment’, ECB Working Paper (1956).

Campbell, J., Evans, C., Fisher, J. & Justiniano, A. (2012), ‘Macroeconomic Effects of Federal Reserve Forward Guidance’, Brookings Papers on Economic Activity 43(1), 1–80.

Hanson, S. & Stein, J. (2015), ‘Monetary policy and long-term real rates’, Journal of Financial Economics 115(3), 429–448.

Hubert, P. & Labondance, F. (2016), ‘The effect of ECB Forward Guidance on Policy Expectations’, Sciences Po publications (30).

Nakamura, E. & Steinsson, J. (2013), ‘High frequency identification of monetary non-neutrality: The information effect’, NBER Working Paper (w19260).

Poloz, S. (2014), ‘Integrating uncertainty and monetary policy-making: A practitioner’s perspective’, Bank of Canada Discussion Paper (2014-6).

International Asset Allocations and Capital Flows: The Benchmark Effect

By Tomas Williams (Economics ’12, GPEFM ’17), Assistant Professor of International Finance at George Washington University in Washington, DC.

Tomas Williams (Economics ’12, GPEFM ’17) is Assistant Professor of International Finance at George Washington University in Washington, DC. His paper, “International Asset Allocations and Capital Flows: The Benchmark Effect” (with Claudio Raddatz, Central Bank of Chile and Sergio Schmukler, World Bank Research Group) is forthcoming at the Journal of International Economics.

International Asset Allocations and Capital Flows: The Benchmark Effect

As financial intermediaries such as open-end funds with benchmark tracking grow in importance around the world, the issue of which countries belong to relevant international benchmark indexes (such as the MSCI Emerging Markets) has generated significant attention in the financial world (Financial Times, 2015). The reason is that the inclusion/exclusion of countries from widely followed benchmarks has implications for the allocation of capital across countries. As institutional investors become more passive, they follow benchmark indexes more closely. These benchmark indexes change over time, as index providers reclassify countries, implying that investment funds have to re-allocate their portfolio among the countries they target. The capital flows generated by these portfolio re-allocations are important since worldwide open-end funds that follow a few well-known stock and bond market indexes manage around 37 trillion U.S. dollars in assets (ICI, 2016). These changes in benchmark indexes can produce unexpected effects in international capital flows, linked to how financial markets work, not necessarily to economic fundamentals.

One clear example of these counterintuitive reallocations happened when MCSI announced in 2009 that it would upgrade Israel from emerging to developed market status, moving it from the MSCI Emerging Markets (EM) Index to the World Index. When the upgrade became effective in May 2010, Israel faced equity capital outflows of around 2 billion dollars despite its better status (Figure 1 below, click image to enlarge). The reason is that Israel became a smaller fish in a bigger pond. Israel’s weight in the MSCI EM Index decreased from 3.17 to 0, while it increased from 0 to 0.37 in the MSCI World Index. Israeli stocks in the MSCI index fell almost 4 percent in the week of the announcement and significantly underperformed the stocks not included in the index. The week prior to the effective date (when index funds rebalanced their portfolio) there was a 4.2 percent drop in the MSCI Israel Index, versus a 1.5 fall in the Israeli stocks outside the index.

Figure 1. Direct Benchmark Effect: Aggregate Flows — This figure shows aggregate data on flows in Israel around the time of large benchmark weight changes. Figure 1 shows data for portfolio equity liability flows and portfolio debt liability flows for Israel quarterly between 2007 and 2011. Figure 2 shows the cumulative flows from frontier markets passive funds around the upgrade of Qatar and United Arab Emirates to the MSCI Emerging Markets.

The effects of index reclassifications go beyond the countries and asset classes being specifically targeted. Spillovers could occur to other countries that share a certain benchmark with countries affected by reclassifications. A clear example of this is the upgrade in June 2013 of Qatar and United Arab Emirates (UAE) from the MSCI Frontier Markets (FM) Index to the MSCI EM Index. Together, these two countries were around 40 percent of the MSCI FM Index before the reclassification. When this reclassification took place, funds tracking closely the MSCI FM Index had to sell securities from these two countries and use the money to invest in the rest of the countries in the MSCI FM Index. This resulted in significant capital inflows and stock market price increases in countries such as Nigeria, Kuwait, and Pakistan (Figure 2, click image to enlarge).

Figure 2. Cumulative Flows from Frontier Passive Funds

These movements in financial markets have led to speculations and market movements related to potential new reclassifications. One recent and prominent example is that of China. For the past two years, MSCI delayed numerous times the introduction of China A-shares as a part of the MSCI Emerging Markets. Finally, in June 2017, they confirmed the inclusion of only a fraction of these stocks, creating capital inflows into the Chinese stock markets, and increases in stock prices (Financial Times, 2017). Chinese sovereign bonds may see similar capital inflows if J.P. Morgan, Citibank and Barclays decide to add China into their flagship bond indexes (CNBC, 2017).

In a recent study (Raddatz et al., 2017), we systematically document these benchmark effects, showing the various channels through which prominent international equity and bond market indexes affect asset allocations, capital flows, and asset prices across countries. Benchmarks have statistically and economically significant effects on the allocations and capital flows of mutual funds across countries. For example, a 1 percent increase in a country’s benchmark weight results on average in a 0.7 percent increase in the weight of that country for the typical mutual fund that follows that benchmark. These benchmark effects on the mutual fund portfolios are relevant even after controlling for time-varying industry allocations and country-specific or fundamental factors. Exogenous events that modify benchmark indexes affect benchmark weights. Furthermore, asset prices move both during the announcement and effective dates of the benchmark changes in response to the capital movements.

