Category Archives: financial forecasting

The Laplace Distribution and Financial Returns

Well, using EasyFit from Mathwave, I fit a Laplace distribution to the residuals of the regression on S&P daily returns I discussed yesterday.

Here is the result.


This beats a normal distribution hands down. It also appears to beat the Matlab fit of a t distribution, but I have to run down more details on forms of the t-distribution to completely understand what is going on in the Matlab setup.

Note that EasyFit is available for a free 30-day trial download. It’s easy to use and provides metrics on goodness of fit to make comparisons between distributions.

There is a remarkable book online called The Laplace Distribution and Generalizations. If you have trouble downloading it from the site linked here, Google the title and find the download for a free PDF file.

This book, dating from 2001, runs to 458 pages, has a good introductory discussion, extensive mathematical explorations, as well as applications to engineering, physical science, and finance.

The French mathematical genius Pierre Simon Laplace proposed the distribution named after him as a first law of errors when he was 25, before his later discussions of the normal distribution.

The normal probability distribution, of course, “took over” – in part because of its convenient mathematical properties and also, probably, because a lot of ordinary phenomena are linked with Gaussian processes.

John Maynard Keynes, the English economist, wrote an early monograph (Keynes, J.M. (1911). The principal averages and the laws of error which lead to them, J. Roy. Statist. Soc. 74, New Series, 322-331) which substantially focuses on the Laplace distribution, highlighting the importance it gives to the median, rather than average, of sample errors.

The question I’ve struggled with is “why should stock market trading, stock prices, stock indexes lead, after logarithmic transformation and first differencing to the Laplace distribution?”

Of course, the Laplace distribution can be generated as a difference of exponential distributions, or as combination of a number of distributions, as the following table from Kotz, Kozubowski, and Podgorski’s book shows.


This is all very suggestive, but how can it be related to the process of trading?

Indeed, there are quite a number of questions which follow from this hypothesis – that daily trading activity is fundamentally related to a random component following a Laplace distribution.

What about regression, if the error process is not normally distributed? By following the standard rules on “statistical significance,” might we be led to disregard variables which are drivers for daily returns or accept bogus variables in predictive relationships?

Distributional issues are important, but too frequently disregarded.

I recall a blog discussion by a hedge fund trader lamenting excesses in the application of the Black-Scholes Theorem to options in 2007 and thereafter.

Possibly, the problem is as follows. The residuals of autoregressions on daily returns and their various related transformations tend to cluster right around zero, but have big outliers. This clustering creates false confidence, making traders vulnerable to swings or outliers that occur much more frequently than suggested by a normal or Gaussian error distribution.

The Distribution of Daily Stock Market Returns

I think it is about time for another dive into stock market forecasting. The US market is hitting new highs with the usual warnings of excess circulating around. And there are interesting developments in volatility.

To get things rolling, consider the following distribution of residuals from an autoregressive model on the difference in the natural logarithms of the S&P 500.

This is what I am calling “the distribution of daily stock market returns.” I’ll explain that further in a minute.


Now I’ve seen this distribution before, and once asked in a post, “what type of distribution is this?”

Now I think I have the answer – it’s a Laplace distribution, sometimes known as the double exponential distribution.

Since this might be important, let me explain the motivation and derivation of these residuals, and then consider some implications.

Derivation of the Residuals

First, why not just do a histogram of the first differences of daily returns to identify the underlying distribution? After all, people say movement of stock market indexes are a random walk.

OK, well you could do that, and the resulting distribution would also look “Laplacian” with a pointy peak and relative symmetry. However, I am bothered in developing this by the fact that these first differences show significant, even systematic, autocorrelation.

I’m influenced here by the idea that you always want to try to graph independent draws from a distribution to explore the type of distribution.

OK, now to details of my method.

The data are based on daily closing values for the S&P 500 index from December 4, 1989 to February 7, 2014.

I took the natural log of these closing values and then took first differences – subtracting the previous trading day’s closing value from the current day’s closing value. This means that these numbers encode the critical part of the daily returns, which are calculated as day-over-day percent changes. Thus, the difference of natural logs is in fact a ratio of the original numbers – what you might look at as the key part of the percent change from one trading day to the next.

So I generate a conventional series of first differences of the natural log of this nonstationary time series. This transforms the original nonstationary series to a  one that basically fluctuates around a level – essentially zero. Furthermore, the log transform tends to reduce the swings in the variability of the series, although significant variability remains.


Removing Serial Correlation

The series graphed above exhibits first order serial correlation. It also exhibits second order serial correlation, or correlation between values at a lag of 2.

Based on the correlations for the first 24 lags, I put together this regression equation. Of course, the “x’s” refer to time dated first differences of the natural log of the S&P daily closing values.


Note that most of the t-statistics pass our little test of significance (which I think is predicted to an extent on the error process belonging to certain distributions..but). The coefficient of determination or R2 is miniscule – at 0.017. This autoregressive equation thus explains only about 2 percent of the variation in this differenced log daily closing values series.

Now one of the things I plan to address is how, indeed, that faint predictive power can exert significant influence on earnings from stock trading, given trading rules.

