Economics Gone Astray

The following sections are included:

• Dedication
• Acknowledgements
• Praise for Economics Gone Astray
• Disclaimer
• Foreword
• Introduction
• Contents

US inflation has been subdued for well over two decades, as in all the major, mature industrial economies. This is not a recent phenomenon, and is not due to the lagged impact of the 2008 financial panic. Indeed, whether measured by the consumer price index (CPI) or the Federal Reserve’s (Fed’s) favorite personal consumption price deflator (PCE), core inflation, which excludes the more volatile food and energy categories, has been stuck in a 1% to 3% range in the US since 1994 and into 2018…

Debt is among the most fundamental and least understood aspects of the macro-economy. The dominant schools of macroeconomic thought — classical, Keynesian and monetarist — pay it inadequate attention. From the perspective of these schools of thought, economies with low levels of debt operate in exactly the same fashion as those with extremely high levels of debt. From their point of view, debt ratios essentially do not matter. The problem with that is in real life, debt levels not only matter, debt burdens influence critical facets of the economy, from the pace of growth to the level of interest rates…

Tax cuts are typically politically popular. And, they are often justified by their potential to stimulate economic activity. The concept is that lower tax rates lead to higher real GDP growth, and faster GDP growth leads to higher tax revenues down the road. The link between lower tax rates and future economic growth is, however, exceedingly tenuous. The statistical evidence for tax cuts leading to higher economic growth is mixed and not very convincing. A number of the critical assumptions in the economic theory of tax cuts are often ignored. When one replaces these heroic assumptions with a more realistic view of the world, it goes a very long way in helping to explain why tax cuts do not seem to contribute to economic growth, when the intuition is otherwise. Even so and despite the lack of impact on economic growth, tax cuts unambiguously seem to help equity markets and raise share prices…

One of the rarely stated but nevertheless implied tenants of most schools of economic thought, including monetarism and Keynesianism, is that economic downturns are unpredictable and arrive randomly. In this chapter, we present evidence that this is not the case. Rather, the economy moves in cycles that are governed by a feedback loop in which monetary policy both drives changes in unemployment, credit spreads and equity index volatility and, in turn, reacts to those same three variables…

Demographic patterns are one of the more important drivers of economic growth potential, ranking right up there with the type of economic system, the degree of political stability, property rights and viability of the rule of law. Economists in the 1800s paid plenty of attention to demographics. Witness the debates ignited by the economist and cleric, Reverend Thomas Robert Malthus, around whether population growth might outstrip the ability of the planet to feed all the people. In the post-war era of modern economics, however, demographic patterns have often taken a back seat, at least until recently. Basic economics textbooks tend to downplay demographic patterns and macro-economic growth models may simply assume a constant age distribution of the population and steady growth of the labor force. These assumptions make for truly bad economics and very poor long-term macro-economic forecasting…

Despite a tight labor market, wage and productivity growth remained low in the US in the post-Great Recession decade. Nominal hourly average wages rose by 2.6% in the US between 2016 and 2017, on par with post-Recession performance. Labor productivity growth averaged only 1.1% between 2007 and 2016. Between 1995 and 2007, by contrast, the average growth rate was 2.5%. Although cyclical factors may be partially to blame, there are several key structural features of the US economy contributing to diminished growth. Wage and productivity growth have been held back by persistently low inflation expectations, workforce demographics, and the shift in the payrolls distribution in the economy in favor of lower-wage sectors…

What is most striking about the economics of bitcoin is the juxtaposition of the certainty of supply and the uncertainty of demand. The rate at which bitcoin is mined has been highly predictable and, unlike almost any other asset, currency or commodity, its ultimate supply is a known quantity, fixed in advance. There will never be more than 21 million coins. This feature makes bitcoin supply almost perfectly inelastic. No matter how high the price rises, miners will not ultimately produce any more than the prescribed amount. Moreover, price rises will not even necessarily incentivize a more rapid mining of bitcoin. Even if they did, it would mean miners create more bitcoin today at the expense of creating less of it in the future since the total supply will reach a hard, asymptotic limit of 21 million coins, expected to be reached by 2040 or so, based on the mining algorithms…

The regulation of derivative markets in the US focuses on protecting individual investors from fraud and criminal activity, assuring the integrity of markets and safeguarding the economy against systematic risk emanating from the financial sector. These three critical objectives are not embedded in one regulatory authority, but are instead distributed across several institutions with very different origins and priorities, based in no small way on the historical context that led to their creation. That is, to appreciate the sources of different regulatory philosophies among the major regulators of derivative markets in the US, one has to examine why each institution was brought into existence and how that shaped its specific regulatory style and priorities…

Volatility in equities, bonds, and other asset classes remained at very low levels compared to historical norms in 2017, as measured by the standard deviation of daily market price movements. Yet, at the same time, uncertainty was exceptionally high concerning a wide-ranging array of potentially market-moving events which were often in the daily news and getting considerable attention. The co-existence during 2017 of relatively low volatility and high uncertainty presented an interesting conundrum…

In the age of Hollywood’s Moneyball, big data, machine learning and artificial intelligence (AI), it seems only natural to apply our new and evolving statistical tools to predicting outcomes in financial markets. Given the difficulties in predicting market behavior, the bar seems low, and open to dreams of making significant improvements in predictive analytics as applied to financial markets…

Challenges to optimization abound in the world of portfolio construction and financial risk assessment. While financial optimization theory is highly sophisticated, with detailed attention paid to model construction and critical assumptions, the current state of practice leaves much to be desired, and may best be described as a patchwork quilt held together by band aids or the ubiquitous duct tape. On the horizon, however, are some potential improvements in the analytical techniques underpinning how optimization methods are used in both portfolio construction and financial risk management. From the promise of exhaustive searches using quantum computers to the advances in pattern recognition available through structured machine learning, financial optimization methods are about to get a major makeover. Change may be coming, and it’s about time!…

We have observed in studying financial markets that 100-year floods occur quite often, maybe several every decade, so we know simple risk models can be inadequate and misleading. Many financial risk models start with a risk reading taken from the options markets — implied volatility. Implied volatility is a standard deviation based metric and, while not required, users typically embed the presumption of a bell-shaped curve. Starting with implied volatility, the risk manager or financial analyst then must work to augment the tails of the probability distribution to increase the odds of extreme events actually happening to align more closely with historical experience. After all, it is the extreme events that can do the most financial damage, so it is critical that the expected probability distribution be augmented beyond a simple standard deviation analysis to properly account for the possibilities…

Many fund managers offer investment programs or portfolio management services based on quantitative models. Model-driven investment strategies offer the promise of a disciplined approach that may enhance returns with only a minimal increase in risk, if any. Quantitative investment strategies typically are supported by an impressive amount of background research that can give the investor added confidence. But the reliable evidence of actual track records to evaluate these quantitative strategies is not always available, especially due to the severe survivorship bias present in investment performance databases. Consequently, simulations of past performance take on special significance…

After the 2008 financial panic, central banks in the US, UK, Europe and Japan experimented with the aggressive use of their balance sheets to stabilize their financial markets and encourage a return to higher rates of economic activity. These activities have become known as quantitative easing or QE. This research focuses mostly on balance sheet activities employed by the US Federal Reserve (Fed), and distinguishes between the initial round of quantitative easing (QE1) in late 2008 with later rounds of balance sheet activity to purchase more US Treasury securities or mortgage-backed securities (QE2 & QE3) and to adopt the maturity extension program (i.e., Operation Twist). With respect to certain ideas presented here, in a few cases we also consider European Central Bank (ECB) activities that were relevant to the discussion. [See Chapter 15 for an in-depth comparison of the effectiveness of the Fed’s QE programs versus the approach utilized by the ECB.]…

After the financial panic of September 2008 and the recession that followed, several of the key central banks of the mature, industrial world expanded their balance sheets aggressively, in what has become known as the era of quantitative easing (QE). Yet, there were major differences in the motivations and objectives of central banks for their QE programs, which led to very different approaches to their asset expansion practices. In turn, the different implementation methods resulted in very different outcomes, posing strikingly different challenges for their respective economies, with critical implications for future central bank policies. Our research compares and contrasts the activities of the Federal Reserve (Fed) and the European Central Bank (ECB). We come to relatively obvious as well as some potentially controversial conclusions, including the following:

• Quick and aggressive Fed and ECB actions after the bankruptcy of Lehman Brothers and badly managed bailout of AIG more than likely prevented another Great Depression.
• Later Fed QE programs adopted from 2011, even as the US economy was already recovering, may not have helped job creation at all.
• The ECB’s focus on liquidity loans calmed financial markets, but did not assist banks in shedding distressed assets and may have hindered economic growth compared to the Fed’s approach to purchase assets, reducing bank balance and capital requirements and leading to a faster economic recovery.
• The Fed’s exit from QE is likely to be highly complex, involving delays in returning to a more traditional short-term interest rate policy, diminished contributions to the US Treasury from central bank net earnings, and the potential for loss of some of the Fed’s independence over time as the US Congress increases its oversight concerning the size of the Fed’s balance sheet and potentially large unrealized portfolio losses.
• The ECB’s initial approach to QE, mostly through loans to the banking system, allowed for the easiest and most natural exit path among the major central banks – just let the loans mature.

After the Panic of 1907, the US Congress moved to establish a central bank with the powers to serve as a lender of last resort and prevent runs on banks turning into full-fledged financial crises. The Federal Reserve came into being in 1913, and its charter mandated that the new central bank “promote an elastic currency.” The term “elastic” in the opening words of the charter was intended to underscore the need for a robust banking system that could withstand shocks and not collapse upon itself. There was no mention whatsoever of a dual mandate of promoting price stability and encouraging full employment. The dual mandate concept emerged after World War II, as the US Congress reflected on the Federal Reserve’s near total abrogation of its assigned duties in the 1930s with its failure to serve as a lender of last resort as had been intended…

The following sections are included:

• About the Authors
• List of Figures
• Index