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*E-mail: leandromgomess@gmail.com
Global Asset Pricing and Financial Intermediary Leverage
Leandro de Miranda Gomes*
PUC-RIO
March 2015
Abstract
We propose a 2-factor global asset pricing model based on prices of risk of financial
intermediary firms leverage and US market returns. We show that our model present
considerably better statistical and economic properties, as small intercept and
economically meaningful prices of risk, than benchmarks like US Fama-French 3-
factor, Global US Fama-French and single factor leverage model. There are also
considerably improvements when simultaneously pricing US and International
portfolios in comparison to US-only portfolios, indicating a global presence of US
financial intermediary system on asset pricing through different markets. We use
conventional 25 size-book to market sorted US and International portfolios, but our
results are robust to the inclusion of momentum portfolios and maturity sorted bonds
portfolios.
1. Introduction
At the Federal Reserve Bank of Bostons 56th
Economic Conference on October
2011, the President & Chief Executive Officer of the institution, Eric S. Rosegreen,
addressed the long term effects of the Great Recession, highlighting the role of large
global financial intermediaries as they suffered severe distress on the events of the crisis
and had a decisive role transmitting the shocks to the wider global financial system
through their interconnectedness with other financial institutions.
Given its relevant size on the financial system, the intermediary sector is not
only an important global financial link, but also plays a relevant role on the US
Financial Markets. Within the intermediary sector, security dealers and brokers and
institutions from the shadow banking system appear to be the most prominent ones,
presenting strong explanatory power for many financial asset returns, as suggested by
Adrian, Moench and Shin (2010), including a relevant role on the explanation of the
cross sectional US portfolios returns through a single US Security Dealers and Brokers
Leverage Factor model proposed by Adrian, Etula and Muir (2010, 2015).
Based on the global nature of US Security Dealers and Brokers, we argue that
the US Security Dealers and Brokers Leverage Factor is also useful in explaining the
cross section of Global Portfolios, following an intermediary financial Stochastic
Discount Factor framework as in Adrian, Etula and Muir (2010,2015). Using different
combinations of US and International Stocks Portfolios sorted by Size, Book to Market
and Momentum, as well as different maturities for Bonds Portfolios, we show that a US
2-factor model with the US Security Dealers and Brokers Leverage and the US Excess
Market Return as factors, is the most stable and robust among all models tested, like
Adrian, Etula and Muir(2010,2015) single leverage model, International 2-Factors
Model1 and both International and US Fama French 3 Factors Model.
Although no model can consistently price the cross-section of US or
International excess returns, we find that adding International portfolios to the analysis
1 International 2-Factors are US Security Dealers and Brokers Leverage Factor and International Excess
Market Return. In the absence of Global Leverage data, USs may be seen as a proxy.
does improve our cross-sectional results as pricing errors become gerenally smaller and
prices of risk become consistent with theory. The US 2-Factor Models present the most
significant improvements, even when challenging Momentum Portfolios are added.
While the addition of Global portfolios lead to the most noteworthy improvements, the
model is also robust to the inclusion of European-only or even UK-only portfolios.
While our goal is to show that the leverage factor is priced in the cross-section of
Global portfolios, a minor contribution to the literature is to address the robustness of
Adrian, Etula and Muir(2010,2015) single leverage factor model to changes in
definition of leverage and to sample period. On the June 5, 2014 publication of the
Flow of Funds, there was an important change on one of the variables that were used to
construct the leverage factor in Adrian, Etula and Muir(2010,2015). Before, the
Security Repurchase Agreements appeared only as a net variable on the liabilities
section, while on the updated version it appears both on the assets and liabilities. Table
1 shows the implications of this change on the construction of the Leverage in both
scenarios for the fourth quarter of 2013.
The change, in absolute terms, is non-negligible. Unfortunately, because of data
availability constraints, theres no way to fully replicate the old methodology to look for
possible implications on the dynamics of the transformation. However, in order to
investigate imaginable problems arising, the old data is artificially reconstructed
subtracting the security REPO from both the total financial assets and total liabilities,
recreating the net variable on the liabilities side. Intuitively, theres no economic
explanation for the use of the old variable, nonetheless the following analyses will
report both versions. Results report a considerable sensibility towards this methodology
change. In addition, results show that when we consider the 1981-2014 or 1991-2014
windows, where the latter is the same as the used on global portfolios, the single
leverage factor models performance is considerably worse, indicating a lack of
robustness to shorter time periods. Because data is quarterly, this can be attributed to the
lack of betas variability, which may prevent a precise estimate of leverage price of risk.
2. Related Literature
Leverage started to gain attention primarily after the Great Recession, even
though leverage cycle theory existed prior to the crisis, as seen in Geanokoplos
(1997,2003) and Fostel and Geanokoplos (2008). Contrarily to standard economic
theory, the Leverage Cycle Theory suggests that asset prices and consequently the real
economic activity, are driven not by interest rate, but by endogenous leverage and
volatility, through the role of collateral. Additionally, as presented in Fostel and
Geanokoplos (2013), the cycle component of this theory focus on how low volatility
and safe economic perspective can lead the real economic to a boom and a highly
leveraged economy, as well as a confluence of factors, primarily a bad news leading to
increased volatility and uncertainty can lead to credit tightening and a severe
deleverage, and thus, a market crash.
Empirically, Adrian, Moench and Shin (2010) documented the high pro-
cyclicality of the leverage cycle, where low asset prices are associated to an also low
Leverage, and proposed that this cyclical characteristic may be due to regulatory or
other constraints, such as Value at Risk limits. Another approach to rationalize the
stylized fact emphasizes how personal experience of agents can shape risk preferences,
as theoretically proposed by Krishnamuty (2009) and empirically suggested by
Malmendier and Nagel (2011) that look to the role of the Great Depression on
individuals willingness to invest in equities, and Koudijs and Both (2014) that
investigate the collateralized Amsterdam loan market in the 18th
century.
Given the relevance of financial intermediary firms, Adrian, Etula and Muir
(2010, 2015) suggest a shift on the traditional asset pricing theory emphasis on
household Stochastic Discount Factor (SDE) to an intermediary SDE. They argue that
the assumptions of a household SDE are easily violated, like full market participation of
all households, absence of transaction costs, and the need of continuous optimized
expectations, while the financial intermediary firms fit the hypothesis more naturally.
Furthermore, they construct a single factor model based on a non-seasonal log
innovations of US Security Dealers and Brokers Leverage that outperforms US Fama
French 3-factor and CAPM models on the 1968-2009 time window. Adrian, Moench
and Shin (2014) also extends these results to a dynamic framework using the Dynamic
Asset Pricing Model (Adrian, Crump and Moench, 2014) that allows for time-varying
price of risk and time-varying betas.
Nonetheless, US security dealers and brokers are genuine global companies,
incorporating shocks from all parts of the world through its subsidiaries, or foreign
holders. Since there is no availability of data for security dealers and brokers from
others countries, it is impossible to calculate a true global leverage factor.