But let me leave that whole area – how you make money with such a relationship – to a later discussion, since I’ve touched on this before.

Instead, let me just observe that if you subtract the predicted values from this regression from the actuals trading day by trading day, you get the data for the pointy, highly symmetric distribution of residuals.

Furthermore, these residuals do not exhibit first or second, or higher, autocorrelation, so far as I am able to determine.

This means we have separated out algorithmic components of this series from random components that are not serially correlated.

So you might jump to the conclusion that these residuals are then white noise, and I think many time series modelers have gotten to this point, simply assuming they are dealing with Gaussian white noise.

Nothing could be further from the truth, as the following Matlab fit of a normal distribution to some fairly crude bins for these numbers.


A Student-t distribution does better, as the following chart shows.


But the t-distribution still misses the pointed peak.

The Laplace distribution is also called the double exponential, since it can be considered to be a composite of exponentials on the right and to the left of the mean – symmetric but mirror images of each other.

The following chart shows how this works over the positive residuals.


Now, of course, there are likelihood ratios and those sorts of metrics, and I am busy putting together a comparison between the t-distribution fit and Laplace distribution fit.

There is a connection between the Laplace distribution and power laws, too, and I note considerable literature on this distribution in finance and commodities.

I think I have answered the question I put out some time back, though, and, of course, it raises other questions in its wake.

Looking Ahead, Looking Back

Looking ahead, I’m almost sure I want to explore forecasting in the medical field this coming week. Menzie Chin at Econbrowser, for example, highlights forecasts that suggest states opting out of expanded Medicare are flirting with higher death rates. This sets off a flurry of comments, highlighting the importance and controversy attached to various forecasts in the field of medical practice.

There’s a lot more – from bizarre and sad mortality trends among Russian men since the collapse of the Soviet Union, now stabilizing to an extent, to systems which forecast epidemics, to, again, cost and utilization forecasts.

Today, however, I want to wind up this phase of posts on forecasting the stock and related financial asset markets.

Market Expectations in the Cross Section of Present Values

That’s the title of Bryan Kelly and Seth Pruitt’s article in the Journal of Finance, downloadable from the Social Science Research Network (SSRN).

The following chart from this paper shows in-sample (IS) and out-of-sample (OOS) performance of Kelly and Pruitt’s new partial least squares (PLS) predictor, and IS and OOS forecasts from another model based on the aggregate book-to-market ratio. (Click to enlarge)


The Kelly-Pruitt PLS predictor is much better in both in-sample and out-of-sample than the more traditional regression model based on aggregate book-t0-market ratios.

What Kelly and Pruitt do is use what I would call cross-sectional time series data to estimate aggregate market returns.

Basically, they construct a single factor which they use to predict aggregate market returns from cross-sections of portfolio-level book-to-market ratios.


To harness disaggregated information we represent the cross section of asset-specific book-to-market ratios as a dynamic latent factor model. We relate these disaggregated value ratios to aggregate expected market returns and cash flow growth. Our model highlights the idea that the same dynamic state variables driving aggregate expectations also govern the dynamics of the entire panel of asset-specific valuation ratios. This representation allows us to exploit rich cross-sectional information to extract precise estimates of market expectations.

This cross-sectional data presents a “many predictors” type of estimation problem, and the authors write that,

Our solution is to use partial least squares (PLS, Wold (1975)), which is a simple regression-based procedure designed to parsimoniously forecast a single time series using a large panel of predictors. We use it to construct a univariate forecaster for market returns (or dividend growth) that is a linear combination of assets’ valuation ratios. The weight of each asset in this linear combination is based on the covariance of its value ratio with the forecast target.

I think it is important to add that the authors extensively explore PLS as a procedure which can be considered to be built from a series of cross-cutting regressions, as it were (See their white paper on three-pass regression filter).

But, it must be added, this PLS procedure can be summarized in a single matrix formula, which is


Readers wanting definitions of these matrices should consult the Journal of Finance article and/or the white paper mentioned above.

The Kelly-Pruitt analysis works where other methods essentially fail – in OOS prediction,

Using data from 1930-2010, PLS forecasts based on the cross section of portfolio-level book-to-market ratios achieve an out-of-sample predictive R2 as high as 13.1% for annual market returns and 0.9% for monthly returns (in-sample R2 of 18.1% and 2.4%, respectively). Since we construct a single factor from the cross section, our results can be directly compared with univariate forecasts from the many alternative predictors that have been considered in the literature. In contrast to our results, previously studied predictors typically perform well in-sample but become insignifcant out-of-sample, often performing worse than forecasts based on the historical mean return …

So, the bottom line is that aggregate stock market returns are predictable from a common-sense perspective, without recourse to abstruse error measures. And I believe Amit Goyal, whose earlier article with Welch contests market predictability, now agrees (personal communication) that this application of a PLS estimator breaks new ground out-of-sample – even though its complexity asks quite a bit from the data.

Note, though, how volatile aggregate realized returns for the US stock market are, and how forecast errors of the Kelly-Pruitt analysis become huge during the 2008-2009 recession and some previous recessions – indicated by the shaded lines in the above figure.

