Category Archives: Quantitative Easing (QE)

global business forecasts, impact of QE, Quantitative Easing (QE)

The End of Quantitative Easing, the Expansion of QE

The US Federal Reserve Bank declared an end to its quantitative easing (QE) program at the end of October.

QE involves direct Fed intervention into buying longer term bonds with an eye to exercising leverage on long term interest rates and, thus, encouraging investment. Readers wanting more detail on how QE is implemented – check Ed Dolan’s slide show Quantitative Easing and the Fed 2008-2014: A Tutorial

The New York Times article on the Fed actions – Quantitative Easing Is Ending. Here’s What It Did, in Charts – had at least two charts that are must-see’s.

First, the ballooning of the Federal Reserve Balance sheet from less than $1 trillion to $4.5 trillion today –

FedQEassets

Secondly, according to Times estimates, about 40 percent of Fed assets are comprised of mortgage-backed securities now – making the Fed a potential major player in the US housing markets.

MBS

Several recent articles offer interpretation – what does the end of this five-year long program mean for the US economy and for investors. What were the impacts of QE?

I thought Jeff Miller’s “Old Prof” compendium was especially good – Weighing the Week Ahead: What the End of QE Means for the Individual Investor. If you click this link and find a post more recent than November 1, scroll down for the QE discussion. Basically, Miller thinks the impact on investors will be minimal.

This is also true in the Business Week article The Hawaiian Tropic Effect: Why the Fed’s Quantitative Easing Isn’t Over

But quantitative easing is the gift that keeps on giving. Even after the purchases end, its effects will persist. How could that be? The Fed will still own all those bonds it bought, and according to the agency itself, it’s the level of its holdings that affects the bond market, not the rate of addition to those holdings. Having reduced the supply of bonds available on the market, the Fed has raised their price. Yields (i.e. market interest rates) go down when prices go up. So the effect of quantitative easing is to lower interest rates for things Americans actually care about, such as 30-year fixed-rate mortgages.

Some other articles which attempt to tease out exactly what impacts QE did have on the economy –

Evaluation of quantitative easing QE had “some effects” but it’s one of several influences on the bond market and long term interest rates.

Quantitative easing: giving cash to the public would have been more effective

QE has also had unforeseen side-effects. The policy involved allowing banks and other financial institutions to exchange bonds for cash, and the hope was that this would lead to improved flows of credit to firms looking to expand. In reality, it encouraged financial speculation in property, shares and commodities. The bankers and the hedge fund owners did well out of QE, but the side-effect of footloose money searching the globe for high yields was higher food and fuel prices. High inflation and minimal wage growth led to falling real incomes and a slower recovery.

What Quantitative Easing Did Not Do: Three Revealing Charts – good discussion organized around the following three points –

  1. QE did not work according to the textbook model
  2. QE did not cause inflation
  3. QE was not powerful enough to overcome fiscal restraint

Expansion of QE

But quantitative easing as a central bank policy is by no means a dead letter.

In fact, at the very moment the US Federal Reserve announced the end of its five-year long program of bond-buying, the Bank of Japan (BOPJ) announced a significant expansion of its QE, as noted in this article from Forbes.

Last week, as the Federal Reserve officially announced the end of its long-term asset purchase program (commonly known as QE3), the Bank of Japan significantly ratcheted up its own quantitative easing program, in a surprising 5-4 split decision. Starting next year, the Bank of Japan will increase its balance sheet by 15 percent of GDP per annum and will extend the average duration of its bond purchases from 7 years to 10 years. The big move by Japan’s central bank comes amid the country’s GDP declining by 7.1% in the second quarter of 2014 (on an annualized basis) from the previous quarter following the increase of the VAT sales tax from 5% to 8% in Japan earlier this year and worries that Japan could fall into another deflationary spiral..

The scale of the Japanese effort is truly staggering, as this chart from the Forbes article illustrates.

 CentralBankAssets

The Economist article on this development Every man for himself tries to work out the implications of the Japanese action on the value of the yen, Japanese inflation/deflation, the Japanese international trade position, impact on competitors (China), and impacts on the US dollar.

What about Europe? Well, Bloomberg offers this primer – Europe’s QE Quandary. Short take – there are 18 nations which have to agree and move together, Germany’s support being decisive. But deflation appears to be spreading in Europe, so many expect something to be done along QE lines.

If you are forecasting for businesses, government agencies, or investors, these developments by central banks around the world are critically important. Their effects may be subtle and largely in unintended consequences, but the scale of operations means you simply have to keep track.

currency forecasts, developed country forecast, European economy, financial forecasts, Quantitative Easing (QE)

Something is Happening in Europe

Something is going on in Europe.

Take a look at this chart of the euro/dollar exchange rate, and how some event triggered a step down mid week of last week (from xe.com).

euroexchange

The event in question was a press conference by Mario Draghi (See the Wall Street Journal real time blog on this event at Mario Draghi Delivers Fresh ECB Plan — Recap).