The US Leverage Factor has been used inthe literature as a proxy for the
unavailable global variable. Stressing the global nature of the financial system and the
leverage cycles, Bruno and Shin (2014a) propose a global banking model, where
regional banks borrow in US dollars from global banks and then lend to local corporate
owners, emphasizing that the global intermediary financial system is responsible for the
aggregate credit risk, and consequently, global liquidity. Testing theory predictions,
they also construct a panel including 46 countries using global factors, as the US
Security Dealers and Brokers leverage as a proxy, to support their predictions.
Credit conditions are also determined by global factors in Bruno and Shin
(2014b), that also synchronizes the corporate risk-taking of different regions and
sectors. Empirically, they show that US Security Dealers and Brokers as a proxy for
Global Liquidity is indeed a relevant global risk-taking factor. Bruno and Shin (2014c)
also present the monetary policy risk-taking channel of capital flows, where US
monetary policy is relevant to both international banks leverage and cross-borders
capital flows.
Finally, the Global Asset Pricing literature so far has struggled to propose a
global-factors only model, and Lewis (2011), explore possible explanations that make
this a hard task to be accomplished, highlighting the domestic nature of monetary and
fiscal policies and the home equity bias, as well as the exchange risk included on global
portfolios. However, Lewis points out that a world CAPM still is the most common
benchmark when looking to Global Portfolios.
3.Data
The US Security Dealers and Brokers Leverage is defined as:
Both Total Financial Assets and Total Liabilities are from the L.128 Table from
Flow of Funds Report, now Z1 Release, as December 11, 2014. Data is quarterly. To
approximate the old methodology, the approach is to recreate the NET Repo Agreement
variable on the liabilities side. Precisely, Old Leverage is defined as:
To construct the Leverage Factor, we first take the Leverage Log Innovation,
i.e., log(Leverage), and make it seasonally adjusted using expanding windows starting
in 19652. Following Adrian, Etula and Shin(2010,2015), as the data before mid-60s
doesnt seem reliable because of negative or too low leverage, the time series of the
factor will start at 1968.
The Portfolios used for the US analysis are the 25 Size-Book to Market and 10
Momentum from Frenchs library. US Treasury Bonds Portfolios are sorted by
maturities, 0-6 months, 12-18months, 24-30 months, 36-42 months, 48-54 months and
5-10 years. The data is from CRSP.
International Portfolios, both 25-Size Book to Market and 25-Size Momentum,
are constructed using data from 22 Developed Countries excluding the US, and are
available only starting at 1991. European 25-Size Book to Market and 25-Size
Momentum are from 16 countries and available at the same source. Finally, UK 25
2 Following the same methodology, the season adjustment is proceeded using expanding windows, i.e.,
using all the data up to date, starting three years before the initial starting date (1968). Therefore, through
the regressions below, the leverage factor is adjusted using data starting in 1965.
Size-Book to Market are from Xfi Centre For Finance and Investment by University of
Exeter and are available since 1981.
When we refer to Global portfolios, both International and US-only portfolios
are being simultaneously used, unless further noticed. In only one situation another set
of Global portfolios is used, which are constructed very similarly of International
portfolios, but also includes US portfolios. This data is also available at Frenchs
library.
For the excess return calculations, all Portfolios above use the 3-Month T-Bill as
the risk-free rate, with the exception to the UKs, that use the Risk-Free rate from
Exeter database. As the leverage is only available at a quarterly frequency, all excess
returns are time-aggregated into quarterly excess returns, and then multiplied by four to
transform them in annual excess returns.
Three different sets of factors are used. US, Global3 and European Fama-French
Factors are from Frenchs library, while UKs Factors are from Exeter Univeristy
library.
For USs Portfolios, four different dates combinations will be used to allow us
compare the modes performance through time: 1968-2009, the time window used by
Adrian, Etula, Shin (2010,2015), 1968-2014, 1981-2014 and 1991-2014. For UKs
Portfolios, only 1981-2014 and 1991-2014, while for the others, 1991-2014.
US 25, International 25, Europe 25 and UK 25 portfolios are sorted by size-book
to market only, while US 31 and 35 include, respectively, 6 Bonds and 10 Momentum
portfolios. US 41 includes both of them and International, Europe and UK 50 Portfolios
both 25 size-book to market and 25 size-momentum portfolios.
4. Methodology
For all different portfolios, i=1,,N, each factors beta is estimated through
conventional time-series regressions, i.e.,
Ri,te = ai + i,f ft + ei,t , t=1,...,T.
3 The Global Factors, differently from International Portfolios, take the United States into account.
Then, as usual, the factors price of risk, f, is calculated through the cross
section of Portfolios Expected Returns, where, vi + c is the pricing error.
E[Ri,te] = c + i,f f + vi , i=1,...,N
Good model properties are a small intercept, as well as stable and economically
significant prices of risk. Although the conventional t-statistic is presented (in italic), it
is prudent to take into consideration that the betas are estimated and thus already have
own estimation errors, hence we also report the t-statistic correction as proposed by
Shanken (1992). We also hope for a small Mean Absolute Pricing Error (MAPE), i.e.,
, and non-rejection of the null hypothesis that all pricing errors are
jointly equals to zero, using the Gibbons-Ross-Shanken (1989), GRS statistic.
5. Results
Since Adrian, Etula and Muir (2010,2015) proposed the Leverage Factor, the
definition of the variables used in its construction has changed. As one of ours main
goals is to compare models performance in the pricing of International portfolios, first
we look at the US-only case, taking a closer attention to the robustness over the
methodology change and to changes in the sample period as International data is
considerably shorter. Tables 2-4 provide the comparison between old and new Leverage
Factor in four different time windows, 1968-2009, 1968-2014, 1981-2014 and 1991-
2014. We also compared their performance to an alternative US 2-factor that also
includes the market return as a factor, and to the US Fama French 3-factor models, . We
test these model using US 25, US 31 and US 41 Portfolios.
Next, we take a Global perspective, pricing simultaneously US and International
Portfolios4. In addition to using International portfolios, we also test the effect of adding
European-only and UK-only portfolios for robustness.. Tables 5-9 present the
comparison between a range of models (Leverage Single Factor, US 2-Factors, US
Fama French 3-factor, Global 2-factor and Global 3-factor) using different portfolio
combinations: US 25 + International 25, International 25, US 31 + Interntional 25, US
4 Data for 25 Size-Book to Market and 25 Size-Momentum sorted portfolios are also available at Frenchs
library. However, due to a small time frame, we prefer to include International portfolios to estimate
leverage price of risk more precisely.
35 + International 50 and US 41 + International 50.CAPM performs considerable
worse, so results are not reported.
We also show, a horse race between the 2-US Factor and 4-US Factor, which is
a combination of the leverage factor and the three factors on Fama French model, on
table 10, and the use of a different global portfolios set on table 11 to assess robustness.
5.1 US-only Portfolios
First, we address the possible problems concerning the methodology change of
the leverage calculation, as well as the model performance over shorter time periods.
The results shown in tables 2-4 imply that the old leverage calculation methodology
performs considerably better than the new-definition leverage. Nonetheless, there are
some shortcomings on the older methodology, as Both GRS statistic and pricing errors
increase considerably on shorter time-windows. For example, when all 41 US
portfolios are analyzed, the intercept goes from 0.36 on the 1968-2009 time frame to
7.97 on 1991-2014. This pattern can be seen in all other models, but the increase is not
nearly as steep as for the old Leverage 1-factor model. Also, both new and old leverage
price of risk remains stable through different portfolios sets, time-frames and
methodologies approach. When we add the 6 bonds or 10 momentum portfolios,
however, US 2-factor outperform both old and new leverage 1-factor, presenting
smaller and non-significant intercepts, at the 1.5-3.25 range, while maintaining the price
of risk stability for both leverage and market.