Still something is better than nothing, and I look for improvements to this approach – which already has been applied to international stocks by Kelly and Pruitt and other slices portfolio data.

More on the Predictability of Stock and Bond Markets

Research by Lin, Wu, and Zhou in Predictability of Corporate Bond Returns: A Comprehensive Study suggests a radical change in perspective, based on new forecasting methods. The research seems to me to of a piece with a lot of developments in Big Data and the data mining movement generally. Gains in predictability are associated with more extensive databases and new techniques.

The abstract to their white paper, presented at various conferences and colloquia, is straight-forward –

Using a comprehensive data set, we find that corporate bond returns not only remain predictable by traditional predictors (dividend yields, default, term spreads and issuer quality) but also strongly predictable by a new predictor formed by an array of 26 macroeconomic, stock and bond predictors. Results strongly suggest that macroeconomic and stock market variables contain important information for expected corporate bond returns. The predictability of returns is of both statistical and economic significance, and is robust to different ratings and maturities.

Now, in a way, the basic message of the predictability of corporate bond returns is not news, since Fama and French made this claim back in 1989 – namely that default and term spreads can predict corporate bond returns both in and out of sample.

What is new is the data employed in the Lin, Wu, and Zhou (LWZ) research. According to the authors, it involves 780,985 monthly observations spanning from January 1973 to June 2012 from combined data sources, including Lehman Brothers Fixed Income (LBFI), Datastream, National Association of Insurance Commissioners (NAIC), Trade Reporting and Compliance Engine (TRACE) and Mergents Fixed Investment Securities Database (FISD).

There also is a new predictor which LWZ characterize as a type of partial least squares (PLS) formulation, but which is none other than the three pass regression filter discussed in a post here in March.

The power of this PLS formulation is evident in a table showing out-of-sample R2 of the various modeling setups. As in the research discussed in a recent post, out-of-sample (OS) R2 is a ratio which measures the improvement in mean square prediction errors (MSPE) for the predictive regression model over the historical average forecast. A negative OS R2 thus means that the MSPE of the benchmark forecast is less than the MSPE of the forecast by the designated predictor formulation.


Again, this research finds predictability varies with economic conditions – and is higher during economic downturns.

There are cross-cutting and linked studies here, often with Goyal’s data and fourteen financial/macroeconomic variables figuring within the estimations. There also is significant linkage with researchers at regional Federal Reserve Banks.

My purpose in this and probably the next one or two posts is to just get this information out, so we can see the larger outlines of what is being done and suggested.

My guess is that the sum total of this research is going to essentially re-write financial economics and has huge implications for forecasting operations within large companies and especially financial institutions.

Stock Market Predictability – Controversy

In the previous post, I drew from papers by Neeley, who is Vice President of the Federal Reserve Bank of St. Louis, David Rapach at St. Louis University and Goufu Zhou at Washington University in St. Louis.

These authors contribute two papers on the predictability of equity returns.

The earlier one – Forecasting the Equity Risk Premium: The Role of Technical Indicators – is coming out in Management Science. Of course, the survey article – Forecasting the Equity Risk Premium: The Role of Technical Indicators – is a chapter in the recent volume 2 of the Handbook of Forecasting.

I go through this rather laborious set of citations because it turns out that there is an underlying paper which provides the data for the research of these authors, but which comes to precisely the opposite conclusion –

The goal of our own article is to comprehensively re-examine the empirical evidence as of early 2006, evaluating each variable using the same methods (mostly, but not only, in linear models), time-periods, and estimation frequencies. The evidence suggests that most models are unstable or even spurious. Most models are no longer significant even insample (IS), and the few models that still are usually fail simple regression diagnostics.Most models have performed poorly for over 30 years IS. For many models, any earlier apparent statistical significance was often based exclusively on years up to and especially on the years of the Oil Shock of 1973–1975. Most models have poor out-of-sample (OOS) performance, but not in a way that merely suggests lower power than IS tests. They predict poorly late in the sample, not early in the sample. (For many variables, we have difficulty finding robust statistical significance even when they are examined only during their most favorable contiguous OOS sub-period.) Finally, the OOS performance is not only a useful model diagnostic for the IS regressions but also interesting in itself for an investor who had sought to use these models for market-timing. Our evidence suggests that the models would not have helped such an investor. Therefore, although it is possible to search for, to occasionally stumble upon, and then to defend some seemingly statistically significant models, we interpret our results to suggest that a healthy skepticism is appropriate when it comes to predicting the equity premium, at least as of early 2006. The models do not seem robust.

This is from Ivo Welch and Amit Goyal’s 2008 article A Comprehensive Look at The Empirical Performance of Equity Premium Prediction in the Review of Financial Studies which apparently won an award from that journal as the best paper for the year.

And, very importantly, the data for this whole discussion is available, with updates, from Amit Goyal’s site now at the University of Lausanne.


Where This Is Going

Currently, for me, this seems like a genuine controversy in the forecasting literature. And, as an aside, in writing this blog I’ve entertained the notion that maybe I am on the edge of a new form of or focus in journalism – namely stories about forecasting controversies. It’s kind of wonkish, but the issues can be really, really important.

