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This empirical study analyzes the information and predictive content of the Baltic Dry Index (BDI) with respect to a range of financial assets and the macroeconomy. By using panel methodological approaches and daily data spanning the period 1985–2012, the empirical analysis documents the joint predictability capacity of the BDI for both financial assets and industrial production. The results reveal the role of the BDI in predicting the future course of the real economy, yielding a link between financial asset markets and the macroeconomy.
The ability to track and even anticipate economic activity has been an ongoing endeavor of economic researchers. The debate over the use of a single or collection of leading indicators has been long standing (see Stock and Watson [
Moreover, oil prices have been a recurrent topic in terms of representing the impact of supply shocks in propagating cyclical fluctuations (see Hamilton [
However, an area of inquiry not explicitly considered has been the supply (chain) structure associated with the shipping industry. In particular, the structure of the shipping industry is relatively predictable with any changes in shipping costs due largely to changes in worldwide demand for raw materials (Stopford [
This index can be used as an overall economic indicator as it reflects the direction of enduse prices for goods that use the raw materials that are shipped in dry bulk. The BDI is especially relevant for the trade of the Less Developed Countries (LDCs) whose exports are mainly made up of primary goods, with the majority of them relying on bulk carriers for international transportation. In this respect, the BDI reflects important components of the cost of trade, rendering a negative effect on the LDCs trading activities. The close association between the cost of shipping raw materials and the production of intermediate and final goods, has led some analysts and researchers to conclude that the demand for commodities and, therefore, economic activity, is reflected by movements in the BDI. In other words, the association between the BDI and stock markets comes in an indirect manner, since the BDI reflects changes in economic activity, (
It is assumed that when the BDI rises, the increase reflects a stronger demand for commodities, as producers are purchasing more materials to accommodate the growth in production while a downward trend in the BDI implies that producers face insufficient consumer demand, with firms curtailing production as a result. Some analysts consider the BDI to be a useful indicator, especially when looking for signs of economic recovery, on the grounds that the index provides realtime updates visàvis traditional economic indicators. As a matter of fact, the BDI could also reflect some speculative movements, since there are futures contracts on BDI (albeit with small volumes) and the underlying freight market may also reflect the speculative actions of market participants. One of the very limited papers that consider a shipping index as a leading indicator to predict a number of economic and financial variables is that of Bashki
Given the limited literature on new leading indicators that could be used not only to predict the future course of economic growth, but also a number of asset prices and, thus, assist international portfolio investors to form more rational investment strategies, the appropriate research question this study investigates is the quality of the information and predictive content of the BDI as a leading indicator with respect to a number of asset markets and the macroeconomy as well. In terms of the asset markets, this empirical endeavor investigates whether changes in the BDI could be fully, partially or not all reflected in asset prices, thus, lending support or not to the Efficient Market Hypothesis (EMH). The study is largely exploratory in nature by utilizing daily data for G7 countries within a panel framework to infer the longrun equilibrium relationship between the BDI with both the financial asset markets and macroeconomy along with the shortrun and longrun causal dynamics. Furthermore, the outofsample forecasting performance of the BDI with respect to the financial asset markets and the macroeconomy is presented as well. Our work builds on the work of Alizadeh and Muradoglu [
We collect daily data for the G7 countries: Canada, France, Germany, Italy, Japan, U.K., and the U.S. with respect to stock market returns (AP^{STK}), interest rates on shortterm bonds (AP^{STB}), interest rates on longterm bonds (AP^{LTB}), commodity prices (AP^{COM}) and oil prices (OP), and monthly data on industrial production (IP) over the period 1985 to 2012.
The analysis begins with an examination of the unit root properties of the respective variables via panel unit root tests to determine the order of integration. Several panel unit root tests are undertaken to ascertain the robustness of the results. These panel unit root tests include Levin and Lin [
However, the three indicator variables, BDI, the MSCI, and oil prices, are, first, tested using the augmented DickeyFuller [
Unit root tests.
Variables  LL  FADF  FPP  HT  Breit 