Finally, while GRS tests reject all the tested models, the relevant leverage price
of risk in all the models, as well as higher than 60% Adjusted R2 estimates, provide
some evidence on the role of the US Security Dealers and Brokers on the cross section
of asset markets.
5. 2 International and Global Portfolios Pricing
In this section, we test our main hypothesis that the US leverage factor can price
the cross-section of global portfolios, i.e., international and US portfolios. We test the
conjecture that the addition of international portfolios can improve the overall
performance of the model due to a more precise estimate of the price of leverage risk,
relying on the hypothesis that the US leverage factor is a good proxy for an
unobservable global leverage factor. Overall, tables 5-9 show that the inclusion of
international portfolios turn all models better, but mainly US 2-factor and US FF.
The inclusion of only 25 International portfolios on US original 25 portfolios is
already enough to improve the results, as seen in table 5. Our US 2-factor model, for
example, presents a smaller intercept of 2.61, against a 9.11 with US-only 25 portfolios,
while maintaining relevant and positive leverage and market prices of risk. However
US Fama-French model intercept also declines from 10.18, when US-only portfolios are
used, to 0.24, when international and US portfolios are priced simultaneously. Although
all models present better economic and statistical properties, the steepest improvements
happen with the US 2-factors and US FFs models. We also show on table 6 that models
also improve from International-only portfolios when US portfolios are included.
However, the US Fama-French model is not as robust as the US 2-factor model
to the inclusion of different types of portfolios. Although adding bonds portfolios seems
make all models better, US 2-factor presents considerable improvements when
compared to US Fama-French model. US 2-factor is the only model to present non-
significant intercepts, and present stable coefficients when comparing the US-only 25 to
US 25 + International 25 portfolios, which is a highly desirable economic feature. We
also show on table 8, when momentum portfolios are included, and on table 9, when
both momentum and bond portfolios are added, that the US-factor clearly presents the
best results. While US Fama-French model presents a high intercept of 7.88 and a
negative price of risk on the first case, US 2-factor model presents a sign of market
price of risk that is consistent with economic theory, whereas maintaining a stable
leverage price of risk and low intercept.
GRS tests interpretation does not permit us to statistically reject the null
hypothesis of jointly zero jointly prices errors, however, US 2-factor model intercepts
are all between 2.49 and 4.67 and are all insignificant., which is an unique feature
among all models. Also, from the economic theory stand point, the fact that leverage
price of risk always lies between 67-73 and market price of risk between 3.17-5.36,
suggest an interesting stability over different cross-sectional results. Both statistical and
economic properties across portfolios can be visualized in the figures 1-5.
When we add Europe and UK-only portfolios, as seen in tables 5-9, results
suggests the US 2-factor as the most consistent model, albeit not performing well in
some cases, as we can see on the significant intercept on UK 25 + US 41 and Europe 50
+ US 35 besides a negative market price of risk on Europe 50 + US 35. Also, Tables 5
and 6 also provide evidence on the improvements when US 25 Portfolios are added to
Europe or UK 25 portfolios. US 2-Factors present significant 16.22 and 8.96 intercepts
on Europe-only and UK-only 25, correspondingly, while when including US 25, turn to
respectively a not significant 5.35 and 6.88.
On a horse race between US 2-factor, US 4-factor and Global 4-factor models,
table 10 shows that the results to US models are similar, and considerably superior to
the Global model. Indeed, US 4-factor model also present a similar low intercept, but
also shows a higher adjusted R2. However, this model lack robustness when we include
of momentum portfolios. Thus, as US 2-factor is a more parsimoniously model,
presenting generally better results, we do not find added to value to the extra factors
considered on the 4-factor model.
Finally, we assess the performance of US-2 factor model when US portfolios are
already used on the construction of Global portfolios. The main shortcoming to this
approach is the potential lack of variability that can hurt the estimated betas precision.
Nonetheless, table 11 suggest that although US 2-factor model maintains the relative
best economic and statistical properties, the performance with Global 25 portfolios and
Global 50 portfolios are much worse than the benchmark case.
6. Conclusion
We show that US 2-factor model can consistently price cross-sectional returns of
global portfolios, presenting small pricing errors and prices of risk compatible to
economic theory. The results are also considerable stable across different sets of
portfolios, including momentum and bonds. Also, this is the only model that present
non-significant intercepts on all different global portfolios combinations.
Other robustness tests include the addition of other non-US portfolios, as
European and UKs, a horse race with 4-factor model, and a different construction of
global portfolios. In all these scenarios, US 2-factor perform substantially better than
single factor leverage, or global and US Fama-French models.
One question that emerges when looking to these results why a Global 2-Factor
model doesnt seem to be the most consistent, if indeed this economic story of a
relevant intermediary financial system is true. One speculative answer is that financial
intermediary firms base their decision on a US framework, since most of them are from
there, or because its the most important benchmark/market available. Another
speculative answer is that this only happened due to the absence of a true Global
Leverage Factor and if we could use this variable along with the Global Market Excess
Return, the results could be improved. One last explanation draws the attention to the
Risk-Free rate, which even for Global and European Portfolios, is a common 3 month
T-Bill. Perhaps a better proxy for a global risk-free rate could lead to significantly
different results.