I also have a “hands-on” philosophy, when it comes to this sort of information. I much rather explore actual data and run my own estimates, than pick through theoretical arguments.

So anyway, given that Goyal generously provides updated versions of the data series he and Welch originally used in their Review of Financial Studies article, there should be some opportunity to check this whole matter. After all, the estimation issues are not very difficult, insofar as the first level of argument relates primarily to the efficacy of simple bivariate regressions.

By the way, it’s really cool data.

Here is the book-to-market ratio, dating back to 1926.


But beyond these simple regressions that form a large part of the argument, there is another claim made by Neeley, Rapach, and Zhou which I take very seriously. And this is that – while a “kitchen sink” model with all, say, fourteen so-called macroeconomic variables does not outperform the benchmark, a principal components regression does.

This sounds really plausible.

Anyway, if readers have flagged updates to this controversy about the predictability of stock market returns, let me know. In addition to grubbing around with the data, I am searching for additional analysis of this point.

Evidence of Stock Market Predictability

In business forecast applications, I often have been asked, “why don’t you forecast the stock market?” It’s almost a variant of “if you’re so smart, why aren’t you rich?” I usually respond something about stock prices being largely random walks.

But, stock market predictability is really the nut kernel of forecasting, isn’t it?

Earlier this year, I looked at the S&P 500 index and the SPY ETF numbers, and found I could beat a buy and hold strategy with a regression forecasting model. This was an autoregressive model with lots of lagged values of daily S&P returns. In some variants, it included lagged values of the Chicago Board of Trade VIX volatility index returns. My portfolio gains were compiled over an out-of-sample (OS) period. This means, of course, that I estimated the predictive regression on historical data that preceded and did not include the OS or test data.

Well, today I’m here to report to you that it looks like it is officially possible to achieve some predictability of stock market returns in out-of-sample data.

One authoritative source is Forecasting Stock Returns, an outstanding review by Rapach and Zhou  in the recent, second volume of the Handbook of Economic Forecasting.

The story is fascinating.

For one thing, most of the successful models achieve their best performance – in terms of beating market averages or other common benchmarks – during recessions.

And it appears that technical market indicators, such as the oscillators, momentum, and volume metrics so common in stock trading sites, have predictive value. So do a range of macroeconomic indicators.

But these two classes of predictors – technical market and macroeconomic indicators – are roughly complementary in their performance through the business cycle. As Christopher Neeley et al detail in Forecasting the Equity Risk Premium: The Role of Technical Indicators,

Macroeconomic variables typically fail to detect the decline in the actual equity risk premium early in recessions, but generally do detect the increase in the actual equity risk premium late in recessions. Technical indicators exhibit the opposite pattern: they pick up the decline in the actual premium early in recessions, but fail to match the unusually high premium late in recessions.

Stock Market Predictors – Macroeconomic and Technical Indicators

Rapach and Zhou highlight fourteen macroeconomic predictors popular in the finance literature.

1. Log dividend-price ratio (DP): log of a 12-month moving sum of dividends paid on the S&P 500 index minus the log of stock prices (S&P 500 index).

2. Log dividend yield (DY): log of a 12-month moving sum of dividends minus the log of lagged stock prices.

3. Log earnings-price ratio (EP): log of a 12-month moving sum of earnings on the S&P 500 index minus the log of stock prices.

4. Log dividend-payout ratio (DE): log of a 12-month moving sum of dividends minus the log of a 12-month moving sum of earnings.

5. Stock variance (SVAR): monthly sum of squared daily returns on the S&P 500 index.

6. Book-to-market ratio (BM): book-to-market value ratio for the DJIA.

7. Net equity expansion (NTIS): ratio of a 12-month moving sum of net equity issues by NYSE-listed stocks to the total end-of-year market capitalization of NYSE stocks.

8. Treasury bill rate (TBL): interest rate on a three-month Treasury bill (secondary market).

9. Long-term yield (LTY): long-term government bond yield.

10. Long-term return (LTR): return on long-term government bonds.

11. Term spread (TMS): long-term yield minus the Treasury bill rate.

12. Default yield spread (DFY): difference between BAA- and AAA-rated corporate bond yields.

13. Default return spread (DFR): long-term corporate bond return minus the long-term government bond return.

14. Inflation (INFL): calculated from the CPI (all urban consumers

In addition, there are technical indicators, which are generally moving average, momentum, or volume-based.

The moving average indicators typically provide a buy or sell signal based on a comparing two moving averages – a short and a long period MA.

Momentum based rules are based on the time trajectory of prices. A current stock price higher than its level some number of periods ago indicates “positive” momentum and expected excess returns, and generates a buy signal.

Momentum rules can be combined with information about the volume of stock purchases, such as Granville’s on-balance volume.

Each of these predictors can be mapped onto equity premium excess returns – measured by the rate of return on the S&P 500 index net of return on a risk-free asset. This mapping is a simple bi-variate regression with equity returns from time t on the left side of the equation and the economic predictor lagged by one time period on the right side of the equation. Monthly data are used from 1927 to 2008. The out-of-sample (OS) period is extensive, dating from the 1950’s, and includes most of the post-war recessions.

