Can the Baltic Dry Index predict foreign exchange rates?

Finance Research Letters xxx (xxxx) xxx–xxx

Contents lists available at ScienceDirect

Finance Research Letters journal homepage: www.elsevier.com/locate/frl

Can the Baltic Dry Index predict foreign exchange rates? Han Liyana, Wan Lia, Xu Yangb, a b

⁎

Beihang University, School of Economics & Management, Beijing, China Beijing University of Technology, Beijing-Dublin International College, Beijing, China

ARTICLE INFO

ABSTRACT

Keywords: Baltic Dry Index Exchange rates Long-run Predictability

The Baltic Dry Index (BDI) is commonly perceived as a leading indicator of economic activities. In this paper, we explore whether the BDI has predictive ability for exchange rates of fourteen major currencies against US dollar. Results of panel regression demonstrate that the BDI provides statistically significant long-run predictability of currency returns. An increase in the BDI is associated with a depreciation of currency. The three sub-indices of the BDI also show significant predictive power with similar patterns. The in-sample and out-of-sample results of individual time-series regressions illustrate that the BDI shows significant predictive ability in majority of the cases and displays an inverted U-shaped predictive pattern. Our results imply that by capturing information about economic fundamentals the BDI is a useful predictor for exchange rates.

1. Introduction A large literature focuses on the role of economic fundamentals in predicting the changes in currency returns since Meese and Rogoff (1983) pose the "exchange rate disconnect puzzle". Recently, several variables have been proposed as potential predictors which contain useful information from economic fundamentals, such as commodity prices (Chen and Rogoff, 2003; Ferraro et al., 2015; Ji et al., 2015), external imbalance measures (Gourinchas and Rey, 2007), economic uncertainty (Christou et al., 2018; Ma et al., 2019), and etc. However, empirical results regarding those potential predictors are still mixed due to the inability of models to reconcile, quantitatively, the relation between exchange rates and economic fundamentals (Rossi, 2013). Moreover, existing research focus on the short-term relation between economic fundamentals and exchange rates. Evidence on the long-run predictability of currency returns is limited. In this study, we try to resolve this puzzle by introducing novel predictors based on the Baltic Dry Index (BDI) and explore the long-term relationship with exchange rates. Issued by the Baltic Exchange on daily basis since May 1985, the BDI is a composite of three sub-indices based on different vessel size (Capesize, Panamax and Supramax), standing for the freight rate level in the dry bulk shipping market. Our interest in the BDI stems from the prevailing view that BDI is importantly associated with real economy since international trade heavily relies on the three major shipping markets (tanker, dry bulk and container). According to the Baltic Exchange reports in 2018, around 80% of all internationally traded goods and commodities are carried at some point by a ship. In 2013 the world's ships carried over 9.6 billion tonnes of cargo, a record volume for seaborne trade. The relationship between the dry bulk market and real economy has been widely examined (see, Apergis and Payne, 2013; Bakshi et al., 2012; Bildirici et al., 2015; Kilian, 2009, among others). In light of this, the BDI is not only relevant to the dry bulk market but also can reflect global demand for raw materials, such as iron ore, coal, grain, timber and steel. Moreover, recent empirical studies on the BDI extend to generate predictability of financial asset such as stock returns and commodity prices (see, Alizadeh and Muradoglu, 2014; Apergis and Payne,

⁎

Corresponding author. E-mail address: [email protected] (Y. Xu).

https://doi.org/10.1016/j.frl.2019.04.014 Received 8 November 2018; Received in revised form 25 March 2019; Accepted 6 April 2019 1544-6123/ © 2019 Elsevier Inc. All rights reserved.

