Narrative Economics by Robert Shiller - Storytelling but no predictions

Economics is a social science. It tries to explain the behavior of consumers and producers, and the interaction of buyers and sellers. It is not physics. Expectations are constantly being made based on changing sets of data and the reaction of different groups. There is herd mentality. At the extreme, there is the madness of crowds. There is rationality in beliefs, but also individual and group biases. It is a complex web or network of action and reaction.

Quantitative models try to simplify the complex through testable hypotheses based on models assuming rationality, but behavior may not follow the simplifying assumptions often employed. Consequently, the history and structure of markets adds important color and explanation for behavior.

The new book, Narrative Economics by Robert Shiller, tries to put the social back into the science of economics through a discussion of how the mood, feelings, and meme of narratives rise and fall through time. Narratives have a contagious flow like a disease. Narratives may start out slow, then build and finally exhaust and fall. With narrative comes changes in sentiment and behavior.

I appreciate the importance of narrative and how it may create fads and fashion which influence demand, but the challenge of economics as a science is to generate testable hypotheses provide predictions. This is the core view of the Friedman positive economics, Chicago School, approach. By a standard of attempting to provide meaningful predictions of phenomena not yet observed, the book fails.

The ebb and flow of narrative is interesting, but Shiller does not provide any testable hypotheses on why these narrative themes will begin or end. He discusses the Kermack-McKendrick model of disease epidemics which is an interesting approach for thinking about narrative growth, but this does not help us with why some ideas stick. Shiller uses the frequency of words as measured by Google Ngrams to show the trends of narrative, but this is only descriptive not predictive. 

Narratives come and go, but this work provides no link between stories and market behavior that is measurable.  Why do some ideas move through markets and not others? His work does not account for the interesting advances in networking. It could be an issue of who starts the idea and the social network that exists with these idea generators. There are ways to advance this thinking, but not discussed in Shiller's book. 

Investors should be aware of narratives, themes, and sentiment, but awareness has to be at some point converted to something measurable. The use of word frequency has been applied to finance to measure the distribution of memes. When some narrative gets more frequency or play, there is a potential for increased demand. Still, the quality and importance of this link is mixed.  

Providing stories of narrative themes can tell us about time and place, but it tells us nothing on whether a given narrative will have an impact on prices or behavior. This could just be another way of adding history to economics. I can agree with the idea that more history and understanding of beliefs and sentiments should be explored, but there is a large chasm between descriptions and predictions.

Financial stress and Fed action - Is it needed?

First questions you ask for problem solving: "What is the problem to be solved?" Who has the problem?" 

So, what is the problem that is being solved with the massive purchase of Treasury bills by the Fed each month. The Fed works under a dual mandate of growth and stable inflation. In this capacity, they will serve as the lender of last resort in a crisis and provide for financial stability to help reach the dual mandate. Financial stability may be the third implicit mandate.

If there is financial stress, then the Fed may want to take action relieve the stress that may spill-over to the real economy. However, the measures of financial stress from the St Louis Fed and the Kansas City Fed are at or near all time lows. The Chicago Fed Financial Conditions Index is also near all-time lows and has been very stable. All are well below their constructed averages. 

Financing congestion and a spike in repo can have externalities on Treasury and short-term financing, but there has been now an extended repo program since September and a massive monthly buying program of $60 billion per month. This type of activity would suggest a massive financial stress problem.

This expected or perceived stress does not exist in the index numbers. It did not exist before September and does not exist today. There was a heightened level in December of 2018, but the lowering of rates seemed to have solved that problem. If the data indices are wrong or flawed, then they should be dropped. If they are useful, then policy-makers should at least take heed of their levels. 

Regulation and technical issues were the drivers of the repo and a solution of throwing money at the problem is not a fix. What kind of Fed guidance is given to the market when it is inconsistent their data? We have seen the result of Treasury bill buying, spiking of financial asset pricing. Once the buying subsides, we should know what will happen.  