The following table shows what the authors call out-of-sample (OS) R2 for the 14 so-called macroeconomic variables, based on a table in the Handbook of Forecasting chapter. The OS R2 is equal to 1 minus a ratio. This ratio has the mean square forecast error (MSFE) of the predictor forecast in the numerator and the MSFE of the forecast based on historic average equity returns in the denominator. So if the economic indicator functions to improve the OS forecast of equity returns, the OS R2 is positive. If, on the other hand, the historic average trumps the economic indicator forecast, the OS R2 is negative.


(click to enlarge).

Overall, most of the macro predictors in this list don’t make it.  Thus, 12 of the 14 OS R2 statistics are negative in the second column of the Table, indicating that the predictive regression forecast has a higher MSFE than the historical average.

For two of the predictors with a positive out-of-sample R2, the p-values reported in the brackets are greater than 0.10, so that these predictors do not display statistically significant out-of-sample performance at conventional levels.

Thus, the first two columns in this table, under “Overall”, support a skeptical view of the predictability of equity returns.

However, during recessions, the situation is different.

For several the predictors, the R2 OS statistics move from being negative (and typically below -1%) during expansions to 1% or above during recessions. Furthermore, some of these R2 OS statistics are significant at conventional levels during recessions according to the  p-values, despite the decreased number of available observations.

Now imposing restrictions on the regression coefficients substantially improves this forecast performance, as the lower panel (not shown) in this table shows.

Rapach and Zhou were coauthors of the study with Neeley, published earlier as a working paper with the St. Louis Federal Reserve.

This working paper is where we get the interesting report about how technical factors add to the predictability of equity returns (again, click to enlarge).


This table has the same headings for the columns as Table 3 above.

It shows out-of-sample forecasting results for several technical indicators, using basically the same dataset, for the overall OS period, for expansions, and recessions in this period dating from the 1950’s to 2008.

In fact, these technical indicators generally seem to do better than the 14 macroeconomic indicators.

Low OS R2

Even when these models perform their best, their increase in mean square forecast error (MSFE) is only slightly more than the MSFE of the benchmark historic average return estimate.

This improved performance, however, can still achieve portfolio gains for investors, based on various trading rules, and, as both papers point out, investors can use the information in these forecasts to balance their portfolios, even when the underlying forecast equations are not statistically significant by conventional standards. Interesting argument, and I need to review it further to fully understand it.

In any case, my experience with an autoregressive model for the S&P 500 is that trading rules can be devised which produce portfolio gains over a buy and hold strategy, even when the Ris on the order of 1 or a few percent. All you have to do is correctly predict the sign of the return on the following trading day, for instance, and doing this a little more than 50 percent of the time produces profits.

Rapach and Zhou, in fact, develop insights into how predictability of stock returns can be consistent with rational expectations – providing the relevant improvements in predictability are bounded to be low enough.

Some Thoughts

There is lots more to say about this, naturally. And I hope to have further comments here soon.

But, for the time being, I have one question.

The is why econometricians of the caliber of Rapach, Zhou, and Neeley persist in relying on tests of statistical significance which are predicated, in a strict sense, on the normality of the residuals of these financial return regressions.

I’ve looked at this some, and it seems the t-statistic is somewhat robust to violations of normality of the underlying error distribution of the regression. However, residuals of a regression on equity rates of return can be very non-normal with fat tails and generally some skewness. I keep wondering whether anyone has really looked at how this translates into tests of statistical significance, or whether what we see on this topic is mostly arm-waving.

For my money, OS predictive performance is the key criterion.

Forecasting Housing Markets – 3

Maybe I jumped to conclusions yesterday. Maybe, in fact, a retrospective analysis of the collapse in US housing prices in the recent 2008-2010 recession has been accomplished – but by major metropolitan area.

The Yarui Li and David Leatham paper Forecasting Housing Prices: Dynamic Factor Model versus LBVAR Model focuses on out-of-sample forecasts for house price indices for 42 metropolitan areas. Forecast models are built with data from 1980:01 to 2007:12. These models – dynamic factor and Large-scale Bayesian Vector Autoregressive (LBVAR) models – are used to generate forecasts of the one- to twelve- months ahead price growth 2008:01 to 2010:12.

Judging from the graphics and other information, the dynamic factor model (DFM) produces impressive results.

For example, here are out-of-sample forecasts of the monthly growth of housing prices (click to enlarge).


The house price indices for the 42 metropolitan areas are from the Office of Federal Housing Enterprise Oversight (OFEO). The data for macroeconomic indicators in the dynamic factor and VAR models are from the DRI/McGraw Hill Basic Economics Database provided by IHS Global Insight.

I have seen forecasting models using Internet search activity which purportedly capture turning points in housing price series, but this is something different.

The claim here is that calculating dynamic or generalized principal components of some 141 macroeconomic time series can lead to forecasting models which accurately capture fluctuations in cumulative growth rates of metropolitan house price indices over a forecasting horizon of up to 12 months.

That’s pretty startling, and I for one would like to see further output of such models by city.