AP^{STK}  −1.36  16.52  14.35  −1.25  −1.25 
−9.71 *  163.44 *  146.51 *  −8.49 *  −9.34 *  
AP^{STB}  −1.19  14.31  13.29  −1.15  −1.30 
ΔAP^{STB}  −8.54 *  172.36 *  137.18 *  −8.36 *  −9.71 * 
AP^{LTB}  −1.26  13.59  14.28  −1.16  −1.22 
ΔAP^{LTB}  −9.38 *  178.48 *  175.28 *  −9.55 *  −9.90 * 
AP^{COM}  −1.29  14.52  16.33  −1.07  −1.14 
ΔAP^{COM}  −9.36 *  165.73 *  152.39 *  −9.47 *  −9.73 * 
IP  −1.05  11.06  12.31  −1.03  −1.28 
ΔIP  −9.74 *  150.38 *  148.46 *  −8.74 *  −7.94 * 




BDI  −1.14  −1.23  −1.25  
ΔBDI  −7.82*  −8.48 *  −7.62*  
MSCI  −1.29  −1.11  −1.24  
ΔMSCI  −8.39*  −7.74*  −8.83*  
OP  −1.25  −1.27  −1.20  
ΔOP  −7.83*  −9.37*  −8.39* 
Notes: Δ denotes first differences; LL denotes the Levin and Lin test; FADF and FPP denotes the Maddala and Wu test; HT denotes the Harris and Tzavalis test; Breit denotes the Breitung test; * accepts the null hypothesis of stationarity at the 1% level.
Two specifications of the longrun equilibrium are postulated to explore the role of the BDI on financial asset markets and the macroeconomy given by Equations (1) and (2).
To determine whether a longrun relationship exists between financial assets, industrial production, and the BDI we utilize the methodology of Pedroni’s [
Panel cointegration tests.





Panel vstatistic  56.44952 *  Group ρstatistic  −57.35286 * 
Panel ρstatistic  −54.09539 *  Group PPstatistic  −57.48095 * 
Panel PPstatistic  −54.43875 *  Group ADFstatistic  −12.27308 * 
Panel ADFstatistic  −12.74976 *  



Panel vstatistic  57.96732 *  Group ρstatistic  −57.83485 * 
Panel ρstatistic  −56.92985 *  Group PPstatistic  −58.75298 * 
Panel PPstatistic  −56.74398 *  Group ADFstatistic  −12.50084 * 
Panel ADFstatistic  −11.76439 *  





Panel vstatistic  56.57549 *  Group ρstatistic  −55.72306 * 
Panel ρstatistic  −57.28944 *  Group PPstatistic  −58.82745 * 
Panel PPstatistic  −57.89075 *  Group ADFstatistic  −12.93284 * 
Panel ADFstatistic  −12.32486 *  





Panel vstatistic  57.84562 *  Group ρstatistic  −56.22375 * 
Panel ρstatistic  −56.63762 *  Group PPstatistic  −56.96574 * 
Panel PPstatistic  −56.09894 *  Group ADFstatistic  −11.58942 * 
Panel ADFstatistic  −11.32387 *  





Panel vstatistic  53.42573 *  Group ρstatistic  −52.93287 * 
Panel ρstatistic  −52.98956 *  Group PPstatistic  −52.79640 * 
Panel PPstatistic  −52.67860 *  Group ADFstatistic  −8.62583 * 
Panel ADFstatistic  −9.63297 * 
Notes: Both the panel and group mean panel tests are distributed asymptotically as standard normal. Of the seven tests, the panel vstatistic is a onesided test in which large positive values reject the null hypothesis of no cointegration. For the remaining test statistics, large negative values reject the null hypothesis of no cointegration. The 1% significance level denoted by “*”.
Given the presence of a longrun equilibrium, we follow Pedroni [
FMOLS (fullymodified ordinary least squares) longrun estimates.
Variable  a_{1}  AdjustedR^{2}  