Please cite this article as: Han Liyan, Wan Li and Xu Yang, Finance Research Letters, https://doi.org/10.1016/j.frl.2019.04.014

Finance Research Letters xxx (xxxx) xxx–xxx

L. Han, et al.

2013; Bakshi et al., 2012; Papapostolou et al., 2016, among others). Armed with this economically and empirically motivated BDI factor, we first intuitively explore its dynamic correlation with exchange rates. Fig. 1 plots the time series of the BDI and exchange rates of AUD, CAD and NZD (US dollar per unit of foreign currency). As shown, most of the time, the BDI and the three currencies show the same or opposite trend, implying that the BDI is closely correlated with the three currencies. The paper then empirically investigates the predictability of exchange rates based on BDI factor by answering three questions: (1) Whether the BDI has predictive ability for exchange rates? If so, what is the predictive pattern (powerful or weak, long-term or shortterm, consistent or transient)? (2) Whether the three components of the BDI generate similar predictive power? (3) Whether the predictive ability of the BDI substantially heterogeneous across currencies? In doing so, we first estimate panel regressions of all 14 exchange rates and then conduct predictive time-series regressions individually. Our paper fills an important gap in the finance literature on exchange rate predictability. To our best knowledge, we are the first paper to propose that the BDI factor contain useful information about fundamentals and thus can be seen as a new potential predictor for exchanges rates. Our paper also extends the existing research on the linkage between the BDI and financial assets since relevant evidence remains relatively limited. Our results demonstrate that the BDI has a long-run predictive ability for exchange rates from two aspects. First, with the increase of the forecast horizon, more predictors based on the BDI show significant predictive power. Second, if the predictor based on the BDI has more past information, it could show stronger predictive power. Further, sub-indices of the BDI show similar predictive ability when compared with the BDI. Finally, international evidence suggests that the BDI has an inverted U-shaped predictive pattern that peaks at 12-month forecast horizon. Overall, an increase in current BDI is generally associated with a depreciation of exchange rates in fairly long periods. 2. Data We focus on 14 exchange rates of major currencies (US dollar per unit of foreign currency) which are the top 15 traded currencies (including USD) in OTC foreign exchange according to the Triennial Survey of Bank for International Settlements (2016).1 To examine the predictability of exchange rates, currency returns at various forecast horizons are defined as follows.

si, t + h = (si, t + h

(1)

si, t )*100

where si,t denotes the log of nominal exchange rate of currency i (US dollar per unit of foreign currency), h is the forecast horizon (hmonth ahead) and Δsi,t + h indicate h-month ahead of exchange rate returns of currency i. Monthly spot exchange rate data is end-ofmonth and collected from Board of Governors of the Federal Reserve System.2 Our sample runs from 1995:01 to 2018:08 except for CNY and EUR. EUR spot rates span from 1999:01 to 2018:08. Note that CNY spot rates are available from 1995:01, but for prudential reasons, we choose the sample ranging from 2005:08 to 2018:08, since the renminbi regime is subject to strict regulatory controls before 2005:08.3 Shipping markets are of significant importance for international trade and carry information about global economic activity. The BDI is a composite of three sub-indices (BCI, BPI and BSI) that respectively measure different sizes of dry bulk carriers or merchant ships: Capesize, Panamax and Supramax.4 Predictors based on the BDI are defined as follows.

BDIL = ln(BDIt + L)

(2)

ln(BDIt ), L = 3, 6, 9, 12, 18, 24

where L denotes the lagged horizon. BDIL is supposed to contain more past information as L gets longer. For the three individual components of the BDI, we also construct corresponding predictor variables as BCIL, BPIL and BSIL. The monthly data of BDI, BCI, BPI and BSI are extracted from DataStream. To construct corresponding predictors, our sample for the BDI (BCI, BPI and BSI) runs from 1993:01 (1999:03, 1998:05 and 2005:07) to 2018:08, respectively. For improving robustness, we incorporate macroeconomic fundamentals as control variables since macroeconomic fundamentals may also influence the exchange rate returns. Here, the inflation differentials (Engel and West, 2005) are considered as control variable,5 which is defined as follows. i, t + h

= [(pt + h

pt )

(pt*+ h

pt* )]*100

(3)

where pt + h and pt*+ h denote the logarithm of CPI in the home and foreign economies. The monthly CPI data is obtained from DataStream and is not available for Australia and New Zealand. Our sample for CPI runs from 1995:01 to 2018:08 except for Eurozone, which starts from 1996:01. 1