The European Central Bank under Draghi is moving into exotic territory – trying negative interest rates on bank deposits and toying with variants of Quantitative Easing (QE) involving ABS – asset backed securities.

All because the basic numbers for major European economies, including notably Germany and France (as well as long-time problem countries such as Spain), are not good. Growth has stalled or is reversing, bank lending is falling, and deflation stalks the European markets.

Europe – which, of course, is sectored into the countries inside and outside the currency union, countries in the common market, and countries in none of the above – accounts for several hundred million persons and maybe 20-30 percent of global production.

So what happens there is significant.

Then there is the Ukraine crisis.

Zerohedge ran this graphic recently showing the dependence of European countries on gas from Russia.

eurdependence

The US-led program of imposing sanctions on Russia – key individuals, companies, banks perhaps – flies in the face of the physical dependence of Germany, for example, on Russian gas.

On the other hand, there is lots of history here on all sides, including, notably, the countries formerly in the USSR in eastern Europe, who no doubt fear the increasingly nationalistic or militant stance shown by Russia currently in, for example, re-acquiring Crimea.

As Chancellor Merkel has stressed, this is an area for diplomacy and negotiation – although there are other voices and forces ready to rush more weapons and even troops to the region of conflict.

Finally, as I have been stressing from time to time, there is an emerging demographic reality which many European nations have to confront.

Edward Hugh has several salient posts on possibly overlooked impacts of aging on the various macroeconomies involved.

There also is the vote on Scotland coming up in the United Kingdom (what we may, if the “yes” votes carry, need to start calling “the British Isles.”)

I’d like to keep current with the signals coming from Europe in a few blogs upcoming – to see, for example, whether swing events in the next six months to a year could originate there.

accuracy of forecasts, Federal Reserve policies and impacts, financial forecasting, forecasting mortgage interest rates, Quantitative Easing (QE)

Interest Rates – 2

I’ve been looking at forecasting interest rates, the accuracy of interest rate forecasts, and teasing out predictive information from the yield curve.

This literature can be intensely theoretical and statistically demanding. But it might be quickly summarized by saying that, for horizons of more than a few months, most forecasts (such as from the Wall Street Journal’s Panel of Economists) do not beat a random walk forecast.

At the same time, there are hints that improvements on a random walk forecast might be possible under special circumstances, or for periods of time.

For example, suppose we attempt to forecast the 30 year fixed mortgage rate monthly averages, picking a six month forecast horizon.

The following chart compares a random walk forecast with an autoregressive (AR) model.

30yrfixed2

Let’s dwell for a moment on some of the underlying details of the data and forecast models.

The thick red line is the 30 year fixed mortgage rate for the prediction period which extends from 2007 to the most recent monthly average in 2014 in January 2014. These mortgage rates are downloaded from the St. Louis Fed data site FRED.

This is, incidentally, an out-of-sample period, as the autoregressive model is estimated over data beginning in April 1971 and ending September 2007. The autoregressive model is simple, employing a single explanatory variable, which is the 30 year fixed rate at a lag of six months. It has the following form,

rt = k + βrt-6

where the constant term k and the coefficient β of the lagged rate rt-6 are estimated by ordinary least squares (OLS).

The random walk model forecast, as always, is the most current value projected ahead however many periods there are in the forecast horizon. This works out to using the value of the 30 year fixed mortgage in any month as the best forecast of the rate that will obtain six months in the future.

Finally, the errors for the random walk and autoregressive models are calculated as the forecast minus the actual value.

When an Autoregressive Model Beats a Random Walk Forecast

The random walk errors are smaller in absolute value than the autoregressive model errors over most of this out-of-sample period, but there are times when this is not true, as shown in the graph below.

30yrfixedARbetter

This chart itself suggests that further work could be done on optimizing the autoregressive model, perhaps by adding further corrections from the residuals, which themselves are autocorrelated.

However, just taking this at face value, it’s clear the AR model beats the random walk forecast when the direction of interest rates changes from a downward movement.

Does this mean that going forward, an AR model, probably considerably more sophisticated than developed for this exercise, could beat a random walk forecast over six month forecast horizons?

That’s an interesting and bankable question. It of course depends on the rate at which the Fed “withdraws the punch bowl” but it’s also clear the Fed is no longer in complete control in this situation. The markets themselves will develop a dynamic based on expectations and so forth.

In closing, for reference, I include a longer picture of the 30 year fixed mortgage rates, which as can be seen, resemble the whole spectrum of rates in having a peak in the early 1980’s and showing what amounts to trends before and after that.

30yrfixedFRED

asset bubbles, financial forecasts, forecasting asset bubbles, global business forecasts, Quantitative Easing (QE)

And Now – David Stockman

David Stockman, according to his new website Contra Corner,

is the ultimate Washington insider turned iconoclast. He began his career in Washington as a young man and quickly rose through the ranks of the Republican Party to become the Director of the Office of Management and Budget under President Ronald Reagan. After leaving the White House, Stockman had a 20-year career on Wall Street.

Currently, Stockman takes the contrarian view that the US Federal Reserve Bank is feeding a giant bubble which is bound to collapse

He states his opinions with humor and wit, as some of article titles on Contra Corner indicate –

Fed’s Taper Kabuki is Farce; Gong Show of Cacophony, Confusion and Calamity Coming

Or

General John McCain Strikes Again!

Big Data, data science, dynamics of social media, Fed tapering, Quantitative Easing (QE)

Links January 16, 2014

Economic Outlook

Central Station: January Fed Taper on Track

Federal Reserve officials, including a strong supporter of their easy money policies, have so far brushed off the weak employment report as a blip in an otherwise strengthening economic recovery. This suggests they are likely to stick to their plan to gradually wind down their bond-buying program this year as the recovery picks up momentum…

“True, the December jobs report was disappointing,” said Chicago Fed President Charles Evans, who has been a champion of aggressive central bank efforts to spur stronger economic growth. But, he added, “the recent data on economic activity generally have been encouraging” and “importantly, the labor market has improved.”

He said the tentative plan to reduce the monthly bond buys in $10 billion increments “seems quite reasonable” and “it makes sense to continue that in January.

Atlanta Fed President Dennis Lockhart, a centrist on Fed policies, said Monday the December employment report hadn’t shaken his expectation that the central bank would stick to the taper plan.

Meanwhile two opponents of the bond-buying program, Dallas Fed President Richard Fisher and Philadelphia Fed President Charles Plosser indicated in separate speeches Tuesday they were all for winding it down.

Given that chorus, it appears probable Fed officials will trim their monthly bond purchases to $65 billion from $75 billion at their next policy meeting January 28-29 meeting. Now that tapering is under way, the bar for stopping the process seems quite high.

Big Data

Big Data systems are making a difference in the fight against cancer Open source, distributed computing tools speedup an important processing pipeline for genomics data

Big Data to increase e-tailer profits

As tablet and smartphone usage becomes more widespread, shopping online has become quicker and easier and the speed of delivery has become critical in the online fulfilment race.

The group of researchers, which includes Arne Strauss, Assistant Professor of Operational Research at Warwick Business School, propose an analytic approach that will predict when people want their shopping delivered depending on what delivery prices (or incentives such as discounts or loyalty points) are being quoted for different delivery time slots.

It takes into account accepted orders to date as well as orders that are still expected to come in….

The new approach was tested using real shopping data from a major e-grocer in the UK over a period of six months and generated a four per cent increase in profits on average in a simulation study, outperforming traditional delivery pricing policies.

Big Data and Data Science Books – A Baker’s Dozen – from Analytic Bridge

  1. Big Data: A Revolution That Will Transform How We Live, Work, and T…, by Viktor Mayer-Schonberger and Kenneth Cukier
  2. The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t, by Nate Silver
  3. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie…, by Eric Siegel
  4. The Human Face of Big Data, by Rick Smolan and Jennifer Erwitt
  5. Data Science for Business: What you need to know about data mining …, by Foster Provost and Tom Fawcett
  6. The Black Swan: The Impact of the Highly Improbable, by Nassim Nicholas Taleb
  7. Competing on Analytics: The New Science of Winning, by Thomas H. Davenport and Jeanne G. Harris
  8. Super Crunchers: Why Thinking-by-Numbers is the New Way to Be Smart, by Ian Ayres
  9. Big Data Marketing: Engage Your Customers More Effectively and Driv…, by Lisa Arthur
  10. Journeys to Data Mining: Experiences from 15 Renowned Researchers, by Mohamed Medhat Gaber (editor)
  11. The Fourth Paradigm: Data-Intensive Scientific Discovery, by T.Hey, S.Tansley, and K.Tolle (editors)
  12. Seven Databases in Seven Weeks: A Guide to Modern Databases and the…, by Eric Redmond and Jim Wilson
  13. Data Mining And Predictive Analysis: Intelligence Gathering And Cri…, by Colleen McCue

Social Media

The History and Evolution of Social Media an Infographic (click to enlarge)

socialmediahistory

If you like this, there are many more infographics focusing on social media at https://www.pinterest.com/JuanCMejiaLlano/social-media-ingles/ – some in Spanish. Also check out Top 10 Infographics of 2013 [Daily Infographic].

Economics

Economics: Science, Craft, or Snake Oil? – nice, but sort of equivocating essay by Dani Rodrik from his new offices at the Princeton Institute for Advanced Studies. Answer – all of the above.

Origin and Importance of the Salesman in US – A piece of US business history and culture. I like this.