AP^{STK}  0.148  [0.00] *  0.63 
AP^{STB}  0.081  [0.01] *  0.52 
AP^{LTB}  0.104  [0.00] *  0.57 
AP^{COM}  0.119  [0.01] *  0.45 
IP  0.175  [0.00] *  0.66 
Notes: a_{1} is the coefficient for BDI; pvalue is the probability value attached the a_{1} coefficient estimate; and adjusted R^{2} is the adjusted coefficient of determination. Probability values are in brackets with the 1% significance level denoted by “*”.
Next, we estimate a panel error correction model to infer both the shortrun and longrun causality between the respective financial asset markets, industrial production, and the BDI. Shortrun causality is denoted by Wald Ftests on the lagged coefficients of the first differences of the respective variables while longrun causality is given by the statistical significance of the error correction term (ECT).
Panel causality test results.






ΔAP^{STK}  ΔBDI  ECT  BDI→AP^{STK} (SR and LR)  
ΔAP^{STK}    64.73  −0.168  
[0.00] *  [0.00] *  
ΔBDI  1.19    −0.041  
[0.46]  [0.23]  






ΔAP^{STB}  ΔBDI  ECT  BDI→AP^{STB} (SR/LR)  
ΔAP^{STB}    48.92  −0.137  
[0.00] *  [0.00] *  
ΔBDI  0.35    −0.042  
[0.69]  [0.57]  






ΔAP^{LTB}  ΔBDI  ECT  BDI→AP^{LTB} (SR/LR)  
ΔAP^{LTB}    58.82  −0.149  
[0.00] *  [0.00] *  
ΔBDI  0.41    −0.025  
[0.72]  [0.68]  






ΔAP^{COM}  ΔBDI  ECT  BDI→AP^{COM} (SR/LR)  
ΔAP^{COM}    62.33  −0.164  
[0.00] *  [0.00] *  
ΔBDI  0.55    −0.021  
[0.68]  [0.50]  






ΔIP  ΔBDI  ECT  BDI→IP (SR/LR)  
ΔIP    52.07  −0.144  
[0.00] *  [0.00] *  
ΔBDI  42.06    −0.124  
[0.00] *  [0.00] * 
Notes: Wald
Moreover, the negative sign associated with the parameter estimates for the error correction terms represents the speed of the adjustment of financial asset prices or industrial production towards longrun equilibrium in response to shocks. The larger the value of this parameter estimate, the stronger is the response of the variable to the previous period’s deviation from longrun equilibrium. All error correction terms display a relatively high speed of adjustment toward longrun equilibrium with the speed of adjustment as follows: −0.168 (AP^{STK}), −0.137 (AP^{STB}), −0.149 (AP^{LTB}), −0.164 (AP^{COM}) and −0.144 (IP).
We also address the merit of the BDI visàvis a number of alternative indicators. Such alternative indicators are the MSCI World Index and oil prices. Both the MSCI World Index and oil prices (in the same manner as the BDI) reflect the expectations of investors and market operators regarding the performance of firms and the economy in general. To the extent that these expectations are largely correct, these indices could be also used to gauge future economic activity. The former index has been used in the literature by Harvey [
Thus, the alternative longrun specifications incorporating the MSCI are given as follows in Equations (3) and (4):
In addition, the longrun specifications incorporating oil prices are noted as follows in Equations (5) and (6):
As in Equations (1) and (2), we are interested in the statistical significance of the a_{1} coefficient, in light of the presence of the alternative indicators. The new panel cointegration test results incorporating the above specifications are reported in
Extended panel cointegration tests.