Specifically, the 14 currencies include Euro (EUR), Japanese Yen (JPY), United Kingdom Pound (GBP), Australian Dollar (AUD), Canadian Dollar (CAD), Swiss Franc (CHF), Chinese Yuan/Renminbi (CNY), Swedish Krona (SEK), New Zealand Dollar (NZD), Mexican Peso (MXN), Singapore Dollar (SGD), Hong Kong Dollar (HKD), Norwegian Krone (NOK) and South Korean Won (KRW). See more details, https://www.bis.org/publ/ rpfx16fx.pdf 2 https://www.federalreserve.gov/default.htm 3 For the purpose of strengthening the managed floating exchange rate regime based on market supply and demand, the People's Bank of China (PBC) issued a statement to reform the exchange rate regime on July 21, 2005. 4 Capesize: 180,000 dwt; Panamax: 82,500 dwt; Supramax: 58,328 dwt. 5 We also consider other control variables like the interest rate differential, which is defined as iri, t+ h = iri,t + h iri*,t + h , where irt + h and irt*+ h denote the interest rate in the home and foreign economies respectively. Note that when we (i) exclude control variable or (ii) solely add the interest rate differential or (iii) add the inflation and interest rate differentials, the main findings about the BDI are virtually unchanged. 2

Finance Research Letters xxx (xxxx) xxx–xxx

L. Han, et al.

Fig. 1. Time series evolution of BDI and exchange rates of AUD, CAD and NZD, 1995:01-2018:08.

3. Predictive analysis 3.1. Fixed-effect panel regression We begin with a panel regression for the exchange rates, which can be expressed as:

si, t + h =

i

+

1 x j, t

+

2

i, t + h

+

i, t + h,t

= 1, 2,

,T

(4)

h

where xj,t is the predictor variable based on index j. Specifically, xj,t denotes BDIL,t, BCIL,t, BPIL,t and BSIL,t respectively. εi,t + h is a disturbance term and T is the total size of the full sample. The coefficients in Eq. (4) are estimated using fixed-effect setting. Table 1 reports the predictive results for h-month ahead exchange rate returns based on the BDI and its three individual components. Generally, the BDI generate significant predictive coefficients for exchange rates at longer horizons (3, 6, 12, 18 and 24month ahead). It should be noted that significant coefficients are negative for all predictors based on the BDI except for BDI3. Apart from the BDI3, significant coefficients of predictors range from −2.95 to −0.77. The negative sign indicates that an increase in the shipping index is associated with a decrease of currency returns in subsequent periods. In tuition, an increase in the cost of global shipping transportation relates to a higher cost of imported raw materials, which may cause imported inflation and consequently depreciation of currency. Coefficients associated with the inflation differentials are all positive and significant, indicating that foreign currency tends to depreciate when domestic inflation is relatively higher than that in the United States. Regarding the predictability of various horizons, our results suggest that the BDI has a long-term relationship with exchange rates, which is reflected in two aspects. First, we focus on the forecast horizon (h) for exchange rates. As illustrated in Table 1, with the increase of h, more and more predictors based on the BDI tend to show significant predictive power. For instance, at the 3 or 6-month horizon, only one predictor shows significant coefficient at confidence levels above 5%. At the 12-month horizon, the coefficients associated is significant for 2 out of 6 predictors based on the BDI. At the 18-month horizon, 3 out of 6 predictors show significant coefficients. At the 24-month horizon, all predictors based on the BDI show significant coefficients. Besides, the R2s are almost gradually increase with longer forecast horizons. Second, we focus on the lagged horizon (L). BDIL is supposed to contain more past information as L gets longer. With the increase of L, predictors based on BDI show stronger predictive power by covering more forecast horizons. For example, BDI3, BDI6, and BDI9 show significant predictive power only at one certain horizon while the predictive ability of BDI24 cover most of forecast horizons (6, 12, 18 and 24-month ahead). Among different predictors based on BDI, the BDI24 perform better. This could be explained by the fact that the BDI24 contain more past information and the diffusion of information from shipping markets to financial markets is gradual. Further, we compare the results based on the three sub-indices of the BDI with those based on the BDI. The three sub-indices of BDI provide similar predictive performance in terms of same lagged period. For example, at the 12-month forecast horizon, BDI18, BCI18, BPI18, and BSI18 all have negative and significant coefficients ranging from −1.40 to −0.95. Their R2s are 7.27%, 9.97%, 8.28% and 14.87% respectively. It is noted that the predictors based on BSI have larger (negative and significant) coefficients and larger R2s across different forecast horizons. In general, information contained in different components of the BDI generates similar predictive power as BDI does. Overall, there is a long-run significant predict ability of the BDI predictors: (i) as forecast horizons increase, predictability of exchange rates increases and (ii) predictors based on long-run BDI information provide more powerful predictive ability. An increase in current BDI is generally associated with a depreciation of exchange rates in fairly long periods. In comparison to the BDI, the three sub-indices of BDI show similar predictive ability as the BDI does.