Academics, financial institutions, and policy makers have already started paying attention to the potential effects of benchmarks on capital flows and asset prices, as well as on herding, momentum, and risk taking (BIS, 2014; Arslanalp and Tsuda, 2015; IMF, 2015, Shek et al., 2015; Vayanos and Woolley, 2016). More work in this area would be welcomed as passive investing continues expanding.

References

Arslanalp, S., Tsuda, T., 2015. Emerging Market Portfolio Flows: The Role of Benchmark-Driven Investors. IMF Working Paper 15/263, December.

BIS, 2014. International Banking and Financial Market Developments. BIS Quarterly Review.

CNBC, 2017. Chinese Stocks got their Global Stamp of Approval, and now Bonds may be next.

Financial Times, 2015. Emerging Market Investors Dominated by Indices. August 4.

Financial Times, 2017. China Stocks Set for $500bn Inflows after MSCI Move. June 21.

ICI, 2016. Investment Company Institute: Annual Factbook.

IMF, 2015. Global Financial Stability Report.

MSCI, 2016. Potential Impact on the MSCI Indexes in the Event of the United Kingdom’s Exit from the European Union (“Brexit”). June.

Raddatz, C., Schmukler, S., Williams, T., 2017. International Asset Allocations and Capital Flows: The Benchmark Effect. Working Papers 2017-XX, The George Washington University, Institute for International Economic Policy.

Shek, J., Shim, I., Shin H.S., 2015. Investor Redemptions and Fund Manager Sales of Emerging Market Bonds: How Are They Related? BIS Working Paper 509.

Vayanos, D., Woolley, P., 2016. Curse of the Benchmarks. LSE Discussion Paper 747.

Wall Street Journal, 2014. Colombia Wins Investors’ Favor – And That’s the Problem. August 13.

About Tomas Williams

From his website:

I am an Assistant Professor of International Finance at George Washington University in Washington, D.C. My main fields of research are International Finance, Financial Economics and Empirical Banking. I have a special interest on financial intermediaries and how they affect international capital flows and economic activity. More specifically, I have been working on how the use of well-known benchmark indexes by financial intermediaries affects both financial markets and real economic activity.

More personally, I grew up in Buenos Aires, and studied economics at Universidad del CEMA. Afterwards, I moved to Barcelona and completed the Master’s Degree in Economics and Finance (Economics Program) at Barcelona GSE. Later on, I received my Ph.D. in Economics and Finance from Universitat Pompeu Fabra. I also spent one year as a visiting doctoral student in the Financial Markets Group (FMG) at the London School of Economics and Political Science.

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Can misguided monetary policy explain the European housing bubble?

Patrick Altmeyer (Finance student ’18) who has an interest in monetary policy, shares his work on whether misguided monetary policy can explain the European housing bubble.

Property prices surged throughout Europe in the early 2000s before collapsing during the crisis and causing tremendous welfare losses. This dissertation uses Structural Vector Autoregression (SVAR) to analyse the role of house prices within the monetary transmission mechanism in Europe over the past decades in order to understand whether lax interest rate policy had caused the bubble. Quarterly observations of inflation, output, consumption, real estate prices and mortgage variables for eight European countries were used. Sample periods vary by model specification but generally four decades.

Impulse response functions for the baseline SVAR suggest that real estate prices did indeed respond positively to dovish monetary policy and thereby amplified conventional effects on consumer spending. However, the interpretation of these preliminary results is complicated by explosive house price dynamics during the early 2000s. The linear vector autoregressions fail to fully capture these non-linear elements of the time series. A statistical test developed by Homm and Breitung (2012) is therefore used to identify bubble periods in the various countries analysed. Explosive house price dynamics are found in all countries but Germany as shown in Figure 1.

Figure 1: House price trends in European countries. Shaded areas indicate bubble periods.

Information about house price bubbles is subsequently used to augment the baseline SVAR in various ways. Consequently, the measured effect of a decrease in interest rates on house prices remains positive, but to a lesser extent. Overall, evidence found here suggests that interest rate policy alone was not responsible for the European housing bubble. Rather, it appears that the boom could be better explained by joint effects of loose monetary policy, financial liberalisation and associated mortgage market innovations. Note, for example, that total securitisation activity measured in terms of the number of euro-denominated asset-backed securities outstanding increased six fold from 2000 until the credit bubble burst in mid 2007. Unsurprisingly, many have drawn a connection between monetary policy and securitisation commonly arguing that the latter amplified the conventional credit effects of the former. Information about mortgage rates and lending activity is used as a proxy for mortgage securitisation and added to the SVAR in the final section of the empirical part. Indeed, these variables are found to have high explanatory power with respect to house price trends in most countries as evident in Figure 2, which plots forecast error variance decompositions for each country under the preferred model specification.

Figure 2: Forecast error variance decompositions.

The paper therefore concludes that stricter interest rates more closely aligned with policy rules could not have entirely avoided the bubbles, hence this approach is not recommended for the future. Putting more focus on asset price stability and thereby departing from the policy rate’s traditional role of smoothing consumption and consumer prices would be too complicated and is therefore not advisable, either. In light of the finding that financial innovations have greatly contributed to bubbles, policy makers should continue current efforts on imposing stricter regulation through macroprudential measures.

The full article can be read here.