But where is the publication of the final paper? The PDF file linked above was presented at the Agricultural & Applied Economics Association’s 2011 Annual Meeting in Pittsburgh, Pennsylvania, July, 2011. A search under both authors does not turn up a final publication in a refereed journal, but does indicate there is great interest in this research. The presentation paper thus is available from quite a number of different sources which obligingly archive it.


Currently, the lead author, Yarui Li, Is a Decision Tech Analyst at JPMorgan Chase, according to LinkedIn, having received her PhD from Texas A&M University in 2013. The second author is Professor at Texas A&M, most recently publishing on VAR models applied to business failure in the US.

Dynamic Principal Components

It may be that dynamic principal components are the new ingredient accounting for an uncanny capability to identify turning points in these dynamic factor model forecasts.

The key research is associated with Forni and others, who originally documented dynamic factor models in the Review of Economics and Statistics in 2000. Subsequently, there have been two further publications by Forni on this topic:

Do financial variables help forecasting inflation and real activity in the euro area?

The Generalized Dynamic Factor Model, One Sided Estimation and Forecasting

Forni and associates present this method of dynamic prinicipal componets as an alternative to the Stock and Watson factor models based on many predictors – an alternative with superior forecasting performance.

Run-of-the-mill standard principal components are, according to Li and Leatham, based on contemporaneous covariances only. So they fail to exploit the potentially crucial information contained in the leading-lagging relations between the elements of the panel.

By contrast, the Forni dynamic component approach is used in this housing price study to

obtain estimates of common and idiosyncratic variance-covariance matrices at all leads and lags as inverse Fourier transforms of the corresponding estimated spectral density matrices, and thus overcome(s)[ing] the limitation of static PCA.

There is no question but that any further discussion of this technique must go into high mathematical dudgeon, so I leave that to another time, when I have had an opportunity to make computations of my own.

However, I will say that my explorations with forecasting principal components last year have led to me to wonder whether, in fact, it may be possible to pull out some turning points from factor models based on large panels of macroeconomic data.


The following chart, courtesy of the Bank of Japan (BOJ), shows inflation and deflation dynamics in Japan since the 1980’s.


There is interesting stuff in this chart, not the least of which is the counterintuitive surge in Japanese consumer prices in the Great Recession (2008 et passim).

Note, however, that the GDP deflator fell below zero in 1994, returning to positive territory only briefly around 2009. The other index in the chart – the DCGPI  –  is a Domestic Corporate Goods Price Index calculated by Japanese statistical agencies.

The Japanese experience with deflation is relevant to deflation dynamics in the Eurozone, and has been central to the thinking and commentary of US policymakers and macroeconomic/monetary theorists, such as Benjamin Bernanke.

The conventional wisdom explains inflation and deflation with the Phillips Curve. Thus, in one variant, inflation is projected to be a function of inflationary expectations, the output gap between potential and actual GDP, and other factors – decline in export prices, demographic changes, “unlucky” historical developments, or institutional issues in the financial sector.

It makes a big difference how you model inflationary expectations in this model, as John Williams points out in a Federal Reserve Bank of San Francisco Economic Letter.

If inflationary expectations are unanchored, a severe recession can lead to a deflationary spiral.

The logic is as follows: In the early stage of recession, the emergence of slack causes the inflation rate to dip. The resulting lower inflation rate prompts people to reduce their future inflation expectations. Continued economic slack causes the inflation rate to fall still further. If the recession is severe and long enough, this process eventually will cause prices to fall and then spiral lower and lower, resulting in ever-faster deflation rates. The deflation rate stabilizes only when slack is eliminated. And inflation turns positive again only after a sustained period of tight labor markets.

This contrasts with “well anchored” inflationary expectations, where people expect the monetary authorities will step in and end deflationary episodes at some point. In technical time series terms – inflation time series exhibit longer term returns to mean values and this acts as a magnet pulling prices up in some deflationary circumstances. Janet Yellen and her husband George Ackerlof have commented on this type of dynamic in inflationary expectations.

The Industrial Side of the Picture

The BOJ Working Paper responsible for the introductory chart also considers “other factors” in the Phillips Curve explanation, presenting a fascinating table.


The huge drop in prices of electric machinery in Japan over 1990-2009 caught my attention.

The collapse in electric machinery prices represent changed conditions in export markets with cheaper and high quality electronics manufactured in China and other areas harboring contract electronics manufacturing.

Could this be a major contributor to persisting Japanese deflation, initially triggered obviously by massive drops in Japanese real estate in the earlyi 1990’s?

An interesting paper by Haruhiko Murayama of the Kyoto Research Institute – Reality and Cause of Deflation in Japan – makes a persuasive case for just that conclusion.

Murayama argues competition from China and other lower wage electronics producers is a major factor in continuing Japanese deflation.

The greatest cause of the deflation is a lack of demand, which in turn is attributable to the fact that emerging countries such as China, South Korea and Taiwan have come to manufacture inexpensive high-quality electrical products by introducing new equipment and by taking advantage of their cheap labor. While competing with emerging countries, Japanese electrical machinery makers have been forced to lower their export prices. In addition, an influx of foreign products has reduced their domestic sales, and as a result, overall earnings and demand in Japan have declined, leading to continuous price drops.

He goes on to say that,

..Japan is the only developed country whose electric machinery makers have been struggling because of the onslaught of competition from emerging countries. General Electric of the United States, which is known as a company founded by Thomas Edison and which was previously the largest electric machinery maker in the world, has already shifted its focus to the aircraft and nuclear industries, after facing intense competition with Japanese manufacturers. Other U.S. companies such as RCA (Radio Corporation of America), Motorola and Zenith no longer exist for reasons such as because they failed or were acquired by Japanese companies. The situation is similar in Europe. Consequently, whereas electric machinery accounts for as much as 19.5% of Japan’s nominal exports, equivalent products in the United States (computers and peripherals) take up only 2.3% of the overall U.S. exports (in the October-December quarter of 2012)

This explanation corresponds more directly to my personal observation with contract electronics manufacturing and, earlier, US-based electronics manufacturing. And it seems to apply relatively well for Europe – where Chinese competition in broadening areas of production pressure many European companies – creating a sort of vacuum for future employment and economic growth.

The Kyoto analysis also gets the policy prescription about right –

..the deflation in Japan is much more pervasive than is indicated by the CPI and its cause is a steep drop in export prices of electrical machinery, the main export item for the country, which has been triggered by the increasing competition from emerging countries and which makes it impossible to offset the effects of a rise in import prices by raising export prices. The onslaught of competition from emerging countries is unlikely to wane in the future. Rather, we must accept it as inevitable that emerging countries will continue to rise one after another and attempt to overtake countries that have so far enjoyed economic prosperity.

If so, what is most important is that companies facing the competition from emerging countries recognize that point and try to create high-value products that will be favored by foreign customers. It is also urgently necessary to save energy and develop new energy sources.

Moreover, companies which have not been directly affected by the rise of emerging countries should also take action on the assumption that demand will remain stagnant and deflation will continue in Japan until the electrical machinery industry recovers [or, I would add, until alternative production centers emerges]. They should tackle fundamental challenges with a sense of crisis, including how to provide products and services that precisely meet users’ needs, expand sales channels from the global perspective and exert creativity. Policymakers must develop a price index that more accurately reflects the actual price trend and take appropriate measures in light of the abovementioned challenges.

Maybe in a way that’s what Big Data is all about.

Interest Rates – Forecasting and Hedging

A lot relating to forecasting interest rates is encoded in the original graph I put up, several posts ago, of two major interest rate series – the federal funds and the prime rates.


This chart illustrates key features of interest rate series and signals several important questions. Thus, there is relationship between a very short term rate and a longer term interest rates – a sort of two point yield curve. Almost always, the federal funds rate is below the prime rate. If for short periods this is not the case, it indicates a radical reversion of the typical slope of the yield curve.

Credit spreads are not illustrated in this figure, but have been shown to be significant in forecasting key macroeconomic variables.

The shape of the yield curve itself can be brought into play in forecasting future rates, as can typical spreads between interest rates.

But the bottom line is that interest rates cannot be forecast with much accuracy beyond about a two quarter forecast horizon.

There is quite a bit of research showing this to be true, including –

Professional Forecasts of Interest Rates and Exchange Rates: Evidence from the Wall Street Journal’s Panel of Economists

We use individual economists’ 6-month-ahead forecasts of interest rates and exchange rates from the Wall Street Journal’s survey to test for forecast unbiasedness, accuracy, and heterogeneity. We find that a majority of economists produced unbiased forecasts but that none predicted directions of changes more accurately than chance. We find that the forecast accuracy of most of the economists is statistically indistinguishable from that of the random walk model when forecasting the Treasury bill rate but that the forecast accuracy is significantly worse for many of the forecasters for predictions of the Treasury bond rate and the exchange rate. Regressions involving deviations in economists’ forecasts from forecast averages produced evidence of systematic heterogeneity across economists, including evidence that independent economists make more radical forecasts

Then, there is research from the London School of Economics Interest Rate Forecasts: A Pathology

In this paper we have demonstrated that, in the two countries and short data periods studied, the forecasts of interest rates had little or no informational value when the horizon exceeded two quarters (six months), though they were good in the next quarter and reasonable in the second quarter out. Moreover, all the forecasts were ex post and, systematically, inefficient, underestimating (overestimating) future outturns during up (down) cycle phases. The main reason for this is that forecasters cannot predict the timing of cyclical turning points, and hence predict future developments as a convex combination of autoregressive momentum and a reversion to equilibrium

Also, the Chapter in the Handbook of Forecasting Forecasting interest rates is relevant, although highly theoretical.

Hedging Interest Rate Risk

As if in validation of this basic finding – beyond about two quarters, interest rate forecasts generally do not beat a random walk forecast – interest rate swaps, are the largest category of interest rate contracts of derivatives, according to the Bank of International Settlements (BIS).


Not only that, but interest rate contracts generally are, by an order of magnitude, the largest category of OTC derivatives – totaling more than a half a quadrillion dollars as of the BIS survey in July 2013.

The gross value of these contracts was only somewhat less than the Gross Domestic Product (GDP) of the US.

A Bank of International Settlements background document defines “gross market values” as follows;

Gross positive and negative market values: Gross market values are defined as the sums of the absolute values of all open contracts with either positive or negative replacement values evaluated at market prices prevailing on the reporting date. Thus, the gross positive market value of a dealer’s outstanding contracts is the sum of the replacement values of all contracts that are in a current gain position to the reporter at current market prices (and therefore, if they were settled immediately, would represent claims on counterparties). The gross negative market value is the sum of the values of all contracts that have a negative value on the reporting date (ie those that are in a current loss position and therefore, if they were settled immediately, would represent liabilities of the dealer to its counterparties).  The term “gross” indicates that contracts with positive and negative replacement values with the same counterparty are not netted. Nor are the sums of positive and negative contract values within a market risk category such as foreign exchange contracts, interest rate contracts, equities and commodities set off against one another. As stated above, gross market values supply information about the potential scale of market risk in derivatives transactions. Furthermore, gross market value at current market prices provides a measure of economic significance that is readily comparable across markets and products.

Clearly, by any account, large sums of money and considerable exposure are tied up in interest rate contracts in the over the counter (OTC) market.

A Final Thought

This link between forecastability and financial derivatives is interesting. There is no question but that, in practical terms, business is putting eggs in the basket of managing interest rate risk, as opposed to refining forecasts – which may not be possible beyond a certain point, in any case.

What is going to happen when the quantitative easing maneuvers of central banks around the world cease, as they must, and long term interest rates rise in a consistent fashion? That’s probably where to put the forecasting money.

Credit Spreads As Predictors of Real-Time Economic Activity

Several distinguished macroeconomic researchers, including Ben Bernanke, highlight the predictive power of the “paper-bill” spread.

The following graphs, from a 1993 article by Benjamin M. Friedman and Kenneth N. Kuttner, show the promise of credit spreads in forecasting recessions – indicated by the shaded blocks in the charts.


Credit spreads, of course, are the differences in yields between various corporate debt instruments and government securities of comparable maturity.

The classic credit spread illustrated above is the difference between six-month commercial paper rates and 6 month Treasury bill rates.

Recent Research

More recent research underlines the importance of building up credit spreads from metrics relating to individual corporate bonds , rather than a mishmash of bonds with different duration, credit risk and other characteristics.

Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach is key research in this regard.

The authors first note that,

the “paper-bill” spread—the difference between yields on nonfinancial commercial paper and comparable-maturity Treasury bills—had substantial forecasting power for economic activity during the 1970s and the 1980s, but its predictive ability vanished in the subsequent decade

They then acknowledge that credit spreads based on indexes of speculative-grade or “junk” corporate bonds work fairly well for the 1990s, but their performance is uneven.

Accordingly, Faust, Gilchrist, Wright, and Zakrajsek (GYZ) write that

In part to address these problems, GYZ constructed 20 monthly credit spread indexes for different maturity and credit risk categories using secondary market prices of individual senior unsecured corporate bonds.. [measuring]..the underlying credit risk by the issuer’s expected default frequency (EDF™), a market-based default-risk indicator calculated by Moody’s/KMV that is more timely that the issuer’s credit rating]

Their findings indicate that these credit spread indexes have substantial predictive power, at both short- and longer-term horizons, for the growth of payroll employment and industrial production. Moreover, they significantly outperform the predictive ability of the standard default-risk indicators, a result that suggests that using “cleaner” measures of credit spreads may, indeed, lead to more accurate forecasts of economic activity.

Their research applies credit spreads constructed from the ground up, as it were, to out-of-sample forecasts of

…real economic activity, as measured by real GDP, real personal consumption expenditures (PCE), real business fixed investment, industrial production, private payroll employment, the civilian unemployment rate, real exports, and real imports over the period from 1986:Q1 to 2011:Q3. All of these series are in quarter-over-quarter growth rates (actually 400 times log first differences), except for the unemployment rate, which is simply in first differences

The results are forecasts which significantly beat univariate (autoregressive) model forecass, as shown in the following table.


Here BMA is an abbreviation for Bayesian Model Averaging, the author’s method of incorporating these calculated credit spreads in predictive relationships.

Additional research validates the usefulness of credit spreads so constructed for predicting macroeconomic dynamics in several European economies –

We find that credit spreads and excess bond premiums, when used alongside monetary policy tightness indicators and leading indicators of economic performance, are highly significant for predicting the growth in the index of industrial production, employment growth, the unemployment rate and real GDP growth at horizons ranging from one quarter to two years ahead. These results are confirmed for individual countries in the euroarea and for the United Kingdom, and are robust to different measures of the credit spread. It is the unpredictable part associated with the excess bond premium that has greater influence on real activity compared to the predictable part of the credit spread. The implications of our results are that careful selection of the bonds used to construct the credit spreads, excluding those with embedded options and or illiquid secondary markets, delivers a robust indicator of financial market tightness that is distinct from tightness due to monetary policy measures or leading indicators of economic activity.

The Situation Today

A Morgan Stanley Credit Report for fixed income, released March 21, 2014, notes that

Spreads in both IG and HY are at the lowest levels we have seen since 2007, roughly 110bp for IG and 415bp for HY. A question we are commonly asked is how much tighter can spreads go in this cycle

So this is definitely something to watch.