Panel vstatistic  42.63286 *  Group ρstatistic  −41.82673 * 
Panel ρstatistic  −43.95738 *  Group PPstatistic  −41.50842 * 
Panel PPstatistic  −43.42387 *  Group ADFstatistic  −7.62749 * 
Panel ADFstatistic  −8.82086 *  





Panel vstatistic  45.52984 *  Group ρstatistic  −44.47389 * 
Panel ρstatistic  −44.08942 *  Group PPstatistic  −45.50908 * 
Panel PPstatistic  −44.52896 *  Group ADFstatistic  −8.63264 * 
Panel ADFstatistic  −8.66420 *  





Panel vstatistic  40.38934 *  Group ρstatistic  −40.82764 * 
Panel ρstatistic  −41.90893 *  Group PPstatistic  −39.65259 * 
Panel PPstatistic  −41.67328 *  Group ADFstatistic  −6.58276 * 
Panel ADFstatistic  −6.96318 *  





Panel vstatistic  41.32437 *  Group ρstatistic  −40.82569 * 
Panel ρstatistic  −42.72785 *  Group PPstatistic  −40.42487 * 
Panel PPstatistic  −42.62674 *  Group ADFstatistic  −6.72487 * 
Panel ADFstatistic  −6.42684 *  





Panel vstatistic  39.82376 *  Group ρstatistic  −38.62364 * 
Panel ρstatistic  −40.72365 *  Group PPstatistic  −39.72436 * 
Panel PPstatistic  −40.62438 *  Group ADFstatistic  −6.50894 * 
Panel ADFstatistic  −6.61327 *  





Panel vstatistic  40.47325 *  Group ρstatistic  −40.62548 * 
Panel ρstatistic  −41.22586 *  Group PPstatistic  −42.32546 * 
Panel PPstatistic  −41.72674 *  Group ADFstatistic  −7.76409 * 
Panel ADFstatistic  −6.52547 *  





Panel vstatistic  42.33261 *  Group ρstatistic  41.41436 * 
Panel ρstatistic  −41.14756 *  Group PPstatistic  41.20415 * 
Panel PPstatistic  −41.90855 *  Group ADFstatistic  6.52008 * 
Panel ADFstatistic  −6.68952 *  





Panel vstatistic  40.49956 *  Group ρstatistic  −40.35465 * 
Panel ρstatistic  −41.71135 *  Group PPstatistic  −41.32365 * 
Panel PPstatistic  −41.60908 *  Group ADFstatistic  −6.59089 * 
Panel ADFstatistic  −6.82546 *  





Panel vstatistic  43.56008 *  Group ρstatistic  −43.62541 * 
Panel ρstatistic  −41.41136 *  Group PPstatistic  −43.88325 * 
Panel PPstatistic  −41.90671 *  Group ADFstatistic  −7.83073 * 
Panel ADFstatistic  −7.80665 *  





Panel vstatistic  44.61347 *  Group ρstatistic  −45.98784 * 
Panel ρstatistic  −45.60563 *  Group PPstatistic  −45.16044 * 
Panel PPstatistic  −45.18914 *  Group ADFstatistic  −8.11732 * 
Panel ADFstatistic  −8.70563 * 
Notes: Both the panel and group mean panel tests are distributed asymptotically as standard normal. Of the seven tests, the panel vstatistic is a onesided test in which large positive values reject the null hypothesis of no cointegration. For the remaining test statistics, large negative values reject the null hypothesis of no cointegration. The 1% significance level denoted by “*”.
Extended model FMOLS longrun estimates.









AP^{STK}  0.107  [0.00] *  0.063  [0.00] *  0.68  [0.00] * 
AP^{STB}  0.068  [0.00] *  0.030  [0.00] *  0.54  [0.01] * 
AP^{LTB}  0.051  [0.00] *  0.026  [0.00] *  0.51  [0.00] * 
AP^{COM}  0.085  [0.00] *  0.031  [0.00] *  0.49  [0.01] * 
IP  0.138  [0.00] *  0.059  [0.00] *  0.68  [0.00] * 