Jim Simons' Guiding Principles for a Successful Organization

Jim Simons, the founder of Renaissance Technology, has a set of five guiding principles for a successful organization. While these principles are very general, it provides special insight into the mind of one of the great hedge fund firms. 

Doing the same things as other firms is never a good recipe for unique success; however, doing things different requires time and effort. A commitment to being different also requires a lot of smart people. You can’t be afraid to hire people smarter than you. Check your ego at the door because a special firm needs special people that have more talent than you. 

Success with building a business is about creating beauty in the sense of generating elegant simplicity, having a special core idea or process, and stripping away the excess to find the essence of what you are trying to create. You should describe your organization in one sentence. Forget the long mission statements and focus on a simple theme.

And, don't forget being lucky. Luck comes with hard work, but you need a boost at the right time. All these principles may sound cliche, but there is always truth in the simple cliches. The core issue is always learning to execute on these cliches. 

Expectations differ by generations - How do you deal with generational financial experiences?

• If you grew up during the Great Depression, you are always expecting the next Depression.
• If you were an investor during the Great Inflation, you will always be worried about the next great inflation.
• If you were a big investor during the Great Financial Crisis, you will also be worried about another crash.

I think you get the idea. There are generational events that will drive market expectations and behavior. The big events are usually negative because they are usually a surprise, occur over a short time, and cause significant anxiety. The big events have an imprint on our behavior through the availability heuristic. We then look for confirmation of events that these negative events will occur again. Some of these expectations will only change when that generation dies off, or in the case of investors and traders, no longer represent the majority of assets. 

Risk aversion is related to a number of investor specific characteristics: 

• Associated with lifetime experiences - Did you suffer or profit from a bad event (Financial Crisis, mortgages, tech bubble, LTCM, emerging market failures, etc...)?
• Associated with age - Did you investing life-cycle include these events? 
• Associated with past losses - Did you lose money in last crisis?

The challenge is learning to look beyond rare events, yet in reality, you cannot look beyond them because they have occurred. They are not possibilities but represent reality. Big tail events are a part of finance. We can minimize the look-back period to eliminate these outliers, but it does not change the fact that big negative tail events have occurred. 

The challenge for building any portfolio is identifying and overcoming potential biases based on these generational tail events that may not be as relevant in the current environment while at the same time accounting for reality. Some of the poorer performance of hedge funds may be associated with a decreased willingness to take risk given the tail events of the past. The closure of some funds may be associated with the manager's inability to reconcile his historical experience with the realities of today. The scars of the past bind the present.

Rare events should not be discounted to zero but have to be given their appropriate weight. Unfortunately, the weight for rare, but substantial events, is not a problem that is easy to solve. It is easy to say, give those tail events their due, but they may never occur again in an investor's lifetime. Yet, not accounting for them may generate financial ruin; the intersection between black swan theory and black swan failure.

Perhaps the greatest risk management issue is determining how to properly measure the uncertainty of low probability events of financial failure over a set horizon. What is the likelihood of a 50+% stock crash in the next three years? There are formulas to calculate likelihood given past crashes over some historical sample, but small changes in the problem set-up or looking at a shorter sample of history will give very different answers. More importantly, how should investors respond to different likelihoods of rare events? Should your behavior change if failure moves from .005% to .02%? 

A simple response is that it does not matter much because an investor should always protect against any event that has a minimum tail likelihood. Still, the response of what will be done with this information could be radically different based on who is receiving the information. 

Perhaps every firm should have a wide set of ages and experiences on their investment committee to offset or diversify generational event risk perceptions.   

Clayton Christensen - “The Innovator’s Dilemma” author died and we lose a great management thinker

What MBA or business analyst has not read, "The Innovator's Dilemma"? This is a tour de force in management thinking. It is a clear narrative that has driven firms around the globe to think differently about competition and change.  It does not matter that some business historians have quibbled with some of the details. It forced every reader to think about the implications of a steady-as-you-go status quo strategy. I have enjoyed all of his writing and it has stood the test of time.