3

3 β1

1.26** 0.62* 1.00** 1.18* 0.40 0.00 0.30 0.39 0.34 0.20 0.26 0.26 0.18 0.15 0.14 0.19 0.10 −0.03 0.16 0.18 −0.05 −0.04 −0.12 −0.25

h-month ahead Predictor

BDI3 BCI3 BPI3 BSI3 BDI6 BCI6 BPI6 BSI6 BDI9 BCI9 BPI9 BSI9 BDI12 BCI12 BPI12 BSI12 BDI18 BCI18 BPI18 BSI18 BDI24 BCI24 BPI24 BSI24

0.53*** 0.67*** 0.57*** 0.79*** 0.57*** 0.71*** 0.66*** 0.87*** 0.57*** 0.71*** 0.69*** 0.91*** 0.58*** 0.74*** 0.71*** 0.94*** 0.58*** 0.76*** 0.70*** 1.01*** 0.58*** 0.75*** 0.75*** 1.03***

β2

3.28 3.08 2.95 4.76 2.43 2.46 2.33 3.99 2.42 2.69 2.53 4.14 2.31 2.77 2.48 4.25 2.27 2.89 2.60 4.67 2.26 2.91 2.75 4.89

R2 (%) 0.58 −0.06 0.49 0.65 0.04 −0.25 0.08 0.09 −0.05 −0.01 0.03 0.10 −0.23 −0.19 −0.08 −0.07 −0.27 −0.31 −0.11 −0.17 −0.77** −0.67** −0.80** −1.47***

6 β1 0.88*** 1.21*** 1.02*** 1.70*** 0.88*** 1.23*** 1.13*** 1.72*** 0.88*** 1.28*** 1.18*** 1.73*** 0.88*** 1.31*** 1.20*** 1.75*** 0.88*** 1.37*** 1.27*** 1.80*** 0.89*** 1.40*** 1.38** 1.79***

β2 5.37 5.59 4.55 10.01 5.26 5.84 5.05 9.98 5.26 6.16 5.42 10.03 5.30 6.52 5.53 10.36 5.34 7.07 6.07 10.70 5.94 8.15 7.65 12.94

R2 (%) 0.34 0.08 0.41 0.65 −0.34 −0.34 −0.09 0.01 −0.41 −0.36 −0.06 −0.02 −0.59 −0.55 −0.24 −0.22 −1.08** −0.95** −0.99* −1.40* −1.93*** −1.55*** −2.05*** −4.36***

12 β1 0.79*** 1.15*** 0.93*** 2.05*** 0.79*** 1.21*** 1.02*** 2.05*** 0.79*** 1.30*** 1.08*** 2.05*** 0.79*** 1.37*** 1.14*** 2.06*** 0.80*** 1.47*** 1.33*** 2.04*** 0.82*** 1.55*** 1.54*** 1.84***

β2 6.63 6.54 5.07 13.25 6.64 7.07 5.55 13.40 6.67 7.83 5.98 13.49 6.76 8.45 6.43 13.81 7.27 9.97 8.28 14.87 8.72 11.82 11.87 23.79

R2 (%) 0.28 −0.46 0.57 0.54 −0.48 −0.76 −0.15 −0.25 −0.83 −0.82 −0.62 −0.61 −1.47** −1.57** −1.24* −1.48* −2.08*** −1.81*** −2.19*** −3.67*** −2.39*** −2.38*** −2.51*** −5.14***

18 β1 0.62*** 0.88*** 0.66*** 1.53*** 0.62*** 0.95*** 0.74*** 1.52*** 0.63*** 1.03*** 0.81*** 1.49*** 0.63*** 1.11*** 0.90*** 1.46*** 0.65*** 1.20*** 1.12*** 1.33*** 0.66*** 1.31*** 1.25*** 1.17***