AP^{STK}  0.096  [0.00] *  0.054  [0.00] *  0.69  [0.00] * 
AP^{STB}  0.064  [0.00] *  0.043  [0.00] *  0.57  [0.00] * 
AP^{LTB}  0.052  [0.00] *  0.036  [0.00] *  0.53  [0.00] * 
AP^{COM}  0.073  [0.00] *  0.041  [0.00] *  0.57  [0.00] * 
IP  0.129  [0.00] *  0.068  [0.00] *  0.62  [0.00] * 
Notes: a_{1} is the coefficient for BDI; a_{2} represents the coefficient of either MSCI or OP; pvalue is the probability value attached to the coefficient estimates a_{1} and a_{2}; joint pvalue is the joint probability value associated with the null hypothesis a_{1} = a_{2} = 0; and adjusted R^{2} is the adjusted coefficient of determination. Probability values are in brackets with the 1% significance level denoted by “*”.
Given the extended models that include the MCSI and oil prices, we conduct causality testing associated with the respective panel error correction models to infer both the shortrun and longrun causality between the respective financial asset prices, industrial production, and the relative causality merit between BDI and either MSCI or oil prices. Panels A through E in
Extended panel causality test results.






ΔAP^{STK}  ΔBDI  ΔMSCI  ECT  BDI→AP^{STK} (SR and LR)  
ΔAP^{STK}    62.73  1.34  −0.124  
[0.00] *  [0.59]  [0.00] *  
ΔBDI  1.55    0.61  −0.084  
[0.38]  [0.74]  [0.18]  
ΔMSCI  1.24  0.62    −0.011  
[0.50]  [0.66]  [0.61]  






ΔAP^{STB}  ΔBDI  ΔMSCI  ECT  BDI→AP^{STB} (SR/LR)  
ΔAP^{STB}    52.91  4.22  −0.075  MSCI→AP^{STB }(SR/LR)  
[0.00] *  [0.11]  [0.00] *  
ΔBDI  0.36    0.71  −0.059  
[0.75]  [0.58]  [0.52]  
ΔMSCI  3.18  0.57    −0.036  
[0.19]  [0.62]  [0.69]  






ΔAP^{LTB}  ΔBDI  ΔMSCI  ECT  BDI→AP^{LTB} (SR/LR)  
ΔAP^{LTB}    48.91  1.36  −0.125  
[0.00] *  [0.40]  [0.00] *  
ΔBDI  0.56    0.71  −0.048  
[0.71]  [0.69]  [0.57]  
ΔMSCI  0.74  1.44    −0.059  
[0.61]  [0.41]  [0.48]  






ΔAP^{COM}  ΔBDI  ΔMSCI  ECT  BDI→AP^{COM} (SR/LR)  
ΔAP^{COM}    68.71  1.34  −0.142  
[0.00] *  [0.21]  [0.00] *  
ΔBDI  0.73    1.64  −0.071  
[0.52]  [0.21]  [0.22]  
ΔMSCI  0.63  0.93    −0.037  
[0.69]  [0.28]  [0.42]  






ΔIP  ΔBDI  ΔMSCI  ECT  BDI→IP (SR/LR)  
ΔIP    63.19  31.82  −0.176  MSCI↔IP (SR/LR)  
[0.00] *  [0.00] *  [0.00] *  
ΔBDI  46.11    1.18  −0.146  
[0.00] *  [0.59]  [0.00] *  
ΔMSCI  49.94  0.86    −0.095  
[0.00] *  [0.68]  [0.00] *  






ΔAP^{STK}  ΔBDI  ΔMSCI  ECT  BDI→AP^{STK} (SR/LR)  
ΔAP^{STK}    53.83  14.32  −0.105  OP→AP^{STK} (SR/LR)  
[0.00] *  [0.07] *  [0.00] *  
ΔBDI  1.63    2.04  −0.066  
[0.36]  [0.24]  [0.14]  
ΔOP  0.98  0.85    −0.024  
[0.71]  [0.82]  [0.32]  






ΔAP^{STK}  ΔBDI  ΔMSCI  ECT  BDI→AP^{STK} (SR/LR)  
ΔAP^{STB}    56.71  10.96  −0.102  OP→AP^{STB} (SR/LR)  
[0.00]  [0.00] *  [0.00] *  
ΔBDI  0.61    0.36  −0.053  
[0.68]  [0.80]  [0.15]  
ΔOP  1.22  0.84    −0.038  
[0.39]  [0.45]  [0.32]  