Clayton Christensen died today, and we lost a great thinker, but more importantly, a great person who can be held up as living a full, thoughtful life focused on practical research and what can be done for others. His last book, 'The Prosperity Paradox" centered on how innovation could lift nations out of poverty. 

I think the WSJ obituary had the best review of the man,

When one of his children was accused of pushing another student at school, Dr. Christensen convened a family meeting. “The brand is that the Christensens are known for kindness,” he said.

He expected that brand to serve him well in the afterlife. “When I pass on and have my interview with God, he is not going to say, ‘Oh my gosh, Clay Christensen, you were a famous professor at H.B.S.,’” Dr. Christensen told the Journal in a 2016 interview. “He’s going to say…‘Can we just talk about the individual people you helped become better people?…Can we talk about what you did to help [your children] become wonderful people?’”

I drive past Innosight, the consulting business he started, on the way to Boston every day and I will remember what it means to be innovative and have a brand of kindness.

The good news - more money with stable velocity; the bad news - the plumbing

There is a lag between monetary policy shocks and the impact on the real economy. The lag is not precise, but it is good rule of thumb to use nine months as a base. This is not the same as a policy shock to financial assets which is immediate albeit persistent. The real effects through the lending channel is often slower moving. The cut in rates starting in the spring is why recession fears have abated.

It is relevant to look at money velocity because it provides the link with the nominal GDP. Now the equation, MV=PT is an identity, so we cannot draw definitive conclusions for the money GDP causality, but we can say that velocity has stabilized over the last few years. The Fed balance sheet has exploded over the last decade, but velocity has generally declined during the QE period. We can say that the power of a money shock on GDP has diminished. 

Even though the surge in money took place before the repo crisis, the Fed has had to pour billions into short-term financing through repo auctions and the purchase of Treasury bills. The semantics of whether this is QE does not matter. Liquidity has been added to the system to solve a plumbing problem. The plumbing problem diminishes the ability of current excess reserves to support a level of GDP and lending.

The focus of investors should not be on the amount of liquidity, but whether the plumbing is under stress and what is the threat of contagion from this stress. Since repo is critical not for real lending activities, but for financing of speculative positioning and inventory management of dealer, the current stress is the disconnect between the supply of debt and the demand for what is being issued. Still, a problem with funding the flow of debt will create uncertainty and volatility which will spill-over to lending for the real economy.

Financial Stability Board Report - The shadows of banking are big

The Financial Stability Board (FSB) just released their new study Global Monitoring Report on Non-Bank Financial Intermediation 2019 which includes data through 2018. It provides a comprehensive analysis of the "shadow banking" system now referred to as "non-bank financial intermediation" (NBFI) with a lot of details on the credit system outside banks. 

• Shadow banking continues to grow and is critical part of global financial intermediation, albeit market share for non-bank financial intermediation has declined slightly.
• The relevance of shadow banking differences greatly by country and region which makes monitoring or drawing inferences on global stability difficult.
• Complexity within the credit markets is high and it is not easy to see where there are current critical risks. There are a number of credit function categories and maps for describing financial intermediation.
• The FSB divides NBFI into five economic function categories: collective investment vehicles, lending entities based on short-term funding, financial intermediaries, credit creation facilities, and securitization.

Banking is still important but the financial intermediation in other forms and from other sources means that central banks have a hard time controlling rates, risks, and lending. The data shows that repo markets have increased in importance. The Fed may have thought there were enough excess reserves as of September, but in reality, the financial system is also driven by the flow of funds outside the banking system.

This stability report reinforces our ongoing theme that it is not the liquidity but the plumbing that is the potential problem area for finance. Bank capital has grown, and leverage has been controlled since the Financial Crisis, but shadow banking has continued. The regulatory rules of the game have also changed which impacts credit flows. 

Of course, this report tells us that the shadows are not as dark as before, but there are still a host of issues on the quality of lending, terms, and defaults that still are opaque. 

The next crisis issue will be similar to those in the past, a shortage of liquidity, the inability of borrowers and lenders to access cash in the margin, and contagion from not knowing whether capital will be repaid or who is creditworthy. It is not clear that buying Treasuries or just lowering rates will be able to solve these problems.    

Ensemble modeling - A solution to ambiguity and improved forecasting

Models will fail. Models will miss variables. Models will change with the sample side used. We are imperfect modelers. It is hard to find the right models because it is hard to differentiate between theories. Data relationships change so the importance of some variables change through time. Market see structural changes and regime changes. Hence, a good model today may not work tomorrow. These fundamental issues are not new and discussed even in introductory econometric classes, yet the real world of market forecasting has to find meaningful solutions.

Of course, the need for good theory is paramount to drive an econometric model, but the advancements in machine learning deemphasize the formation of modeling and allow data to speak for itself. An atheoretical approach to modeling will focus on measured success without theory. This is problematic for theorists, but ultimately there needs to be a focus on success. A number of ensemble modeling approaches have been developed to help with prediction. Two major schools can be applied to forecasting problems. The first is the simple forecasting approach of model averaging which was introduced by Clive Granger fifty years ago as a simple combination of forecasts. The second set is more formal process for developing alternative models used in machine learning.

This first ensemble approach:

Average or weighting models - 

• Take a set of model forecasts and average or form some type of weighting scheme for multiple models. This has been shown to be successful especially when the model used are uncorrelated. However, this approach has not been formalized into a process of finding alternative models. 
• The average can be done through some weighting or voting scheme. A better model is given greater weight. This combination of forecasts has developed a large literature on ways to reduce the estimation error of forecasts. For example, a simple application to trend-following would be to use the signal from different trend lengths.

The second set of ensemble approaches which have come out of statistics and computer science include: 

Bagging models - 

• Look for simple variations on the same theme of model averaging. Bagging stands for bootstrap aggregating. A very high-level view of bagging is using a single dataset tested over different subsamples of data sets or bags of data to generate different model choices. The bags are created with resampling to test different models. The modeler who applies the same basic approach across different markets can be thought of as using a bagging method. The forecast results are bundled or averaged to create the bagged model results. 

Boosting models - 

• A boosting approach uses past predictions to help with new models. The idea is to find what has not worked with one model and build a new model that will reduce or offset the failure of the prior model. It can be described as using a modeling method to make a set of weak learners into a stronger learner. A model can be developed, but it will have some errors. A new model can be formed that focuses on the errors of the prior model. A model that is adjusted to account for errors could be thought of as being boosted. Using the gradient boosting method, a model is formed which will have residuals. A new model will be formed to explain the residuals (if they are not random) which is then added to the original model.  

These ensemble approaches are important methods to increase prediction as opposed to drawing some conclusion of model failure. While we have just provided a very simple overview, ensemble techniques have been used for decades to improve quant results. 

Adding uncorrelated models to get better forecasts, training or testing on different data sets to find common parameters or different models, building or adding models that address failure and reduce estimation errors have all been used to help forecasts; however, there are now more structured approaches to generate more efficient ensembles. 

Principal Component Analysis of alternative risk premia shows unique groupings

Principal component analysis (PCA) is a useful tool for providing return groupings of different alternative risk premia strategies. PCA is a simple form of dimensionality reduction that is useful for factor extraction and data transformation. It can help further understand the differences in alternative risk premia relative to traditional equity and bond benchmarks beyond correlation or beta  measures. 

The following analysis is available in a thorough research paper, "A Framework for Risk Premia Investing: Anywhere to Hide" by Kari Vatanen and Antti Suhonen. We have looked at their work in previous posts on beta stability, "ARP strategies and market beta - Check the stability when constructing portfolios" and with cluster analysis in "Alternative risk premia and the advantage of cluster analysis".

They show earlier in their paper the differences in equity and bond betas across return quantiles for equity and bond benchmarks. In another section, they run principal components on the Sharpe ratios for each return quantile for equities and bonds. ARP strategies seem to be concave with respect to equity returns and convex with respect to bond returns. A little deeper analysis suggest that with respect to equity returns there is a pattern for PC2 that is like a long call option ratio spread.  The PC2 for bonds looks like a strangle.  

ARP strategies do well in the mid-return range quantiles for equities but will tail-off for high and low returns. Of course, this is in the context that equity betas are still low across all quantiles. For the bond benchmark, there is also lower PC1 and PC2 at the lower quantile. While this information is interesting, the value is limited since this quantile research is looking at all strategies.  

There is more information when you look at PC1 for both equity and bond benchmarks on a 2-dimensional plot for the bond and equity PC1. In this format, we can see there are clear "offensive"  and "defensive"  ARP strategies where an investor will be more positively (negatively) influenced by equity (bond) returns. This can be a helpful breakdown for ARP portfolio construction.  Carry will get you more offense and trend will get you more defense.

Additional information can be gained by looking at PC1 and PC2 for equity and bond benchmarks. In the case of the equity benchmark, an investor can think about high PC1 and limited PC2 which is carry or low PC1 and mixed PC2 which include fixed income carry and value. There is a third group, which has high PC2 and mixed PC1, and could be considered equity neutral strategies. This category seems to be dominated by trend and momentum.

The first and second PCs using the bond benchmark seems to breakdown the ARP world into positive and negative PC1. These clusters seem to more well-defined than what is seen with using the equity benchmark.  

The principal component analysis does a nice job of providing data transformations that can provide insight on how to group different factor strategies. More specific analysis is required but PCA provide a base framework. 

Centuries of interest rate data - Proves challenging for investors today

There are many explanations for the low interest rates we currently see around the globe. For markets focused analysts, it is the decline in bond market risk premia. For the macroeconomist, it is "secular stagnation", or some narrative around excess demand and supply. The general view is that low interest rates is a problem that has to be solved and it is something that is abnormal, and rates will eventually move back to normal with normal being higher. 

The research by Paul Schmelzing of the Bank of England in Staff Working Paper #845, "Eight centuries of global real interest rates, R-G and "suprasecular" decline, 1311-2018", provides an alternative narrative on levels of interest rates that is challenging for all economists and analysts. A study of real interest rates over centuries of data suggests that rates have been declining for hundreds of years. Current rates may not be abnormal.

• The global low interest rates of today are normal. 
• The trend in real interest rates has been going down for centuries. 
• Negative real interests are not abnormal. 
• Extrapolation for what rates may do using the last few decades is perilous and not informative.
• The spike and decline of rates over the last 100 years is unusual.
• The volatility of inflation and real rates has been abnormal.

There is power in economic history and looking at very long-run data. The author of this monumental work does not oversell his results although he makes some interesting references to the r - g Piketty debate on inequality by observing that the facts of high "r" relative to "g" are not on his side. 

What centuries of data tell us is that any extrapolative forecasting of what is long-term normal or what to expect with interest rates is fraught with peril. You can make your forecasts on what long-rates should do, but tell us why this time is different. 

ARP strategies and market beta - Check the stability when constructing portfolios

Alternative risk premia or cross-sectional factor strategies generally have low correlations with equity and bond market betas, but that it not the same thing as no market beta exposure. Market betas will differ by strategy and will also change with the market environment. A portfolio of ARP strategies will have implicit beta exposure and investors need to account for these risks when constructing a portfolio.

Using tables and figures from the paper "A Framework for Risk Premia Investing: Anywhere to Hide" by Kari Vatanen and Antti Suhonen, we can see that there are ARP strategies that are equity beta sensitive and bond sensitive. The simplest breakdown is that carry strategies have, on average, more equity beta exposure while momentum, trend, and value are more bond sensitive. If you are looking for defensive strategies focus on momentum and trend. Carry may have low equity beta but will still be more sensitive to a risky asset move. This is one of the clear reasons why carry and trend provide good diversification benefit when combined in a portfolio.