β2

6.94 7.47 5.59 13.47 6.98 8.18 6.10 13.58 7.10 9.07 6.74 13.83 7.59 10.64 7.90 14.67 8.64 12.48 11.12 19.96 9.20 14.41 13.19 24.54

R2 (%)

−0.37 −0.58 −0.62 −0.33 −1.76* −1.64** −1.77* −1.82 −2.78*** −2.59*** −2.74*** −3.48*** −2.95*** −2.85*** −2.95*** −4.01*** −2.53*** −2.43*** −2.77*** −4.24*** −1.93*** −2.31*** −2.23*** −4.78***

24 β1

0.60*** 1.04*** 0.76*** 1.84*** 0.61*** 1.12*** 0.87*** 1.76*** 0.62*** 1.21*** 0.98*** 1.66*** 0.62*** 1.29*** 1.09*** 1.56*** 0.63*** 1.38*** 1.28*** 1.52*** 0.62*** 1.52*** 1.39*** 1.36***

β2

8.14 10.28 7.66 18.08 8.59 11.90 9.05 18.87 9.50 13.91 11.08 21.13 10.04 15.43 12.76 23.27 9.99 16.16 14.67 24.52 9.24 16.31 14.98 24.30

R2 (%)

Table 1 Panel predictive regression The estimated coefficients associated with predictor variable (the inflation differentials) are presented in columns 2, 5, 8, 11, and 14 (3, 6, 9, 12 and 15). R-squared statistics are presented in columns 4, 7, 10, 13 and 16. Standard errors are corrected for heteroskedasticity and autocorrelation. To save space, standard errors and currency-specific constants are left unreported. *, **, *** indicate significance level at 10%, 5% and 1%, respectively.

L. Han, et al.

Finance Research Letters xxx (xxxx) xxx–xxx

4

5

SGD

SEK

NZD

NOK

MXN

KRW

JPY

HKD

GBP

EUR

CNY

CHF

CAD

0.87* (0.47) 0.19 (0.23)

0.77* (0.44) 0.52 (0.43) 0.05 (0.13) 0.96** (0.45) 1.35*** (0.39) −0.01 (0.01) −0.72 (0.53) 3.17*** (0.45) 0.35*** (0.13) 1.86*** (0.36) –

–

AUD

−0.69 (0.49) −0.24 (0.33) 0.19 (0.39) 0.45** (0.18) 0.02 (0.42) −0.31 (0.35) −0.01 (0.02) 0.14 (0.46) −0.55 (0.55) 0.51 (0.43) −0.37 (0.43) −0.37 (0.50) −0.42 (0.45) 0.04 (0.24)

β2

Predictor: BDI24 Panel A: In-sample analysis h-month ahead 3 β1

−0.45

0.81

−0.16

8.05

2.29

14.44

−0.04

−0.43

3.54

1.09

2.79

−0.12

0.44

0.34

R2(%) −2.53*** (0.72) −1.23*** (0.46) −0.09 (0.55) 0.88*** (0.27) −0.59 (0.58) −1.83*** (0.5) 0.01 (0.02) −0.11 (0.65) −1.91*** (0.66) −0.22 (0.59) −1.59*** (0.59) −2.33*** (0.74) −2.13*** (0.67) −0.33 (0.32)

6 β1

1.90*** (0.63) 0.23 (0.21)

1.99*** (0.46) 1.53** (0.66) −0.02 (0.13) 2.89*** (0.66) 2.99*** (0.46) −0.01 (0.01) 0.21 (0.50) 5.19*** (0.41) 0.53*** (0.11) 2.97*** (0.37) –

–

β2

0.04

5.68

3.08

19.11

7.03

37.1

−0.66

−0.43

14.06

7.34

5.68

1.24

7.29

3.90

R2(%) −4.50*** (1.04) −2.36*** (0.6) −1.50* (0.78) 1.94*** (0.48) −1.75** (0.87) −3.26*** (0.71) 0.11*** (0.02) −0.42 (0.90) −3.02*** (0.82) −1.78** (0.75) −3.63*** (0.79) −4.85*** (1.10) −4.78*** (0.95) −0.86* (0.48)

12 β1

0.76 (0.61) −0.37* (0.19)