7. Appendix: tables and figures
Figure 1 Actual vs predicted results from US 2-factor for US 25
Figures 2-3 : Actual vs predicted results from US 2-factor for US 25 + International 25
and US 31 + International 25
Figures 4- 5: Actual vs predicted results from US 2-factor for US 35 + International 50
and US 41 + International 50
Table 1: US security dealers and brokers leverage using September 18 and March 6
versions of Flow of Funds for the fourth quarter of 2013
4Q 2013 L.128 as of September 18 (new)
total financial assets 3,408,371 total liabilities 3,324,558
checkable deposits and currency 111,720 security repurchase agreements 1,768,587
security repurchase agreements 1,320,966 credit market instruments 112,398
credit market instruments 476,894 corporate and foreign bonds 112,398
commercial paper 28,654 depository institution loans n.e.c. -
Treasury securities 136,043 trade payables 14,540
agency- and GSE-backed securities 114,164 security credit 897,052
municipal securities and loans; 18,637 Households and nonprofit organizations; security
credit; asset 815,484
corporate and foreign bonds 128,949 U.S.-chartered depository institutions; security credit;
asset 52,885
syndicated loans to nonfinancial corporate
business 50,447
Foreign banking offices in the U.S.; security credit;
asset 28,683
corporate equities 172,386 taxes payable 3,723
security credit 339,184 foreign direct investment in U.S. 119,540
U.S. direct investment abroad 225,786 total miscellaneous liabilities 408,718
total miscellaneous assets 761,435 equity investment by parent companies 1,671,859
unidentified miscellaneous liabilities -1,263,141
Leverage = 40.7
4Q 2013 L.128 as of March 6 (old)
total financial assets 2,087,400 total liabilities 1,992,300
checkable deposits and currency 111,700 security repurchase agreements (net) 135,300
security repurchase agreements - credit market instruments 112,400
credit market instruments 476,900 corporate and foreign bonds 122,025
open market paper 28,700 depository institution loans n.e.c. 112,400
Treasury securities 136,000 trade payables 14,500
agency- and GSE-backed securities 114,200 security credit 1,209,300
municipal securities and loans; 18,600 Customer credit balances 833,800
corporate and foreign bonds 133,100 U.S.-chartered depository institutions; security credit;
asset 187,100
others loans and advances 46,300 Foreign banking offices in the U.S.; security credit;
asset 188,400
corporate equities 172,400 taxes payable 3,700
security credit 339,200 foreign direct investment in U.S. 108,300
total miscellaneous liabilities 517,000
total miscellaneous assets 987,200 equity investment by parent companies 1,659,100
unidentified miscellaneous liabilities -1,250,400
Leverage = 22
Table 2. US 25: On this table we present the cross-sectional results for US single leverage based on old and new methodologies (Lev and Lev_old, respectively), US 2-factor, US Fama-French models,, where Lev and Lev_old
are respective, new and old leverage factors, for 25 US Size-bookt to market sorted portfolios.We present intercept,
leverage factor (LevFac) and the standard 3-factors from Fama French models coefficients, along with the respectives
t-statistics, below coefficients. T-shanken statistics are reported beneath standard t-statistics. We also report adjusted
R2, adjR2, Mean Asset Pricing Error (MAPE) and Gibbons, Ross, Shanken statistic, along with its respective p-value.
Periods analysed are 1968-2009, 1968-2014, 1981-2014 and 1991-2014.
Lev Lev_old US 2-
Factor US FF Lev Lev_old
US 2-
Factor US FF
1968-2009 1968-2014
Intercept 3.12 0.71 5.18 13.09 4.67 2.15 6.68 13.74
4.31 0.71 2.6 2.45 6.83 2.13 3.28 2.46
0.72 0.13 1 3.02 1.19 0.45 1.36 3.2
LevFac 47.39 61.67 45.88
42.47 55.22 41.2
7.26 7.5 6.91
6.66 6.84 6.36
2.59 2.39 2.5
2.6 2.41 2.51
Market-
RF 0.49 -7.81
0.11 -7.28
0.3 -1.48
0.06 -1.32
0.08 -1.52
0.02 -1.45
SMB
1.98
1.73
3.22
2.79
1.1
1.04
HML
5.5
4.94
7.16
6.41
2.77 2.73
Adj R2 0.68 0.7 0.69 0.71 0.64 0.66 0.65 0.66
MAPE 3.12 1.20 5.18 13.09 4.67 2.18 6.68 13.74
GRS 44.18 20.79 43.82 54.35 27.26 23.02 53.11 64.42
1% 65% 0% 0% 29% 52% 0% 0%
1981-2014 1991-2014
Intercept 8.71 5.85 11.292 22.62 10.35 9.78 9.11 18.79
19.17 6.62 3.51 3.86 20.98 15.83 2.9 4.44
2.07 1.1 1.9 4.72 2.26 2.05 1.42 4.02
LevFac 43.82 56.58 38.84
38.17 38.82 40.63
5.16 4.97 3.69
3.98 3.58 3.52
2.08 1.85 1.89
1.42 1.33 1.61
Market-
RF -2.58 -14.68
0.89 -10.18
-0.91 -2.56
0.32 -2.46
-0.38 -2.64
0.12 -1.76
SMB
0.78
2.42
1.04
3.42
0.46
1.16
HML
4.85
3.67
5.17
4.15
2.25 1.37
Adj R2 0.52 0.5 0.51 0.59 0.38 0.33 0.36 0.6
MAPE 8.71 5.85 11.29 22.62 10.35 9.78 9.11 18.79
GRS 49.06 41.74 67.62 68.76 56.75 56.13 52.69 54.95
0% 1% 0% 0% 0% 0% 0% 0%
Table 3. US 31: On this table we present the cross-sectional results for US single leverage based on old and new methodologies (Lev and Lev_old, respectively), US 2-factor, US Fama-French models, where Lev and Lev_old
are respective, new and old leverage factors, for 25 US Size-bookt to market sorted portfolios and 6 bond portfolios
sorted by maturity. We present intercept, leverage factor (LevFac) and the standard 3-factors from Fama French
models coefficients, along with the respective t-statistics, located below coefficients. T-shanken statistics are
reported beneath standard t-statistics. We also report adjusted R2, adjR2, Mean Asset Pricing Error (MAPE) and
Gibbons, Ross, Shanken statistic, along with its respective p-value. Periods analyzed are 1968-2009, 1968-2014,
1981-2014 and 1991-2014.
Lev Lev_old US 2-
Factor US FF Lev Lev_old
US 2-
Factor US FF
1968-2009 1968-2014
Intercept 2.04 0.59 1.50 1.49 2.84 1.06 1.65 1.58
4.10 1.11 2.42 2.28 5.05 1.94 2.57 2.44
0.92 0.34 1.40 2.18 1.30 0.67 1.73 2.55
LevFac 55.92 62.62 51.85
57.24 63.40 48.37
11.20 12.89 9.10
9.80 13.07 8.05
2.36 2.18 2.87
2.48 2.30 2.91
Market-
RF 3.49 3.59
4.25 4.64
5.79 4.26
6.74 5.54
1.07 1.25
1.40 1.73
SMB
2.04
1.84
3.37
3.02
1.13
1.11
HML
5.74
5.18
7.64
6.84
2.89 2.86
Adj R2 0.81 0.85 0.81 0.82 0.76 0.66 0.81 0.83
MAPE 2.19 1.06 1.70 1.64 2.92 1.34 1.81 1.69
GRS 49.91 25.67 52.10 77.37 2.92 25.84 62.43 89.79
1% 69% 1% 0% 0% 68% 0% 0%
1981-2014 1991-2014
Intercept 7.27 3.65 2.94 2.93 8.38 7.38 3.00 3.06
11.59 4.79 3.43 3.41 10.83 8.76 3.62 3.82
2.05 1.11 3.07 4.64 2.17 1.89 3.80 5.70
LevFac 54.35 80.35 55.62
55.70 66.20 53.67
4.17 7.40 6.24
3.32 4.03 3.62
2.48 2.31 2.49
1.90 1.94 1.85
Market-
RF 4.69 4.49
6.17 5.07
5.42 3.96
7.31 4.71
1.37 1.48
1.58 1.44
SMB
1.23
2.71
1.46
3.19
0.73
1.31
HML
5.46
4.87
5.21
4.89
2.53 1.80
Adj R2 0.35 0.64 0.70 0.71 0.25 0.34 0.75 0.78
MAPE 7.27 3.73 3.04 2.99 8.38 7.38 3.08 3.09
GRS 113.98 54.49 106.90 172.34 109.72 94.49 106.98 158.99
0% 0% 0% 0% 0% 0% 0% 0%
Table 4. US 35: On this table we present the cross-sectional results for US single leverage based on old and new methodologies (Lev and Lev_old, respectively), US 2-factor, US Fama-French models, for 25 US Size-book to
market sorted portfolios and 6 bond portfolios sorted by maturity and 10 momentum sorted portfolios. We present
intercept, leverage factor (LevFac) and the standard 3-factors from Fama French models coefficients, along with the
respective t-statistics, located below coefficients. T-shanken statistics are reported beneath standard t-statistics. We
also report adjusted R2, adjR2, Mean Asset Pricing Error (MAPE) and Gibbons, Ross, Shanken statistic, along with its
respective p-value. Periods analyzed are 1968-2009, 1968-2014, 1981-2014 and 1991-2014.
Lev Lev_old US 2-
Factor US FF Lev Lev_old
US 2-
Factor US FF
1968-2009 1968-2014
Intercept 2.19 0.36 1.9 2.47 3.2 0.99 2.07 2.46
3.41 0.5 2.05 1.96 5 1.42 2.27 2.07
0.83 0.16 1.55 3.38 1.23 0.48 1.9 3.73
LevFac 53.83 63.64 52.46
53.99 63.69 49.1
7.68 9.19 6.81
7.47 9.74 6.44
2.63 2.56 3.18
2.8 2.67 3.26
Market-
RF 2.97 2.62
3.75 3.72
3.38 1.77
4.33 2.67
0.9 0.91
1.23 1.39
SMB
1.6
1.45
1.52
1.44
0.88
0.87
HML
4.6
4.26
3.34
3.21
2.3 2.34
Adj R2 0.59 0.68 0.58 0.34 0.58 0.7 0.6 0.4
MAPE 2.61 1.36 2.37 2.79 3.49 1.62 2.47 2.74
GRS 85.82 43.81 86.37 144.87 90.20 40.60 95.47 153.11
0% 31% 0% 0% 0% 44% 0% 0%
1981-2014 1991-2014
Intercept 7.55 4.38 3.07 3.65 8.81 7.97 3.25 4.04
15.33 7.06 3.51 3.17 14.91 12.35 3.78 3.93
2.1 1.14 2.79 5.19 2.3 1.98 3.38 5.92
LevFac 49.43 69.75 56.97
43.54 52.07 53.48
4.68 7.66 7.13
3.35 4.03 6.18
2.69 3.3 2.97
2.3 2.16 2.21
Market-
RF 4.54 3.84
5.84 4.12
5.27 2.81
6.93 3.38
1.31 1.26
1.49 1.16
SMB
0.82
2.49
0.82
2.57
0.48
1.2
HML
4.43
4.04
3.37
3.31
2.04 1.49
Adj R2 0.34 0.59 0.63 0.4 0.2 0.28 0.66 0.53
MAPE 7.55 4.49 3.29 3.79 8.81 7.97 3.47 4.20
GRS 152.70 80.15 129.57 221.95 176.40 157.11 146.85 222.71
0% 0% 0% 0% 0% 0% 0% 0%
Table 5. US 25 + 25: On this table we present the cross-sectional results for US single leverage based on new methodology (Lev), US 2-factor, US Fama-French, Global 2-factor and Global Fama-French models, for 25 US
Size-book sorted portfolios along International, Europe or UK 25 Size-book to market portfolios. We present
intercept, leverage factor (LevFac) and the standard 3-factors from Fama French models coefficients, along with the
respective t-statistics, located below coefficients. T-shanken statistics are reported beneath standard t-statistics. We
also report adjusted R2, adjR2, Mean Asset Pricing Error (MAPE) and Gibbons, Ross, Shanken statistic, along with its
respective p-value. Periods is 1991-2014.
Lev US 2-
Factor
Global
2-
Factor
US FF Global
FF Lev
US 2-
Factor
Global
2-
Factor
US FF Global
FF
US 25 US 25 + International 25
Intercept 10.35 9.11 8.99 18.79 17.40 8.00 2.61 8.20 0.24 11.08
20.98 2.90 2.81 4.44 3.73 20.49 1.43 2.49 0.10 2.04
2.26 1.42 1.45 4.02 3.91 1.53 0.28 1.12 0.03 3.64
LevFac 38.17 40.63 40.48
69.98 73.76 69.64
3.98 3.52 3.63
7.03 7.94 6.06
1.42 1.61 1.61
1.94 1.89 1.87
Market-
RF 0.89 0.72 -10.18 -8.14
5.24 -1.19 5.36 -3.01
0.32 0.24 -2.46 -1.69
2.79 -0.39 1.97 -0.56
0.12 0.09 -1.76 -1.42
0.60 -0.15 0.68 -0.64
SMB
2.42 3.63
4.23 -0.37
3.42 3.71
4.74 -0.36
1.16 1.68
1.88 -0.23
HML
3.67 3.41
6.86 3.58
4.15 4.02
6.56 2.78
1.37 1.39 2.32 1.59
Adj R2 0.38 0.36 0.36 0.60 0.55 0.52 0.57 0.49 0.58 0.14
MAPE 10.35 9.11 8.99 18.79 17.40 8.00 2.95 8.20 1.85 11.08
GRS 56.75 52.69 53.21 54.95 55.58 105.06 98.11 105.36 181.31 203.12
0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
US 25 + Europe 25 US 25 + UK 25
Intercept 8.83 5.35 12.15 8.09 19.46 9.33 6.88 6.95 5.34 4.74
25.43 2.51 4.23 2.42 5.53 26.00 1.21 3.22 2.22 1.68
1.82 0.54 2.15 1.51 2.67 2.03 1.21 1.46 1.32 1.42
LevFac 54.07 58.52 48.89
46.12 48.42 48.00
6.40 6.70 5.13
5.60 5.78 5.73
1.95 1.84 1.93
1.75 1.87 1.88
Market-
RF 3.14 -3.82 -1.78 -11.14
2.17 1.70 1.66 4.28
1.51 -1.46 -0.54 -3.29
1.14 0.80 0.66 1.42
0.34 -0.57 -0.34 -1.44
0.36 0.27 0.34 0.85
SMB
3.93 2.17
3.15 0.62
5.65 2.53
3.58 0.71
1.54 1.24
1.47 0.32
HML
5.12 3.26
4.69 3.36
5.58 4.00
5.25 4.41
1.84 1.37 1.58 1.41
Adj R2 0.45 0.47 0.45 0.55 0.35 0.38 0.39 0.39 0.43 0.28
MAPE 8.83 5.35 12.15 8.09 191.04 9.33 6.88 6.95 5.34 4.75
GRS 139.62 130.18 149.32 201.41 19.46 123.32 114.56 116.38 155.45 164.72
Table 6. US/International/Europe/UK-only 25: On this table we present the cross-sectional results for US single leverage based on new methodology (Lev), US 2-factor, US Fama-French, Global 2-factor and Global
Fama-French models, for 25 US/International/Europe/UK-only portfolios sorted by size-book to market. We present
intercept, leverage factor (LevFac) and the standard 3-factors from Fama French models coefficients, along with the
respective t-statistics, located below coefficients. T-shanken statistics are reported beneath standard t-statistics. We
also report adjusted R2, adjR2, Mean Asset Pricing Error (MAPE) and Gibbons, Ross, Shanken statistic, along with its
respective p-value. Periods is 1991-2014.
Lev US 2-
Factor
Global
2-Factor US FF
Global
FF Lev
US 2-
Factor
Global
2-Factor US FF
Global
FF
US 25 International 25
Intercept 10.35 9.11 8.99 18.79 17.40 6.73 11.79 13.29 6.96 6.04
20.98 2.90 2.81 4.44 3.73 17.07 4.31 3.65 2.16 1.46
2.26 1.42 1.45 4.02 3.91 1.00 1.73 1.59 1.52 1.20
LevFac 38.17 40.63 40.48
97.80 72.81 80.70
3.98 3.52 3.63
5.20 3.26 3.98
1.42 1.61 1.61
1.46 1.60 1.59
Market-
RF 0.89 0.72 -10.18 -8.14
-6.72 -7.62 -3.48 -2.07
0.32 0.24 -2.46 -1.69
-1.88 -2.21 -1.03 -0.56
0.12 0.09 -1.76 -1.42
-0.93 -0.94 -0.61 -0.36
SMB
2.42 3.63
0.42 0.88
3.42 3.71
0.18 1.13
1.16 1.68
0.08 0.52
HML
3.67 3.41
9.05 5.73
4.15 4.02
5.35 6.04
1.37 1.39 2.33 2.42
Adj R2 0.38 0.36 0.36 0.60 0.55 0.52 0.57 0.56 0.63 0.63
MAPE 10.35 9.11 8.99 18.79 17.40 6.73 11.79 13.29 6.96 6.04
GRS 56.75 52.69 53.21 54.95 55.58 19.98 28.03 25.04 48.86 51.71
0.00 0.00 0.00 0.00 0.00 0.70 0.26 0.40 0.00 0.00
Europe 25 UK 25
Intercept 7.72 16.22 18.26 9.76 9.38 8.49 8.96 6.32 5.10 3.92
19.57 4.41 4.18 4.39 3.40 16.74 2.95 2.06 1.66 1.43
1.44 2.57 2.30 2.08 1.91 1.79 1.57 1.16 1.15 0.93
LevFac 54.16 34.73 49.80
39.43 39.08 38.44
3.72 2.21 3.73
2.65 2.54 2.54
1.46 1.10 1.38
1.08 1.09 1.05
Market-
RF -9.48 -10.39 -4.92 -3.06
-0.73 1.65 0.58 2.40
-2.40 -2.61 -2.26 -1.24
-0.23 0.53 0.19 0.79
-1.36 -1.31 -0.86 -0.54
-0.10 0.24 0.10 0.41
SMB
-5.29 -0.91
3.93 2.50
-3.74 -1.68
1.44 1.86
-0.95 -0.46
0.87 1.07
HML
9.20 5.54
4.13 2.48
11.52 10.65
2.73 2.73
2.15 2.22 0.93 0.86
Adj R2 0.35 0.45 0.46 0.86 0.82 0.20 0.17 0.18 0.30 0.39
MAPE 7.72 16.22 18.26 9.76 9.38 8.49 8.96 6.32 5.17 4.05
GRS 25.01 30.50 25.12 21.54 32.86 22.71 21.10 22.10 29.53 31.88
41% 17% 40% 0% 11% 54% 63% 51% 20% 0%
Table 7. US 31 + 25: On this table we present the cross-sectional results for US single leverage based on new methodology (Lev), US 2-factor, US Fama-French, Global 2-factor and Global Fama-French models, for 25 US
Size-book sorted portfolios along 6 bonds portfolios sorted by maturity and International, Europe or UK 25 Size-book
to market portfolios. We present intercept, leverage factor (LevFac) and the standard 3-factors from Fama French
models coefficients, along with the respective t-statistics, located below coefficients. T-shanken statistics are
reported beneath standard t-statistics. We also report adjusted R2, adjR2, Mean Asset Pricing Error (MAPE) and
Gibbons, Ross, Shanken statistic, along with its respective p-value. Periods is 1991-2014.
Lev US 2-
Factor
Global
2-
Factor
US FF Global
FF Lev
US 2-
Factor
Global
2-
Factor
US FF Global
FF
US 31 International 25 Size + US 31
Intercept 8.38 3.00 3.03 3.06 3.11 7.34 2.49 2.95 2.30 2.99
10.83 3.62 3.69 3.82 3.88 16.85 3.05 3.00 2.67 2.33
2.17 3.80 4.01 5.70 5.70 1.56 1.18 2.76 2.03 5.65
LevFac 55.70 53.67 51.60
72.32 73.75 78.59
3.32 3.62 5.36
6.16 8.34 7.88
1.90 1.85 1.80
1.99 1.99 2.06
Market-
RF 6.17 6.14 5.07 6.58
5.36 3.69 3.16 4.90
7.31 6.78 4.71 6.41
6.02 3.71 2.85 3.30
1.58 1.44 1.44 1.75
1.34 0.94 0.88 1.36
SMB
2.71 1.90
4.24 -0.51
3.19 2.05
4.91 -0.52
1.31 0.96
1.85 -0.31
HML
4.87 4.45
6.71 4.33
4.89 5.02
6.71 3.72
1.80 1.85 2.38 1.91
Adj R2 0.25 0.75 0.75 0.78 0.78 0.40 0.66 0.58 0.66 0.31
MAPE 8.38 3.08 3.11 3.09 3.13 7.35 2.82 3.35 2.72 3.77
GRS 109.72 106.98 111.65 158.99 158.74 155.74 148.06 139.61 282.59 296.12
0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Europe 25 + US 31 UK 25 + US 31
Intercept 8.07 2.93 3.36 2.94 3.84 8.51 3.36 3.27 2.98 2.88
18.73 3.63 3.67 3.69 3.49 18.86 3.98 3.81 3.58 3.12
1.83 1.76 3.73 5.32 5.50 2.05 2.78 3.40 4.65 5.11
LevFac 58.12 61.76 63.00
49.87 52.06 51.20
5.24 7.60 7.04
4.55 6.37 6.27
2.06 2.11 2.15
1.86 1.90 1.88
Market-
RF 5.47 4.13 3.23 3.71
5.55 5.24 4.05 6.23
6.52 4.57 3.53 3.07
6.31 5.81 3.86 5.43
1.40 1.07 0.85 1.03
1.40 1.24 1.10 1.65
SMB
4.04 1.40
3.23 0.46
5.90 1.44
3.80 0.56
1.62 0.85
1.53 0.24
HML
5.64 3.70
4.84 3.41
6.61 3.97
5.64 4.68
2.00 1.60 1.62 1.43
Adj R2 0.33 0.64 0.56 0.68 0.39 0.26 0.59 0.59 0.63 0.55
MAPE 8.07 3.04 3.52 3.11 4.10 8.51 3.53 3.42 3.19 3.24
GRS 224.28 206.01 203.44 340.76 356.38 318.36 299.39 302.32 443.45 442.39
0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Table 5. US 35 + 50: On this table we present the cross-sectional results for US single leverage based on new methodology (Lev), US 2-factor, US Fama-French, Global 2-factor and Global Fama-French models, for 25 US
Size-book sorted portfolios along 10 US portfolios sorted by maturity, International, Europe or UK 25 Size-book to
market portfolios and International, Europe Size-momentum portfolios. We present intercept, leverage factor
(LevFac) and the standard 3-factors from Fama French models coefficients, along with the respective t-statistics,
located below coefficients. T-shanken statistics are reported beneath standard t-statistics. We also report adjusted R2,
adjR2, Mean Asset Pricing Error (MAPE) and Gibbons, Ross, Shanken statistic, along with its respective p-value.
Periods is 1991-2014.
Lev US 2-
Factor
Global
2-
Factor
US FF Global
FF Lev
US 2-
Factor
Global
2-
Factor
US FF Global
FF
US 35 International 50 + US 35
Intercept 10.08 8.47 8.64 18.54 20.41 7.93 4.67 11.10 7.88 17.98
24.53 3.45 3.60 7.38 6.64 22.01 2.77 4.32 3.69 6.31
2.29 1.58 1.73 3.51 3.61 1.64 0.62 2.13 1.40 4.70
LevFac 36.33 40.29 39.79
61.45 67.36 53.01
4.35 3.91 3.92
6.02 6.44 4.34
1.57 1.66 1.64
2.02 1.83 1.65
Market-
RF 1.25 0.83 -9.81 -11.34
3.17 -3.88 -2.28 -9.86
0.57 0.38 -3.97 -3.49
1.78 -1.61 -0.96 -3.44
0.19 0.13 -1.53 -1.68
0.44 -0.61 -0.40 -1.88
SMB
2.62 4.02
3.74 -0.44
3.74 3.89
3.27 -0.55
1.26 1.68
1.74 -0.25
HML
2.73 2.26
3.80 0.64
3.01 2.58
2.71 0.57
0.99 0.92 1.28 0.27
Adj R2 0.34 0.33 0.33 0.50 0.39 0.30 0.32 0.30 0.16 0.13
MAPE 10.08 8.47 8.64 18.54 20.41 8.03 5.01 11.10 7.97 17.98
GRS 90.89 84.44 84.54 92.39 88.03 1038.44 934.47 1153.65 1709.91 1704.43
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Europe 50 + US 35 UK 25 + US 35
Intercept 8.87 9.64 15.58 21.61 24.02 9.38 7.07 7.25 9.15 8.78
23.77 4.18 5.84 8.46 9.36 28.84 4.11 4.00 4.37 3.68
1.89 1.26 3.07 3.95 4.74 2.13 1.66 1.88 2.88 2.90
LevFac 48.20 46.36 31.77
41.24 45.17 44.55
4.93 4.13 2.77
5.52 5.68 5.61
2.15 1.64 1.42
1.81 1.99 1.99
Market-
RF -1.04 -6.77 -13.90 -15.03
2.03 1.49 -1.62 0.24
-0.45 -2.82 -5.44 -5.97
1.22 0.85 -0.76 0.10
-0.14 -1.07 -2.29 -2.50
0.38 0.26 -0.36 0.05
SMB
3.28 2.26
2.48 0.56
3.50 2.51
2.90 0.68
1.33 1.35
1.18 0.29
HML
0.81 1.06
3.73 2.77
0.69 1.19
4.00 3.59
0.26 0.43 1.26 1.17
Adj R2 0.22 0.21 0.27 0.30 0.28 0.33 0.34 0.34 0.27 0.15
MAPE 8.99 9.72 15.58 21.61 24.02 9.38 7.07 7.25 9.15 8.78
GRS 1323.52 1353.77 1581.04 1520.13 1572.83 204.80 188.72 191.97 254.64 270.19
0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Table 9. US 41 + 50: On this table we present the cross-sectional results for US single leverage based on new methodology (Lev), US 2-factor, US Fama-French, Global 2-factor and Global Fama-French models, for 25 US
Size-book sorted portfolios along 10 US portfolios sorted by maturity, 6 bond maturity sorted portfolios,
International, Europe or UK 25 Size-book to market portfolios and International, Europe Size-momentum portfolios.
We present intercept, leverage factor (LevFac) and the standard 3-factors from Fama French models coefficients,
along with the respective t-statistics, located below coefficients. T-shanken statistics are reported beneath standard t-
statistics. We also report adjusted R2, adjR2, Mean Asset Pricing Error (MAPE) and Gibbons, Ross, Shanken statistic,
along with its respective p-value. Periods is 1991-2014.
Lev US 2-
Factor
Global
2-Factor US FF
Global
FF Lev
US 2-
Factor
Global 2-
Factor US FF
Global
FF
US 41 International 50 + US 41
Intercept 8.81 3.25 3.34 4.04 3.96 7.54 3.24 4.13 4.18 5.53
14.91 3.78 3.88 3.93 3.61 20.27 3.31 3.54 3.59 3.96
2.30 3.38 3.58 5.92 6.13 1.68 1.16 2.96 2.42 6.35
LevFac 43.54 53.48 52.88
60.96 69.67 70.97
3.35 6.18 6.07
5.60 6.97 6.50
2.30 2.21 2.19
2.00 2.13 2.26
Market-
RF 5.84 5.63 4.12 5.83
4.63 2.59 1.66 2.38
6.93 6.37 3.38 4.43
4.33 2.27 1.17 1.55
1.49 1.35 1.16 1.58
1.14 0.65 0.46 0.65
SMB
2.49 0.62
3.75 -0.94
2.57 0.56
3.29 -1.09
1.20 0.32
1.75 -0.55
HML
4.04 3.33
4.20 1.83
3.31 2.94
3.04 1.51
1.49 1.40 1.46 0.79
Adj R2 0.20 0.66 0.65 0.53 0.47 0.25 0.39 0.39 0.22 0.02
MAPE 8.81 3.47 3.54 4.20 4.13 7.67 3.85 4.58 4.58 5.93
GRS 176.40 146.85 150.40 222.71 227.40 1770.23 1539.75 1519.80 3129.49 3278.19
0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Europe 50 + US 41 UK 25 + US 41
Intercept 8.42 4.26 4.91 6.30 6.73 8.70 3.60 3.58 3.83 3.65
21.37 3.68 3.96 4.65 4.70 21.74 4.29 4.22 4.13 3.68
1.92 1.93 3.75 6.07 6.91 2.17 3.14 3.49 5.56 5.72
LevFac 49.07 59.14 57.76
43.22 51.16 50.37
4.60 5.74 5.43
4.48 6.74 6.63
2.20 2.56 2.61
1.90 2.17 2.15
Market-
RF 4.22 2.73 1.03 1.59
5.31 4.97 3.63 5.64
3.55 2.34 0.69 1.05
6.26 5.73 3.33 4.78
1.04 0.67 0.27 0.44
1.35 1.18 1.02 1.51
SMB
3.17 0.21
2.63 -0.07
2.81 0.20
3.00 -0.09
1.33 0.13
1.25 -0.04
HML
2.25 1.11
4.17 2.97
1.62 0.99
4.39 3.85
0.76 0.46 1.41 1.26
Adj R2 0.18 0.29 0.25 0.10 0.00 0.23 0.53 0.53 0.46 0.40
MAPE 8.57 4.81 5.36 6.59 6.97 8.70 3.81 3.77 4.02 3.98
GRS 5493.39 4706.07 4823.46 8014.77 8276.72 589.02 515.19 519.13 767.20 762.56
0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Table 10. 4-factor and 2-factor horse race: On this table we present the cross-sectional results for, US 2-factor, US 4-factors and Global 4-factors models, for 25 US Size-book sorted portfolios along 10 US
portfolios sorted by maturity, International, Europe or UK 25 Size-book to market portfolios and International,
Europe Size-momentum portfolios. We present intercept, leverage factor (LevFac) and the standard 3-factors from
Fama French models coefficients, along with the respective t-statistics, located below coefficients. T-shanken
statistics are reported beneath standard t-statistics. We also report adjusted R2, adjR2, Mean Asset Pricing Error
(MAPE) and Gibbons, Ross, Shanken statistic, along with its respective p-value. Periods is 1991-2014.
US 2-
Factor
US 4-
Factors
Global 4-
Factors
US 2-
Factor
US 4-
Factors
Global 4-
Factors
US 25 + International 25 US 31 + International 25
Intercept 2.61 2.20 12.27 2.49 2.50 2.96
1.43 0.78 2.98 3.05 2.93 2.94
0.28 0.29 2.51 1.18 2.52 3.56
LevFac 73.76 41.07 88.00 73.75 42.01 87.21
7.94 1.46 6.15 8.34 2.15 6.03
1.89 1.21 1.28 1.99 0.74 1.28
Market-
RF 5.24 4.13 -4.93 5.36 3.84 4.18
2.79 3.76 -1.21 6.02 3.32 3.56
0.60 0.53 -0.83 1.34 1.08 1.14
SMB
3.65 -0.12
3.61 -0.29
6.19 -0.16
3.92 -0.37
1.69 -0.07
1.70 -0.15
HML
7.94 4.55
7.96 5.38
1.79 4.59
6.54 5.77
2.24 1.90 2.07 2.22
Adj R2 0.57 0.59 0.51 0.66 0.68 0.57
MAPE 2.95 2.65 12.27 2.82 2.83 3.30
GRS 98.11 148.43 77.78 148.06 225.02 120.51
0% 0.00 0.01 0.00 0.00 0.00
US 35 + International 50 US 41 + International 50
Intercept 4.67 7.14 12.73 3.24 3.87 4.27
2.77 3.86 5.04 3.31 3.83 3.80
0.62 0.78 1.83 1.16 1.34 2.57
LevFac 67.36 85.32 80.64 69.67 87.63 96.56
6.44 5.64 6.06 6.97 5.82 7.31
1.83 1.37 1.50 2.13 1.46 1.79
Market-
RF 3.17 0.80 -5.17 4.63 4.32 3.07
1.78 0.38 -2.06 4.33 3.27 2.50
0.44 0.09 -0.63 1.14 1.09 0.74
SMB
2.87 0.26
2.85 0.08
2.87 0.39
2.86 0.12
1.24 0.15
1.22 0.05
HML
7.06 2.45
7.48 3.51
5.22 2.48
5.59 3.53
1.58 0.95 1.86 1.41
Adj R2 0.32 0.37 0.37 0.39 0.42 0.38
MAPE 5.01 7.21 12.73 3.85 4.27 4.62
GRS 934.47 704.72 749.21 1539.75 1162.28 1027.14
0.00 0.00 0.00 0 0.00 0.00
Table 11. Other Global Portfolios: On this table we present the cross-sectional results for US single leverage based on new methodology (Lev), US 2-factor, US Fama-French, Global 2-factor and Global Fama-French
models, for Global constructed portfolios, which already use US assets. Portfolios are, Global 25 is constructed using
25 size-book to market global portfolios, Global 50, adding 25 size-momentum portfolios to Global 25, Global 25 + 6
bonds, which includes 6 bond portfolios on Global 25 and Global 50 + 6, which include 6 bond portfolios to Global
50. We present intercept, leverage factor (LevFac) and the standard 3-factors from Fama French models coefficients,
along with the respective t-statistics, located below coefficients. T-shanken statistics are reported beneath standard t-
statistics. We also report adjusted R2, adjR2, Mean Asset Pricing Error (MAPE) and Gibbons, Ross, Shanken statistic,
along with its respective p-value. Periods is 1991-2014.
LevFac US 2-
Factor
Global
2-Factor US FF
Global
FF LevFac
US 2-
Factor
Global
2-Factor US FF
Global
FF
Global 25 Global 50
Intercept 7.65 14.70 14.16 13.58 6.04 7.97 15.82 15.35 20.14 18.53
0.34 4.56 3.94 3.67 1.46 18.68 4.51 4.40 7.71 5.78
1.57 2.95 2.42 3.38 1.20 1.73 3.42 2.87 4.01 3.80
LevFac 61.10 38.80 47.34
55.16 16.31 26.53
11.72 2.62 3.51
3.20 0.68 1.24
1.57 1.39 1.53
1.55 0.45 0.74
Market-
RF -7.85 -7.03 -8.25 -2.07
-8.76 -7.69 -14.89 -12.30
-2.30 -2.08 -2.17 -0.56
-2.34 -2.37 -5.36 -3.93
-1.21 -1.00 -1.52 -0.36
-1.32 -1.18 -2.38 -2.15
SMB
0.87 0.88
1.87 1.12
0.73 1.13
1.40 1.20
0.30 0.52
0.64 0.73
HML
5.17 5.73
0.13 0.44
4.66 6.04
0.08 0.30
1.78 2.42 0.03 0.16
Adj R2 0.52 0.59 0.57 0.66 0.63 0.16 0.22 0.22 0.38 0.21
MAPE 7.65 14.70 14.16 13.58 6.04 8.15 15.82 15.35 20.14 18.52
GRS 48.82 62.96 56.66 73.79 51.71 188.59 277.18 259.62 244.87 210.65
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Global 25 + 6 Bonds Portfolios Global 50 + 6 Bonds Portfolios
Intercept 6.51 2.83 2.74 2.76 2.41 7.30 3.63 3.59 4.73 4.39
12.94 4.05 3.99 4.57 4.23 15.92 3.24 3.20 4.02 3.45
1.84 3.61 3.60 5.15 4.34 1.90 3.87 3.74 6.37 5.90
LevFac 50.28 74.29 69.81
47.30 73.56 69.30
2.58 5.43 5.31
2.42 3.82 3.69
1.31 1.73 1.71
1.30 1.88 1.87
Market-
RF 4.66 3.68 2.79 1.16
4.19 3.18 1.04 1.08
5.66 5.08 3.27 1.51
3.31 2.86 0.69 0.72
1.05 0.98 0.75 0.33
0.91 0.82 0.28 0.31
SMB
2.71 1.21
3.98 1.46
2.38 1.86
2.37 1.36
0.99 0.74
1.48 0.97
HML
6.81 6.00
2.26 1.13
6.15 6.81
1.17 0.68
2.26 2.47 0.68 0.44
Adj R2 0.16 0.62 0.64 0.72 0.70 0.08 0.24 0.24 0.13
MAPE 6.51 2.95 2.87 2.87 2.53 7.50 4.25 4.22 5.07 4.96
GRS 103.06 71.01 75.52 137.03 78.27 436.63 296.41 314.17 606.65 437.87
0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
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Recommended