ΔAP^{LTB}  ΔBDI  ΔMSCI  ECT  BDI→AP^{STK} (SR/LR)  
ΔAP^{LTB}    57.84  11.66  −0.112  OP→AP^{LTB} (SR/LR)  
[0.00] *  [0.06] **  [0.00] *  
ΔBDI  0.58    0.52  −0.040  
[0.61]  [0.56]  [0.49]  
ΔOP  2.64  2.27    −0.058  
[0.24]  [0.26]  [0.30]  






ΔAP^{COM}  ΔBDI  ΔMSCI  ECT  BDI→AP^{STK} (SR/LR)  
ΔAP^{COM}    50.18  5.62  −0.158  OP→AP^{COM }(SR/LR)  
[0.00] *  [0.09] ***  [0.00] *  
ΔBDI  0.81    2.96  −0.074  
[0.45]  [0.16]  [0.31]  
ΔOP  0.52  0.84    −0.048  
[0.77]  [0.39]  [0.27]  






ΔIP  ΔBDI  ΔMSCI  ECT  BDI↔IP (SR/LR)  
ΔIP    73.04  40.01  −0.186  OP↔IP (SR/LR)  
[0.00] *  [0.00] *  [0.00] *  
ΔBDI  38.51    1.25  −0.161  
[0.00] *  [0.48]  [0.00] *  
ΔOP  46.45  1.27    −0.135  
[0.00] *  [0.62]  [0.00] * 
Notes: Wald
To assess the outofsample prediction ability of the BDI we use a rolling regression methodology based on the error correction models presented. That is, each model is first estimated using data up until the first forecasting period. The forecasts are generated at 1, 10, 30, 180, and 365 days (actually, the period of 365 days includes 259 observations. The same also holds for the remaining time periods). In the next step, the estimation period is rolled forward by one day, keeping the total length of the estimation period fixed. New forecasts are then generated at 1, 10, 30, 180 and 365 days. The root mean square error (RMSE), the mean absolute error (MAE), and the Theil inequality coefficient (THEIL) are used to evaluate the outofsample forecasting performance of the respective error correction models.
We begin by first considering a model in which only the BDI is shown to be the key indicator against the two models in which the other two alternative indicators,
This exploratory empirical study analyzes the role of the Baltic Dry Index (BDI) in explaining the behavior of financial asset prices and industrial production. By using a panel data framework for financial asset prices and industrial production over the period 1985–2012, the longrun empirical analysis conducted for both insample and outofsample confirms the role of the BDI as a useful indicator. Furthermore, the empirical findings demonstrate the robustness of the BDI’s role in light of the presence of alternative indicators, the MSCI and oil prices. Overall, this study demonstrates the relevance of the BDI as an indicator that captures the variations across financial asset markets and the macroeconomy. These findings support the claim of the close relationship between the cost of shipping raw materials and the production of intermediate and final goods in that the demand for commodities and, therefore, economic activity, follows movements in the BDI. This relationship of the BDI to the real economy also impacts financial asset markets as well. Our results are in contrast with those reached by Alizadeh and Muradoglu [
Forecasting metrics.












AP^{STK}BDI  AP^{STK}MSCI  AP^{STK}OP  
1  4.906  5.783  0.268  4.961  5.908  0.291  4.937  5.855  0.285 
10  5.024  5.951  0.289  5.461  6.099  0.296  5.286  6.048  0.292 
30  5.508  6.185  0.314  5.677  6.439  0.325  5.854  6.290  0.321 
180  5.894  6.507  0.358  5.949  6.638  0.370  5.898  6.632  0.369 
365  6.105  6.884  0.392  6.337  6.995  0.428  6.218  6.901  0.405 