These alternative risk premia strategies will also be affected differently by "good" and "bad" times. The beta in up markets may not be the same as the beta in down markets as seen in the table below which compares betas in different quintiles.

The differences in betas can be visually displayed in a graph between betas for the lowest quintiles versus all other quintiles. A difference away from the straight line tells us the amount of beta variation risk. This dispersion is what will surprise investors who expect protection from low beta and don't get it when needed for some strategies or get more diversification than expected with other strategies. Data below the line states that estimated beta in the lowest quantile is lower than the other quantiles. Those above the line suggest greater beta risk.

For a selected number of ARP strategies versus bond betas, the researchers found that for low bond return periods, the bond beta exposure increases. This is not for all of the ARP strategies. The graph displays those that show the biggest differences between quantiles. It is notable that there is less dispersion across equity betas than the sensitive bond beta strategies. The risk with carry is not that there will be a large deviation in equity beta during the worst equity quantile but that carry will become more "bad bond" sensitive in the worst bond quantiles. 

One of the core reasons for holding a portfolio of alternative risk premia is to provide diversification for core equity and bond holdings. All ARPs are not alike with respect to their equity and bond betas, and their stability changes with the market environment. Checking their betas and stress for different market environment is critical for understanding risk exposures. 

The "Golden Age" of bond maturity premium unlikely to continue

Can an investor make money in the current bond market? Yes, but don't expect continuation of the "Golden Age" of bond investing of buying long duration versus short-duration bonds and holding as a long-term trade. 

• Inflation is lower with lower expectations.
• Credit risk even for sovereign bonds has declined and is perhaps underpriced.
• The curve albeit upward sloping again is still relatively flat.
• Term premia are close to zero. 
• Perhaps most importantly, history is not on the side of bond investors.

This does not mean that returns cannot be made in the short-run or that bonds are not a good diversifier with respect to equities. It does mean that the past strong performance is unlikely to continue. A normal world shows less maturity premium. A look at the priors before making a judgement says that investors should show care with holding bonds. The storing for increasing bond exposures should be weighed carefully against base conditions.

Smart does not mean rational - Need to think beyond SAT

Dysrationalia - the mismatch between intelligence and rationality or the inability to think rationally in spite of being classified as intelligence, a term coined by Keith Stanovich.

We all recall the comical absent-minded professor who is smart but cannot navigate the real world; however, dysrationalia is a more focused problem where measured intelligence does not match skill at using logic, probability, and the scientific method to make effective decisions. Call it a mindware gap.

Smart people can make biased intuitive judgments and then use their intelligence to dismiss contradictory evidence. They hold beliefs that are not based on logic and defend them with seeing any problem. Sir Arthur Conan Doyle, the creator of Sherlock Holmes, was a strong believer in fairies. He was deeply at odds on this topic with of all people Harry Houdini. Their argument destroyed a close friendship.   

So, does it matter whether you pick a smart manager? Of course, it does. However, the real question is whether beyond a minimum standard is it important to pick the smartest manager. Are there other measures of decision-making skill that will better determine the success of investment managers? Think of the mind game questions used by some tech firms to test the intuition of candidates like, "How many cigar smokers are there in Denmark?" 

There have been some studies on manager characteristics like, "Are Some Mutual Fund Managers Better than Others? Cross-Sectional Patterns in Behavior and Performance" by Judith Chevalier and Glenn Ellison in the Journal of Finance which concluded that excess return performance for mutual fund managers is related to higher-SAT related undergraduate institutions. Another similar paper, shows a similar result for hedge funds, Investing in Talents: Manager Characteristics and Hedge Fund Performances Haitao Li, Xiaoyan Zhang, and Rui Zhao which also shows that SAT scores are correlated with performance. We have not found any studies that focused on the decision-making skills or positive rationality of portfolio managers beyond measuring behavioral biases.

We are not suggesting discarding intelligence tests for job candidates although their usefulness may be marginal. It seems as though the signaling effect of intelligence is through which college attended as opposed to the crass question of score on a standardized test.

Most recruiting experts suggest testing of the specific skills necessary for the job is more critical. Rather, we are arguing that activities like portfolio management may require skills that are not directly measured by academic intelligence tests. I may not want the smartest person in the room deciding, but the person who can make a good decision with limited information and a short time constraint.  

The mindware or characteristics for effective decisions in an uncertain environment require specialized skills. If these skills cannot be found, model rules should be written to focus on the decisions that have to be made.   

See You don't need intelligence; you need "mindware" to be successful

Fundamental versus technical risk premia - Are there distinctions?

Risk premia can be classified through two types, fundamental and technical. Fundamental risk premia are associated with scaling on some fundamental factor like value or quality (price/book or earnings per share for value, or debt to equity return on equity for quality). Any risk premia that sorts on non-price factors is fundamentally-based. Technical-based premia are based on solely on price information like momentum, trend, implied versus actual volatility, or carry. This difference may not mean much to most investors, but it will have an impact on the variation or noise and adjustment of the risk premia. 

Technical or price-based risk premia based on the ranking or dispersion for some price criteria are easy to implement since there is only one key input employed and that information can be obtained daily. 

Fundamental risk premia will use information beyond and therefore will be periodic and subject to measure adjustment and lags. Hence, they will be subject to definitional differences. Price to book, price to earnings, debt to equity, and return on equity rankings will be based on selection criteria and accounting measurement. Two modelers can across on the factor yet obtain different portfolios based on accounting assumptions. For the same set of risk premium assumption, a price-based system will give the same answers.

The return dispersion in a price-based risk premia style category will be tighter than the dispersion for a fundamentally-based style risk factor. Consequently, there will be more portfolio return dispersion for fundamentally-based alternative risk premia. More care is necessary for making any portfolio choice. 

Fed policy "review" - What has not been openly discussed?

The Fed is undergoing an internal policy review that should be released sometime in mid-year, but there has been a number of editorials and discussions in newspapers, and Fed speeches on what should be adjusted or changed with policy. 

Clearly, there has been talk about adjusting the 2% inflation target. Those policy changes have ranged from widening the goal, overshooting the inflation target, nominal GDP targeting or dropping the target all together. There has been talk about improving forward guidance and increasing the tools available for monetary policy. The tools have ranged from the near-term adjustments to the repo market to alternative forms of quantitative easing. There has been talk about improved systemic risk management as well as further regulation of banks, yet one area that has not been discussed is the international role of the Fed.

The elephant in the room is that the world is still dominated by the dollar, so any changes in the Fed's balance sheet and interest rates do not just impact the US but have a deep and meaningful impact all over the world. With an increasing amount of global debt often financed in dollars, US interest rates are all the more important to global borrowers. This Fed importance to international growth and capital markets has not appreciable diminished this century.

Look at the last financial crisis and one will see that the swap lines provided by the Fed to the rest of the world were meaningful. Look at repo financing by foreign central banks or foreign bank Us operations, and we will see a large global impact. In spite of the increased size of emerging market local financing, the dollar flows still matter for EM funding and hedging. Simply put, the Fed is the central bank for the world and actions taken to raise or lower rates, to provide forward guidance, to overshoot inflation targets, or change their balance sheet will have ramifications across the globe. 

A policy review that does not address how the Fed works with the rest of the world misses a critical issue for policy review. However, this is the topic that will not be openly discussed because it will cause Congress and the President to interject their views with this serious discussion of what should be the international role of the Fed versus its domestic policy goal obligations. There is nothing wrong with this involvement except that is not clear where the Congress and the President stand on this issue. Monetary policy statecraft is an arcane and complex topic that does not focus directly on the citizen constituents of the Fed. For global investors, there is no topic that could be more important, yet it is not on the forefront of any discussion.