4.72*** (0.55) 0.60 (0.78) −0.15 (0.14) 2.59*** (0.9) 2.14*** (0.41) −0.02*** (0.01) 1.45*** (0.44) 5.99*** (0.36) 0.5*** (0.08) 3.99*** (0.39) –

–

β2

2.12

8.15

6.39

29.14

13.24

52.72

3.18

8.27

12.07

4.35

9.31

0.97

23.10

6.12

R2(%) −4.41*** (1.26) −2.18*** (0.79) −2.47*** (0.94) 0.34 (0.71) −2.28** (1.02) −4.31*** (0.86) 0.21*** (0.03) −1.23 (1.15) −2.96*** (0.99) −2.74*** (0.87) −2.86*** (0.91) −5.40*** (1.35) −5.80*** (1.11) −0.67 (0.59)

18 β1

−0.22 (0.56) −0.73*** (0.18)

2.90*** (0.53) −0.91 (0.74) 0.23 (0.18) −0.66 (0.89) 1.77*** (0.37) −0.02*** (0.00) 2.21*** (0.42) 5.54*** (0.34) 0.42*** (0.07) 3.7*** (0.34) –

–

β2

6.81

9.09

5.38

31.44

14.04

51.49

8.69

18.63

12.24

1.64

1.05

2.15

10.73

4.07

R2(%)

−2.57* (1.51) −0.93 (0.92) −2.74** (1.12) −1.27 (0.81) −1.81 (1.21) −4.22*** (0.99) 0.22*** (0.03) −2.84** (1.32) −1.63 (1.12) −1.96** (0.95) −1.11 (1.06) −3.73** (1.58) −4.41*** (1.34) 0.02 (0.72)

24 β1

8.99

3.86

1.74

36.27

12.62

52.44

14.39

21.22

12.28

0.23

5.47

1.70

14.81

0.72

R2(%)

(continued on next page)

−0.50 (0.58) −0.87*** (0.17)

4.04*** (0.59) −0.82 (0.78) 0.65*** (0.21) −0.45 (1.00) 1.83*** (0.35) −0.02*** (0.00) 2.64*** (0.39) 5.56*** (0.33) 0.38*** (0.06) 4.20*** (0.34) –

–

β2

Table 2 Individual time-series predictive regression Panel A reports results of in-sample regressions based on Eq. (4). The estimated coefficients associated with predictor variable (the inflation differentials) are presented in columns 2, 5, 8, 11, and 14 (3, 6, 9, 12 and 15). Adjusted R-squared statistics are presented in columns 4, 7, 10, 13 and 16. Standard errors are reported in parenthesis. To save space, constants are left unreported. Panel B reports results of out-of-sample forecasts based on Eq. (4). Note that we exclude control variables. Results contain two forecast evaluation statistics: out-of-sample R-square and MSFE-adjusted, and all forecasts are evaluated with random walk with drift as benchmark. The out-of-sample period is composed of three quarters of full sample. *, **, *** indicate significance level at 10%, 5% and 1%, respectively.

L. Han, et al.

Finance Research Letters xxx (xxxx) xxx–xxx

2 ROS (%)

AUD CAD CHF CNY EUR GBP HKD JPY KRW MXN NOK NZD SEK SGD

−0.73 −1.06 −1.74 −0.60 −1.85 −3.05 −1.51 −1.59 −2.53 −0.49 −1.05 −1.38 −1.15 −3.47

Panel B: Out-of-sample analysis h-month ahead 3

Table 2 (continued)

0.30 −0.73 0.08 1.89** −1.80 −0.85 −0.55 −0.53 −0.53 0.33 −1.04 −0.40 −1.10 0.31

MSFE-adjusted 2.99 0.56 −1.59 −1.36 −4.14 −0.79 −1.39 −5.11 −2.23 −1.70 −0.18 2.32 1.58 −4.73

2 ROS (%)

6

2.45*** 1.58* −0.23 2.02** −0.13 1.25 0.64 0.47 1.09 0.11 0.89 2.55*** 2.03** 0.24

MSFE-adjusted 2.59 1.10 0.15 3.68 −3.70 3.03 4.73 −12.69 3.23 −1.58 1.56 1.21 6.08 −6.13

2 ROS (%)

12

2.55*** 2.12** 1.07 3.06*** 1.30* 2.74*** 4.13*** 1.70** 2.76*** 1.30* 2.39*** 3.06*** 4.02*** 1.36*

MSFE-adjusted −0.70 −0.34 −0.51 −2.02 1.12 3.52 12.98 −15.71 3.25 1.96 0.46 −2.42 8.46 −3.74

2 ROS (%)

18

1.97** 1.39* 0.95 1.07 3.20*** 3.28*** 5.85*** 2.04** 2.24** 2.66*** 2.11** 2.29** 5.95*** 1.94**

MSFE-adjusted

−2.00 −1.49 −3.24 −1.68 −4.93 1.86 13.12 −19.79 −2.90 0.22 −1.15 −3.16 3.42 −1.82

2 ROS (%)

24

1.09 0.23 0.28 0.36 1.30* 3.05*** 6.48*** 2.88*** 1.23 2.01** 1.65** 1.66** 2.87*** 1.43*

MSFE-adjusted

L. Han, et al.

Finance Research Letters xxx (xxxx) xxx–xxx

6

Finance Research Letters xxx (xxxx) xxx–xxx

L. Han, et al.

3.2. Time-series predictive regression So far, we have demonstrated that the BDI provides a significant predictability of exchange rates. Among different predictors based on the BDI, the BDI24 perform better. In this subsection, we use BDI24 as an example to examine whether its predictive power is heterogenous across different currencies. In doing so, we repeatedly run time-series regressions of Eq. (4) for individual currencies. The coefficients in Eq. (4) are estimated using ordinary least squares (OLS). Panel A of Table 2 reports the in-sample predictive results for h-month ahead exchange rate returns in a time-series regression setting. Generally, the significant coefficients associated with the BDI24 are negative for major currencies except for CNY and HKD. And the magnitude of significant coefficients seems to vary across different forecast horizons. For example, in the case of AUD, coefficients of the BDI are −2.53, −4.50, −4.41 and −2.57 (from 6- to 24-month ahead), which reach the largest at the 12-month ahead. Also, the estimated coefficients display an inverted U-shaped predictive pattern. Only one currency shows significant coefficient at the 3-month horizon. The coefficients are significant at the level of 10% for 8 out of 14 currencies at the 6-month horizon, while 13 out of 14 currencies show significant coefficients at the 12-month horizon. Also, 11 out of 14 currencies show significant coefficients at the 18-month horizon. 8 out of 14 currencies show significant coefficients at the 24-month horizon. Interestingly, CNY and HKD, as the notable exception, show positive relation with the predictors based on the BDI. China and Chinese Hongkong commonly possess great gain in world trade and with clear potential in cost control. The coefficients associated with the inflation differentials are almost positive in consistent with results of panel regression. Further, out-of-sample regressions are used to reinforce the predictive ability of the BDI as represented in Panel B of Table 2. We generate forecasts of the exchange rates using a recursive (expanding) estimation window, and then evaluate the out-of-sample forecasts with random walk with drift as benchmark. Results in Panel B contain two forecast evaluation statistics: out-of-sample Rsquare (Campbell and Thompson, 2008) and MSFE-adjusted (Clark and West, 2007). The predictive ability of the BDI could be confirmed by the positive R-squares and significant MSFE-adjusted statistics. When considering both statistics simultaneously we also find an inverted U-shaped predictive pattern. No one currency is predictable at the 3-month horizons. At the 6-month horizon, 4 out of 14 currencies are predictable. Although CNY has significant MSFE-adjusted statistics at the 3 and 6-month horizons, its R-square is negative. BDI provides significant out-of-sample forecasts in 9 out of 14 currencies at the 12-month horizon, with the R-squares ranging from 1.10% (CAD) to 6.08% (SEK). At the 18-month horizon, BDI generates significant predictive ability in 7 out of 14 cases, with the highest R-square reaching 12.98% (HKD). 4 out of 14 currencies are predictable at the 24-month horizon in terms of both MSFE-adjusted and out-of-sample R-square statistics. To sum up, the BDI displays significant in-sample and out-of-sample predictive ability for exchange rates in majority of the cases. Besides, the BDI has an inverted U-shaped predictive pattern that peaks at 12-month horizon. 4. Conclusions and discussion The BDI can reflect the freight rate level in the dry bulk shipping market, and is commonly perceived as a leading indicator of economic activity. In this paper, we demonstrate the predictive ability of the BDI is prominent for exchange rate returns. The effect of the BDI is not replaced or diminished by macroeconomic variables such as inflation and interest rate. By using panel regression and individual time-series regressions, we find that: (i) the BDI has a long-run predict ability for exchange rates; (ii) exchange rate returns are correlated negatively with changes in the BDI in the long term, namely, the currency depreciates as the BDI increase; (iii) international evidence suggests that the BDI has an inverted U-shaped predictive pattern that peaks at 12-month forecast horizon; (iv) the three sub-indices of the BDI also show significant predict power with similar patterns; and (v) consistent with in-sample results, the findings based on out-of-sample regressions reinforce the predictive ability of the BDI. Our results reveal that the BDI could carry information about fundamentals which is useful for exchange rate predictability. Our findings open a new direction for future research. Our research could extend to the out-of-sample forecast with considerations of asset allocation exercises. Moreover, an in-depth analysis on the possible information transmission channel between the shipping sectors to foreign exchange market would be insightful. Recall that CNY and HKD, as the notable exception, show positive relations with the BDI predictors. This interesting finding deserves further discussion. References Alizadeh, A.H., Muradoglu, G., 2014. Stock market efficiency and international shipping-market information. J. Int. Financ. Mark. Inst. Money 33, 445–461. Apergis, N., Payne, J., 2013. New evidence on the information and predictive content of the Baltic Dry Index. Int. J. Financ. Stud. 1 (3), 62. Bakshi, G., Panayotov, G., Skoulakis, G., 2012. The Baltic Dry Index as a predictor of global stock returns, commodity returns, and global economic activity. In: AFA 2012 Chicago. Bildirici, M.E., Kayikci, F., Onat, I.S., 2015. Baltic Dry Index as a major economic policy indicator: the relationship with economic growth. In: Zehir, C., Ozdemir, E.E. (Eds.), Proceedings of the 4th International Conference on Leadership, Technology, Innovation and Business Management. 210. pp. 416–424. Campbell, J.Y., Thompson, S.B., 2008. Predicting excess stock returns out of sample: can anything beat the historical average? Rev. Financ. Stud. 21 (4), 1509–1531. Chen, Y.-C., Rogoff, K., 2003. Commodity currencies. J. Int. Econ. 60 (1), 133–160. Christou, C., Gupta, R., Hassapis, C., Suleman, T., 2018. The role of economic uncertainty in forecasting exchange rate returns and realized volatility: evidence from quantile predictive regressions. J. Forecast. 37 (7), 705–719. Clark, T.E., West, K.D., 2007. Approximately normal tests for equal predictive accuracy in nested models. J. Econ. 138 (1), 291–311. Engel, C., West, K.D., 2005. Exchange rates and fundamentals. J. Polit. Econ. 113 (3), 485–517. Ferraro, D., Rogoff, K., Rossi, B., 2015. Can oil prices forecast exchange rates? An empirical analysis of the relationship between commodity prices and exchange rates. J. Int. Money Finance 54, 116–141. Gourinchas, P.O., Rey, H., 2007. International financial adjustment. J. Polit. Econ. 115 (4), 665–703.

7

Finance Research Letters xxx (xxxx) xxx–xxx

L. Han, et al.

Ji, Q., Liu, M.-L., Fan, Y., 2015. Effects of structural oil shocks on output, exchange rate, and inflation in the BRICS countries: a structural vector autoregression approach. Emerg. Mark. Finance Trade 51 (6), 1129–1140. Kilian, L., 2009. Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. Am. Econ. Rev. 99 (3), 1053–1069. Ma, Y.-R., Ji, Q., Pan, J., 2019. Oil financialization and volatility forecast: evidence from multidimensional predictors. J. Forecast. 1–18. Meese, R.A., Rogoff, K., 1983. Empirical exchange rate models of the seventies: do they fit out of sample. J. Int. Econ. 14 (1), 3–24. Papapostolou, N.C., Pouliasis, P.K., Nomikos, N.K., Kyriakou, I., 2016. Shipping investor sentiment and international stock return predictability. Transp. Res. Part E 96, 81–94. Rossi, B., 2013. Exchange rate predictability. J. Econ. Lit. 51 (4), 1063–1119.

8