AP^{STB}BDI  AP^{STB}MSCI  AP^{STB}OP  
1  5.248  5.409  0.286  5.619  5.731  0.310  5.438  5.629  0.298 
10  5.546  5.817  0.315  5.826  5.905  0.337  5.632  5.844  0.326 
30  5.915  6.236  0.361  6.117  6.448  0.389  6.026  6.372  0.375 
180  6.274  6.548  0.390  6.548  6.825  0.428  6.430  6.669  0.408 
365  6.637  6.871  0.418  6.836  6.995  0.457  6.744  6.903  0.425 












AP^{LTB}BDI  AP^{LTB}MSCI  AP^{LTB}OP  
1  5.127  5.326  0.295  5.548  5.912  0.348  5.335  5.618  0.313 
10  5.548  5.704  0.328  5.983  6.109  0.381  5.711  5.923  0.357 
30  5.905  6.128  0.381  6.326  6.576  0.419  6.224  6.348  0.398 
180  6.327  6.683  0.399  6.874  6.905  0.439  6.615  6.904  0.418 
365  6.629  6.972  0.442  7.318  7.459  0.485  6.983  7.302  0.466 












AP^{COM}BDI  AP^{COM}MSCI  AP^{COM}OP  
1  6.137  6.837  0.314  6.663  7.358  0.346  6.328  7.119  0.322 
10  6.528  7.271  0.355  7.093  7.693  0.392  6.771  7.348  0.360 
30  6.944  7.561  0.387  7.452  7.984  0.425  7.216  7.709  0.398 
180  7.328  7.907  0.422  7.651  8.436  0.492  7.561  8.225  0.473 
365  7.673  8.124  0.461  8.046  8.562  0.536  7.842  8.337  0.491 












IPBDI  IPMSCI  IPOP  
1  5.128  5.674  0.271  5.558  5.903  0.341  5.325  5.751  0.294 
10  5.443  5.935  0.316  5.836  6.438  0.372  5.661  6.264  0.366 
30  5.893  6.438  0.359  6.418  6.922  0.397  6.109  6.614  0.385 
180  6.436  6.951  0.402  6.952  7.513  0.448  6.716  7.228  0.439 
365  6.710  7.358  0.472  7.095  7.774  0.496  6.911  7.592  0.481 
The authors wish to thank two referees of this journal for their constructive comments that improves the picture of the first draft of the paper. Needless to say, errors and omissions are our responsibility.
The authors declare no conflict of interest.
United States (S&P500), United Kingdom (FTSE100), Japan (NIKKEI225), Canada (S&P/TSX Composite), Germany (DAX), France (CAC40), and Italy (S&P/MIB Index). Source:
3month Canadian Government Bonds, 3month French Treasury Bills, 3month German Bubill Government Bonds, 3month Italian Treasury Bills, 3month U.K. Government Bonds, Japanese Treasury Discount Bills, 3month U.S. Generic government bonds. Source:
10year Canadian Government Bonds, 10year French Government Bonds, 10year German Government Bonds, 10year Italian Government Bonds, Japanese Government Bonds, 10year U.K. Government Bonds, 10year U.S. Generic Government Bonds. Source:
The Dow JonesUBS Commodity Index, which is a broadly diversified index that allows investors to track commodity futures. Source:
U.S. Industrial Production (2007 = 100), STCA Canadian Industrial Production (2007 = 100), Japanese Industrial Production (2005 = 100), French Industrial Production (2007 = 100), Italian Industrial Production (2007 = 100), U.K. Industrial Production (2005 = 100), German Industrial Production (2007 = 100). Source:
Oil prices are defined by the spot price of West Texas Intermediate crude oil. West Texas Intermediate (WTI) is a type of crude oil used as a benchmark in oil pricing and the underlying commodity of the New York Mercantile Exchange’s oil futures contracts. Source:
The Baltic Dry Index is a daily average of prices to ship raw materials. It represents the cost paid by an end customer to have a shipping company transport raw materials across seas on the Baltic Exchange, the global marketplace for brokering shipping contracts. Source:
The MSCI captures large and mid cap representation across 24 Developed Markets (DM) countries. With 1,608 constituents, the index covers approximately 85% of the free floatadjusted market capitalization in each country. Source: