1

Is RMB a (Truly) International Currency?

——An Evaluation Based on Offshore Foreign Exchange Market Trading

Patterns

Abstract

This article provides a new framework to evaluate the status of RMB

internationalization. It proposes that the trading patterns of a currency in

global foreign exchange (FX) market embody the currency’s position in

the international monetary system. Based on FX trading data provided by

CLS, the article constructs a ranking of major international currencies

including RMB. It finds that RMB shares more similarities in FX trading

patterns with the established global currencies like US dollar and Euro

than with those regional currencies. The article also explores the policy

implications that the new evaluation approach provides.

Keywords: RMB; Foreign Exchange Market; Trading Pattern

JEL Codes: F33; F37; G15

RMB internationalization has become an important target in China’s

international economic policy1. Naturally, how to assess the progress of RMB has

internationalization become a hot topic for government, academics, and market

participants, both inside and outside China. For example, the People’s Bank of China

(PBoC), China’s central bank, started its annual release of RMB Internationalization

Report in 2015. The report focused on the policy measures taken by the central bank

to facilitate RMB internationalization, but it also quoted SWIFT’s data to show

RMB’s market share in global payment currencies. A more direct attempt to assess

the role of RMB in international monetary system is the RMB internationalization

Index (RII) compiled by the International Monetary Institute (IMI), Renmin

University of China. The RII is a comprehensive measure of RMB’s functions as an

international currency and takes into account the share of RMB in the denomination

of international trade and international finance and in the official foreign reserves.

Some financial institutions also launched their own indexes of RMB

internationalization according to business need, among which the Bank of China

Cross-border RMB Index (CRI) and Standard Chartered

1RMB Internationalization was adopted by Chinese government as an official policy at the 2014 Central Economic Working Conference.

But before that, People's Bank of China (PBoC), China’s central bank, had been encouraging the "overseas use of RMB" for several

years.

2

Renminbi Globalisation Index (RGI) are quite typical. Different from the RII, both CRI

and RGI are based on the scale of business activity instead of the market share of RMB

in specific sectors. Besides the explicit indexes of RMB internationalization, there are

also many researches exploring the evaluation framework of RMB internationalization

(e.g. Chen and Hu, 2013; Qiu and He, 2013) or involving the judgement of the status

of RMB in global monetary system (e.g. Li and Liu, 2010; Lee, 2010; Islam and Bashar,

2012; Burdekin, 2014; Batten and Szilagyi, 2013).

While the above measures of RMB internationalization differ in their forms and

results, they share a commonality with other existing methods for assessing the level of

internationalization of currencies: consisting of the volumes or market shares of the

specific currency in the areas reflecting their international currency functions such as

foreign reserves, trade settlement, international payment, international security

issuance, foreign exchange (FX) market, foreign deposit, etc. However, for a new

comer of international monetary club with severely unbalanced performance in the

above areas like RMB, it’s very difficult to achieve a consensus on the composition of

a volumes or market shares-based measure. Moreover, in some occasions the results

of these type of evaluation methods have ambiguous policy implications and can be

misleading.

In this article, we try to contribute to the topic from a new perspective, trading

patterns of currencies in the FX market. Based on the microstructural data of global

FX market provided by CLS, we find that the currencies have similar trading patterns

also have close positions in the international monetary hierarchy. With the structure-

oriented approach, we achieve a ranking of major international currencies that fits well with the acknowledged order of the international monetary system. After

comparing the trading patterns of RMB in the offshore FX market with those of the

CLS settled currencies, we identify the position of RMB in the ranking of

international currencies. We also explore the policy implications that the new

evaluation approach provides us.

This article is closely linked to the literature on financial market microstructure.

While the majority of studies in this strand focus on price dynamics and market

performance, there have been attempts in applying network analysis to identify the

characters of market participants based on market microstructure data (e.g. Cetorelli

and Peristiani, 2013; Schreiber, 2014). Our study makes a further step in this

direction. To the authors’ knowledge, the article is the first empirical study to rank

currencies according to their level of internationalization based on FX trading

patterns. In this aspect, the article also contributes to the pluralism of topics and

methodologies in the field of financial market microstructure.

The article proceeds as follows. Section 1 briefly describes the policy background

of the study. Section 2 presents the conceptual foundation. Section 3 explains

3

the methodology and the data. Section 4 presents the empirical results. Section 5

explores the policy implications of the empirical results. Section 6 concludes this

article and discusses future work.

1. Policy Background

Many researches recognize the development of the RMB offshore market as one

of the core components to internationalize the Chinese currency (e.g. He and McCauley,

2010; Subacchi, 2010; Cheung, 2014). The offshore market is one of the most important

innovations in the development of the modern financial industry as a market where

nonresidents engage in international currency transactions (Zoromé, 2007). For the

internationalization of currency, the development of offshore markets will help

facilitate the expansion of currency circulation and increase investment and financing

channels (Cheung, 2014). For a currency with a home country that doesn’t fully open

her financial sector yet, the offshore market can potentially build the channel for the

circulation of the currency between residents and nonresidents and form an initial

liquidity and pricing system for the overseas use of the currency (He and McCauley,

2010). Thus, the development of RMB offshore markets became a critical part in the

strategy to promote RMB internationalization.

The key idea in the offshore market-based RMB internationalization is to expand

the use of RMB in the perspective of business categories, traders, and locations

through the participation of people and institutions in RMB business and finally

realize that the RMB has the potential to become a prevailing international currency.

Ideally, the process includes three stages:

Stage 1: Acceptance by the economic agents in business excahnges with China;

Stage 2: Acceptance by the economic agents in business exchanges with those

economic agents above;

Stage 3: Acceptance by economic agents without any direct or indirect business or

financial association with China.

A key step in the process is to make those economic agents without any direct or

indirect business or financial association with China willing to accept RMB. It can be

achieved by two means:

(1) Subsiding RMB holders by providing arbitrage opportunities on exchange rates

or interest rates;

4

(2) Enhancing the possibility for those economic agents to acquire the needed

assets from those who accept RMB by providing ample RMB-

denominated goods and assets.

Although offshore RMB business has seen significant growth in FX turnover and

in trade payments in the last decade, there are still some controversies behind the real

driving force behind the achievement. Many scholars believe that the emergence of

Hong Kong, Taiwan and other RMB offshore centers is actually a result of arbitrage

based on RMB appreciation expectation and the interest rate spread between the

offshore market and the onshore market, so the flurishing RMB offshore market is

fragile and misleading (e.g. He, et al., 2011; Yu, 2014). With the fading of RMB

appreciation expectation in 2015, the growth of offshore RMB business seemed to lose

its momentum (Figure 1). So a question here is if RMB offshore market has fostered

“real” demand for RMB instead of being a result of pure speculation. The study tries

to answer the question with an exploration into the trading patterns of the RMB

offshore market.

Figure 1 Total Outstanding of RMB Deposits in Hong Kong (RMB billion)

Data Source: CEIC.

2. Conceptual Foundation

While the attempts to evaluate RMB internationalization proliferate, most of them

are based on the theoretic framework on the functions of international currency. Kenen

0

200

400

600

800

1000

1200

5

(1983) argues that currency internationalization refers to the use of a currency beyond

its national borders and may be used and held outside its territory by its own residents

or nonresidents. Chinn and Frankel (2005) developed a list of the international

functions of internationalized currencies. According to this list, an international

currency can provide residents or nonresidents with the functions of value storage,

exchange medium and accounting units of the function. It can be used for currency

substitution, investment pricing and trade and financial transactions for private

purposes, as well as for official reserves, currency carriers for foreign exchange

intervention, and anchor currencies for pegging the exchange rate.

Table 1 Basic functions of International currencies

Private Use Official Use

Medium of

Exchange

vehicle currency: invoicing

trade and financial transactions,

settlement currency

vehicle currency: intervention currency in

the foreign exchange market

Unit of Account quote currency (denominating

trade and financial transactions)

anchor of the other currency’s exchange

rate

Store of Value

investment currency (portfolio

allocation) or currency substitution

(private dollarization)

reserve currency (international reserves)

Source: Hartmann (1998)

Chinn and Frankel’s list of the functions of internationalized currencies provide

an intuitive strategy to measure the degree of internationalization of a currency by

combining the indicators corresponding to the functions. Actually, many existent “RMB

internationalization indexes” are essentially weighted sum of some indicators of these

functions, e.g. the RII (Table 2) and the RGI. The difference is only in the choice of

indicators and the determination of weights. For example, in the RII the weights of the

three categories of third-class indicators are equal, while in the RGI the weight of each

sub-indicator is inversely proportional to the variance of the sub-indicator’s value. But

the simplicity of the approach is not without its costs. The straightforwardness in its

construction makes its quality or accuracy as a measure of currency internalization

relies critically on the relevance of the sub-indicators with the corresponding currency

functions, which is not always guaranteed. To some extent, each measure of currency

internationalization under this approach is actually a specific definition of the concept

of “internationalization”.

6

Table 2 Indicator System of RMB Internationalization Index by IMI, Renmin University of China

First-class indicators Second-class indicators Third-class indicators

International Pricing and Payment

Function

Trade Proportion of RMB settlement in

world trade

Capital and Finance

Proportion of RMB credit in global

foreign credit

Proportion of RMB security in

global issuance of international

bonds and bills

Proportion of RMB security in

global remaining sum of

international bonds and bills

Proportion of RMB direct

investment in global foreign direct

investment

International Reserve Function Official Foreign Exchange

Reserves

Proportion of RMB foreign

exchange reserves in global foreign

exchange reserves

Source: International Monetary Institute: RMB Internationalization Report 2015, July, 2015.

Obviously, in this situation the “correctness” (or usefulness) of a measure for

international currency evaluation depends on the specific scenario and the purpose of

its user. The problem is, for a relative newcomer of the game field like RMB, which

are often severely imbalanced in their international currency functions, the subtleness

in the above approach might lead to difficulty in translating the policy implications

from the evaluation of results. As an extreme case, if all the trade settled in RMB were

China’s exports, the increase of the indicators corresponding to its proportion in world

trade could mean nothing for the promotion of nonresidents’ use of RMB, even though

the latter seems to be more relevant to the essence of RMB internationalization.

Moreover, this approach is vulnerable to manipulation as the index compilers can

easily skew the results in their favor by changing the weights of sub-indicators.

Furthermore, this type of index system can be highly sensitive to short-term economic

or financial shocks and be volatile. Expansion of international trade or speculative

fevers to hold (but not to trade) RMB-denominated assets can all lead to a deceptive

upswing of the internationalization index. Also the mixture of private use and official

use of RMB in the index might be confusing for the commercial institutions hesitant

on participating in RMB business, as an increase of RMB’s role in official foreign

reserves might not change its acceptability and liquidity in the financial market.

Our evaluation framework in this article tries to mitigate the potential pitfalls of

the traditional approach by focusing on the behavior of economic agents in the FX

7

market, an often-neglected aspect for a currency to carry out its international functions.

As observed by some researches (e.g. Moore and Payne, 2011; Menkhoff, et al., 2013;

Schreiber, 2014), traders in the FX market with different motives, information, and

market liquidity conditions conduct their transactions in different ways. For a specific

currency traded in the FX market, the characteristics of its traders (e.g. motive,

information, financial capability, counterparty network) and market conditions (e.g.

liquidity, transparency, trading costs) is closely linked with its level of

internationalization in term of both functions and market perception (the latter is also

important as it may influence the acceptability of the currency in the market). This

association implies that the currency’s position in the international monetary hierarchy

gets its reflection in its trading patterns in the FX market. Here the “trading pattern” is

a broad concept. It not only includes the characteristics of the trading itself such as time,

location, institution type, but also includes the structural features of the complex

network comprising of the institutions participating in the trading of specific currencies

and the transactions between them.

A reasonable further assumption is that the currencies having close position in the

international monetary hierarchy share some similarities in their trading patterns. So if

we can identify the specific characteristics of currencies’ trading patterns relevant to

their status of internationalization, we will be able to fix the position of a new newcomer

of the intentional monetary system by comparing its trading patterns with those of the

established international currencies. This approach relies more on the market consensus

on the ranking of some reference currencies and thus avoids an explicit definition of

“currency internationalization”.

A feature of this evaluation framework is the relative stableness of its results. Due

to the network effect among the trading parties in the FX market and the fixed cost

required for a market participant to start trading a new currency or with a new

counterparty, the trading patterns of a currency is much more stable than its trading

volumes. For a currency with an intensely fluctuating market expectation like RMB

around 2015, the feature is certainly an advantage. Moreover, for potential overseas

RMB business participants, the status of RMB internationalization measured based on

its trading patterns in the global financial market is more relevant to their concerns.

3. Methodology Framework

Based on the above idea, this study will seek to determine the indicators

characterizing the features of trading patterns for international currencies and identify

their relationship with the role of the related currencies in global monetary hierarchy.

8

3.1 Data sources

The data for the study are from two sources:

1. CLS-settled FX trades in Q2 2015, comprised of 17 currencies2.

2. A survey of CLS settlement members FX trading for currencies not settled by

CLS in Q2 2015, which includes the offshore RMB.

The first source provides trading data of CLS settled currencies in Q2 2015. We

use the data to build and help calibrate our international currency ranking system. The

second source provides data of 17 CLS settled currencies and CNH traded by CLS

members in Q2 2015. We use the data to identify the position of RMB in our

international currency ranking system.

3.2 Indicators

Since each FX transaction involves two currencies, the indicators are currency

pair-based. We use three categories of indicators:

1. Trading volume and value (on a weekly basis)3

(1) Mean, variance, max, min of total trading volume and trading value4

(2) Mean, variance, max, min of the number of institutions participating the

trading of the currency-pair

(3) Mean, variance of trading volume and trading value categorized by institutions

(4) Mean, variance, max, min of trading volume and trading value categorized by

regions

2. Market concentration (on a weekly basis)5

(1) Mean, variance, max, min of market concentration (with both trading volume

and trading value) by trading institutions (CR(5) and Herfindahl index)

(2) Mean, variance, max, min of the number of counterparties of institutions who

have participated the trading of the currency-pair

3. Trading network6

(1) Distribution function of trading institutions with various number of trading

2Specifically AUD, CAD, CHF, DKK, EUR, GBP, ILS, HKD, JPY, KRW, MXN, NOK, NZD, SEK, SGD, USD, ZAR. The

HUF was added as an 18th currency in Q4 2015 and is not included in the analysis.3 See Appendix A for detailed list. 4 The “volume” here is the number of trade tickets/instructions and the “value” is the gross value between counterparties.5 See Appendix A for detailed list. 6 See Appendix B for detailed description.

9

partners

(2) Topological rich-club coefficient of the trading network

(3) Weighted rich-club coefficient of the trading network

(4) Topological clustering coefficient of the trading network

(5) Weighted clustering coefficient of the trading network

(6) Proportion of transactions between high volume trading institutions.

3.3 Procedures

As our framework focuses on the structural features of currency trading, we first

eliminate the scale difference in the values of indicators by data standardization methods. After that we use Principal Component Analysis (PCA) to reduce the

dimension of the parameter space to a manageable number. Then we represent the

difference in trading patterns between currency pairs with Euclidean distance in the

parameter space.

1. Data standardization

Rescaling the values of indicators to remove the differences in unit and scale.

2. Dimensionality reduction7

We use PCA to derive 2 trading pattern parameters from the first category of

indicators, 4 parameters from the second category, and 7 parameters from the third

category.

3. Cluster analysis

Assuming that the vector of trading pattern parameters for currency pair AB after

dimensionality reduction is

.

And that for currency pair CD is

.

Then the distance between AB and CD is

.

Based on this definition of distance, we apply multidimensional scaling (MDS) to

project all the currency pairs into a two-dimensional plane. Then we use connectivity

7See Appendix C for detailed description.

10

based clustering to group the currency pairs according to the similarity in their trading

patterns.

4. Ranking

Different from some researches also focusing on structural characteristics in FX

trading patterns (e.g. Schreiber, 2014), we don’t make any presumption on the

relationship between trading pattern parameters and the status of currency

internationalization. Instead, we try to derive the relationship by fitting the parameters

to an accepted ranking of international currencies and currency pairs.

Since the US dollar is acknowledged as the most “globalized” currency by being

the largest adopted and traded currency globally according to the BIS Triennial surveys

and SWIFT's trade payments, we take USD as the reference point in international

currency ranking system and measure the level of internationalization of a currency by

the distance between it and USD in the abstract space of trading pattern indicators.

Similarly, we take EURUSD as the reference point in currency-pair system.

For a specific currency C, the distance is calculated based on three parameters.

(1) dist.1: the diversification of the trading counterparties, i.e. the number of

currency pairs which include currency C.

(2) dist.2: the distance between the currency pair C-USD and EURUSD.

(3) dist.3: the average distance between each currency pair consisting of C and a

third currency and the corresponding currency pair consisting of USD and the third

currency.

Then the distance between currency C and USD is

dist = λ ∗ dist.1+ dist.2+ dist.3

where λ is a penalty factor.

To determine the value of λ , we consider the feature parameter vector

for a currency pair AiAj. There are k currencies. So the Euclidean

distance between the currency Ai and Aj in the space of trading pattern is

11

If for a third party currency Al , there is no trading between Ai and Al or between Aj and

Al, then 𝑑𝑖𝑙𝑗𝑙

= 0. Based on the data, d¯ij = ∑dij/N = 3.6, so we set the value of λ to be the

multiples of 3.6, separately i.e. 3.6*1, 3.6*2, 3.6*3.

The currencies are then ranked according to the distances between them and USD.

After we calibrate the procedure against the generally acknowledged hierarchy of

international monetary system, we use trading data including CNH to get its position

among the currencies.

4. Analysis and Results

4.1 Trading pattern of CLS settled currencies

This part of analysis is based on the CLS-settled trading data of 17 currencies in

Q2 2015 (4/5/15 – 6/27/15). There are 77 trading currency pairs as shown in Table 3.

Table 3 Trading relationship between CLS settled currencies

Note: a "1" indicates a currency pair settled by CLS.

After dimensionality reduction on feature parameters with PCA, we get the

location of the currency pairs in the plane of trading pattern with MDS (Figure 2). We

can see clearly that EURUSD has a unique location in the graph and has the longest

Currency Code USD AUD CAD CHF DKK EUR GBP HKD ILS

USD 0 1 1 1 1 1 1 1 1

AUD 1 0 1 1 0 1 1 1 0

CAD 1 1 0 1 0 1 1 1 0

CHF 1 1 1 0 1 1 1 1 0

DKK 1 0 0 1 0 1 1 0 0

EUR 1 1 1 1 1 0 1 1 1

GBP 1 1 1 1 1 1 0 1 0

HKD 1 1 1 1 0 1 1 0 0

ILS 1 0 0 0 0 1 0 0 0

JPY 1 1 1 1 1 1 1 1 0

KRW 1 0 0 0 0 0 0 0 0

MXN 1 0 1 0 0 1 1 0 0

NOK 1 0 1 1 0 1 1 0 0

NZD 1 1 1 1 0 1 1 0 0

SEK 1 1 1 1 1 1 1 0 0

SGD 1 1 1 1 0 1 1 0 0

ZAR 1 1 0 0 0 1 1 0 0

12

average distance to all the other CLS settled currencies.

Figure 2 Location of CLS settled currency pairs in the trading pattern parameter space

We calculate the location of each CLS settled currency as the gravitational center

of all the currency pairs containing the currency (Figure 3). The chart shows USD is

not clustered with the CLS settled currencies. Also other considered well established

international currencies such as EUR, GBP, JPY, are closer to USD than the remaining CLS settled currencies.

13

Figure 3 Location of CLS settled currencies in the trading pattern parameter space

Then we calculate the distance between the CLS settled currencies and USD with

different value of λ (Table 4).

Table 4 Distance between CLS settled currencies and USD

Currency Code dist.1 dist.2 dist.3 λ =3.6∗1 λ =3.6∗2 λ =3.6∗3

USD 0 0 0 0 0 0

EUR 1 0 44.6611 52.8479 61.0347 69.2214

GBP 2 39.5152 72.5989 128.4876 144.8612 161.2347

JPY 3 33.8875 70.5493 128.9972 153.5575 178.1178

CAD 4 47.5049 81.8983 162.1503 194.8974 227.6445

CHF 4 53.4423 79.1097 165.2991 198.0462 230.7932

AUD 5 46.2524 79.1344 166.3206 207.2545 248.1883

NZD 6 56.3867 82.9781 188.4854 237.6061 286.7267

SEK 6 59.2378 81.5388 189.8972 239.0179 288.1385

NOK 8 60.1627 79.0302 204.6871 270.1813 335.6754

14

SGD 8 58.5515 83.2592 207.3048 272.799 338.2932

HKD 9 58.9248 82.7756 215.3813 289.0623 362.7432

DKK 10 61.3806 78.3269 221.5752 303.4429 385.3106

KRW 15 61.5003 0 184.3019 307.1034 429.905

ZAR 11 59.7614 80.7614 230.5772 320.6317 410.6862

MXN 12 57.628 69.9565 225.8257 324.067 422.3082

ILS 14 62.1171 63.1959 239.9278 354.5425 469.1573

Based on the above parameters, we get the ranking of the17 CLS settled currencies

with regard to their level of internationalization (Table 5).

Table 5 International currency ranking based on trading patterns determined from CLS settled FX trades

Rank Currency Name Currency Code SWIFT Rank in the same period

1 United States Dollar USD 1

2 Euro EUR 2

3 British Pound GBP 3

4 Japanese Yen JPY 4

5 Canadian Dollar CAD 6

6 Swiss Franc CHF 8

7 Australia Dollar AUD 7

8 New Zealand Dollar NZD 18

9 Swedish Krona SEK 11

10 Norwegian Krone NOK 13

11 Singapore Dollar SGD 12

12 Hong Kong Dollar HKD 9

13 Danish Krone DKK 17

14 South African Rand ZAR 15

15 Mexican Peso MXN 16

16 Israeli New Shekel ILS None*

17 South Korean Won KRW None*

Data source: the SWIFT rank is from SWIFT RMB Tracker, ILS and KRW are not in the SWIFT ranking list.

*: not provided in SWIFT RMB Tracker.

Compared with the SWIFT rank in the same period based on the currencies’

share in global payments, the ranking seems reasonable except for maybe some

currencies in low positions.

15

4.2 Internationalization level of RMB

With the parameters calibrated, we apply the procedures to the survey of trading

data including CNH. Figure 4 shows the location of CNH-currency pairs in the

trading pattern parameter space. In the figure CNH-currency pairs are located at the

periphery of currency-pair cluster. It implies that the trading patterns of CNH-

currency pairs are different from those of other currency pairs, which excludes the

mixing of all the currency pairs in the figure.

Figure 4 Location of CNH-currency pairs in the trading pattern parameter space

With the clustering analysis (Figure 5), we can see the relationship between CNH-

currency pairs and other currency pairs more clearly. Interestingly, although CNH-

currency pairs seem different from CLS settled currency pairs in Figure 4, they are

not clustering together. There are some points to note. First, USDCNH is very close to

the USD-currency pairs involving other high ranking international currency, such as

GBPUSD, USDCAD and EURUSD. Second, EURCNH is close to EURGBP, while

both of them are far from EURUSD. Third, CNH-currency pairs involving China’s

neighbor regions currencies (CNHHKD, CNHJPY) and British Commonwealth

currencies (AUDCNH, GBPCNH) are very close, but they are far from other CNH-

currency pairs. Four, CNH-currency pairs involving Scandinavian currencies

(CNHSEK, DKKCNH) are far from almost all the other currency pairs. So it seems that

CNH-currency pairs as a whole don’t share much similarity.

16

Figure 5 Clustering Analysis of Currency pairs including CNH

Although it’s not so easy to identify the specific sources in the difference in

trading patterns of CNH-currency pairs. We can still find the sign of divergence in

the trading network indexes. Take the weighted rich-club coefficient of the trading

network in the FX market for example. The graphs of the index value of CNH-

currency pairs other than USDCNH and EURCNH show a sharp contrast against the

graphs of most CLS settled currency pairs (Figure 6). The irregular zigzag curves

imply that not only the population of the traders on these CNH-currency pairs is small

but also the business links between them is sparse.

17

Figure 6 Graph of weighted rich-club coefficient for CLS settled currency pairs and CNH-currency

pairs

But if we focus on only the USD-related currency pairs, we find that the USD-

CNH pair is very close to EUR-USD pair (Figure 7). Actually, it’s the closest among

all the currency pairs. It means that the trading pattern between CNH and USD is

very similar to that between EUR and USD.

Figure 7 Location of USDCNH Pair in the trading pattern parameter space

We calculate the location of CNH as the gravitational center of all the currency

18

pairs containing the currency (Figure 8). Again in the chart CNH is located far from

the CLS settled currencies.

Figure 8 Location of CNH in the trading pattern parameter space

Based on the distance between CNH and USD, we get the ranking of CNH with

regard to their level of internationalization (Table 6). With regard to the similarity with

USD trading patterns, RMB ranks relatively high. From this perspective, RMB could

be considered a global currency like Euro or Pound rather than a regional currency like

the Hong Kong Dollar in relative terms.

Table 6 International Currency Ranking Based on Trading Patterns

Rank Currency Name Currency Code SWIFT Rank in the same period

1 United States Dollar USD 1

2 Euro EUR 2

3 British Pound GBP 3

4 Chinese Offshore Renminbi CNH 5

5 Australia Dollar AUD 7

6 Canadian Dollar CAD 6

7 Japanese Yen JPY 4

8 Swiss Franc CHF 8

9 Swedish Krona SEK 11

10 Norwegian Krone NOK 13

11 New Zealand Dollar NZD 18

19

12 Mexican Peso MXN 16

13 Danish Krone DKK 17

14 South African Rand ZAR 15

15 Singapore Dollar SGD 12

16 Hong Kong Dollar HKD 9

17 Israeli New Shekel ILS None*

18 South Korean Won KRW None*

Data source: the SWIFT rank is from SWIFT RMB Tracker, ILS and KRW are not in the SWIFT ranking list.

*: not provided in SWIFT RMB Tracker.

A way to understand the high ranking of RMB lies in the fact that CNH trading

pattern in FX market shows a strong trend of diversification. We use the concept

“Average Distance between Currency Pairs (ADBCP)” to capture the phenomenon.

ADBCP measures the closeness of the currency pairs containing a specific currency in

the space of trading pattern. CNH owns a very large ADBCP, second only to EUR

(Table 7). The large ADBCP implies that CNH is trading with many currencies with

quite different characteristics – a feature of highly internationalized currencies that

play a role of hub in FX market.

Table 7 International Currency Ranking Based on Trading Patterns

Currency code Average distance between currency pairs

USD 12.96

AUD 7.0519

CAD 7.995

CHF 8.8394

EUR 15.64

GBP 8.6277

JPY 6.5875

SEK 8.2447

CNH 12.7939

As a conclusion, the analysis based on FX market trading patterns tends to

support the view that RMB is already a real international currency. At the same time,

RMB shows quite large difference with most CLS settled currencies in trading

patterns. This paradox can be attributed to two reasons: (1) RMB is not a CLS settled

currency; (2) the sparseness of RMB trading data. The fact that RMB doesn’t join an

international multilateral PvP settlement mechanism like CLS not only limits its

trading efficiency, but also twists its trading patterns to some extent. The sparseness

of RMB trading data is another side of the same issue. Since RMB is not a member of

20

CLS settled currencies, we can’t get systematic RMB trading data through the

platform and have to use survey data for our research. The latter is incomparable with

the former in both richness and accuracy. This problem also threatens the robustness

of our results.

5. Policy Implication

Despite the uncertainty in its future, the development of the RMB offshore

market so far is of great importance to RMB internationalization. The RMB offshore

market allows overseas investors to learn RMB and the RMB business. This is a

critical step for the RMB to compete for the position of global currency in a world

dominated by network externalities. Now the development of RMB offshore market

has reached a turning point. When the excess return to RMB holders vanishes with the

RMB appreciation expectation, the competition of RMB with other currencies will

critically rely on its efficiency as an international medium of exchange, which, in turn,

highly depends on the accessibility of international financial infrastructure, especially

payment system.

However, at least in the FX market, the situation is not optimistic. RMB is not

eligible for PvP settlement through a global settlement system. This fact leaves RMB

trading in the global FX market vulnerable to counterparty settlement risk and

subjected to counterparty credit limits, which clearly doesn’t help encourage financial

institutions to participate in RMB business. As our analysis on FX trading patterns

implies, the most impeded is the trading between RMB and those regional currencies

with a low position in international ranking list. But if the target for RMB

internationalization is to become a global currency, RMB must assume the function

of international medium of exchange in the global FX market, which requires safe

and efficient settlement between RMB and not only the established global currencies

like USD and Euro, but also those less illustrious regional currencies. The

cooperation in international payment system has already become a critical link for

RMB internationalization.

6. Future Work

This article provides a preliminary exploration on the connection between

currencies’ positions in international monetary system and their trading patterns in the

FX market. The revealed relationship is applied to the evaluation of the status of

21

RMB internationalization. Although the results are encouraging, there is still a lot of

work to achieve a reliable ranking of international currencies.

We notice some differences between the ranking of international currencies

derived from the CLS-settled FX trade data and that derived from survey data. For

some currencies such as Japanese yen and New Zealand dollar the change of

ranking is quite significant. Although these discrepancies can be partly explained by

the difference in data quality, they still remind us the necessity of further robust

testing. We will check the stability of our results with the CLS-settled FX trades

data in other periods and also FX trades data from other sources.

Another potential improvement is to use more sophisticated tools to identify

the FX trading patterns related to our topic. As PCA itself can’t guarantee the

relevance of the components derived in the context of our study, we will screen the

variables and calibrate the parameters with different clustering methods (e.g. SVM).

Moreover, so far our method to depict FX trading patterns is still indicator-based. In

future we will also explore the possibility to capture the trading patterns directly

from the raw trading data with deep learning techniques.

22

Reference

Batten, J. and P. Szilagyi (2013): "The Internationalisation of the RMB: New Starts, Jumps

and Tipping Points", SWIFT Institute Working Paper, 2012-001.

Cetorelli, N. and S. Peristiani (2013): "Prestigious Stock Exchanges: A Network Analysis of

International Financial Centers", Journal of Banking & Finance, 37, 1543–1551.

Chen, Y. and B. Hu (2013): "Internationalization of the RMB: An Evaluation Framework",

Economic and Political Studies, 5-20.

Cheung, Y. (2014): "The Role of Offshore Financial Centers in the Process of Renminbi

Internationalization", BOFIT Policy Brief, No. 11.

Chinn, M. and J. Frankel (2005): "Will the Euro Eventually Surpass the Dollar as Leading

International Reserve Currency?", NBER Working Paper, No. 11510.

Cohen, B. (1971): The Future of Sterling as an International Currency, London: Macmillan.

Craig, R, C Hua, P. Ng and R. Yuen (2013): "Chinese Capital Account Liberalization and the

Internationalization of the RMB", IMF Working Paper, WP/13/268.

Hartmann, P. (1998): Currency Competition and Foreign Exchange Markets: The Dollar. the

Yen and the Euro, New York: Cambridge University Press.

He, D. and R. McCauley (2010): "Offshore Markets for the Domestic Currency: Monetary

and Financial Stability Issues", BIS Working Papers, No 320.

He, F., B. Zhang, M. Zhang, Q. Xu,and L. Zheng (2011): "The Current Situation, Prospects,

Problems and Risks of Hong Kong Offshore RMB Financial Markets", International

Economic Review, No.3 (in Chinese).

Islam, M. and O. Bashar (2012):"Internationalization of the Renminbi: Theory and Evidence",

IGS Working Paper Series, No. 04/2012.

Kenen, P. (1983): "The Role of the Dollar as an International Currency", Occasional Papers,

No. 13, Group of Thirty, New York.

Lee, J. (2010): "Will the Renminbi Emerge as an International Reserve Currency", in J. Sachs,

M. Kawai, J. Lee, and W. Woo (eds.), The Future Global Reserve System: An Asian

Perspective, Manila: Asian Development Bank.

Li, D.and L. Liu (2010): “RMB Internationalization: Empirical and Policy Analysis”, in W.

Peng and C. Shu (eds.), Currency Internationalization, Global Experiences, and

Implications for the Renminbi, 167-185, New York: Palgrave Macmillan.

Menkhoff, L., L. Sarno, M. Schmeling and A. Schrimpf (2013): "Information Flows in

Foreign Exchange Markets: Dissecting Customer Currency Trades", BIS Working

Papers, No. 405.

Moore, M. and R. Payne (2011), “On the Sources of Private Information in FX markets”,

Journal of Banking and Finance, 35, 1250-1262.

23

Qiu, X. and T. He (2013): "Quantifying the Prospects and Impacts of RMB

Internationalization", 7th "China Goes Global" Conference.

Schreiber, B. (2014): "Identifying Speculators in the FX Market: A Microstructure Approach",

Journal of Economics and Business, 73, 97-119.

Subacchi, P. (2010): "'One Currency, Two Systems': China's Renminbi Strategy", Chatham

House Briefing Paper, October.

Yu, Y. (2014): "How Far Can Renminbi Internationalization Go?", ADBI Working Paper No.

461.

Zoromé, A. (2007): "Concept of Offshore Financial Centers: In Search of an Operational",

IMF Working Paper, WP/07/87.

24

Appendix A List of indicators for trading scale and market concentration

A1 Trading volume and value

(1)Mean, variance, max, min of total trading volume and trading value

-Trading Value

-Trading Volume

(2) Mean, variance, max, min of the number of institutions participating the

trading of the currency-pair

-The Number of trading participants

(3) Mean, variance, max, min of trading volume and trading value categorized by

institutions

-Trading Value by Bank

-Trading Value by Corporate

-Trading Value by Fund

-Trading Value by Non-Bank Financial Institutions

-Trading Volume by Bank

-Trading Volume by Corporate

-Trading Volume by Fund

-Trading Volume by Non-Bank Financial Institutions

(4) Mean, variance, max, min of trading volume and trading value categorized by

regions

-Trading Value in Americas

-Trading Value in APAC

-Trading Value in EMEA

-Trading Volume in Americas

-Trading Volume in APAC

-Trading Volume in EMEA

-Trading Value in the UK

-Trading Value in the US

-Trading Volume in the UK

-Trading Volume in the US

25

A2 Market concentration

(1) Mean, variance, max, min of market concentration (with both trading volume

and trading value) by trading institutions (CR(5) and Herfindahl index)

-Trading Value Herfindahl Index

-Trading Value Market Concentration (CR5)

-Trading Volume Herfindahl Index

-Trading Volume Market Concentration (CR5)

(2) Mean, variance, max, min of the number of counterparties of institutions who

have ever participated the trading of the currency-pair

-The number of trading party pairs

26

Appendix B Definition of Network indicators

B1. Notation

Matrix A = {𝑎𝑖𝑗} depicts the trading relationship between financial institutions Q

participating the trading of a currency pair, where

𝑎𝑖𝑗 = {1, 𝑖𝑓 𝑖𝑛𝑠𝑡𝑖𝑡𝑖𝑡𝑖𝑜𝑛 𝑖 ℎ𝑎𝑣𝑒 𝑡𝑟𝑎𝑑𝑒𝑑 𝑤𝑖𝑡ℎ 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛 𝑗 𝑖𝑛 𝑡ℎ𝑒 𝑤𝑒𝑒𝑘

0, 𝑖𝑓 𝑖𝑛𝑠𝑡𝑖𝑡𝑢𝑡𝑖𝑜𝑛 𝑖 𝑑𝑖𝑑𝑛′𝑡𝑡𝑟𝑎𝑑𝑒 𝑤𝑖𝑡ℎ 𝑖𝑛𝑠𝑡𝑖𝑡𝑖𝑜𝑛 𝑗 𝑖𝑛 𝑡ℎ𝑒 𝑤𝑒𝑒𝑘

N represents the total number of the financial institutions participating the trading of

the currency pair.

𝑄1 𝑄2 𝑄3 … 𝑄𝑖 … 𝑄𝑁

𝑄1 𝑎11 𝑎12 𝑎13 … 𝑎1𝑖 … 𝑎1𝑁

𝑄2 𝑎21 𝑎22 𝑎23 … 𝑎2𝑖 … 𝑎2𝑁

𝑄3 𝑎31 𝑎32 𝑎33 … 𝑎3𝑖 … 𝑎3𝑁

… … … … … … … …𝑄𝑖 𝑎𝑖1 𝑎𝑖2 𝑎𝑖3 … 𝑎𝑖𝑖 … 𝑎𝑖𝑁

… … … … … … … …𝑄𝑁 𝑎𝑁1 𝑎𝑁2 𝑎𝑁3 … 𝑎𝑁𝑖 … 𝑎𝑁𝑁

𝑤𝑖𝑗 represents the trading value between institution Q𝑖 and institution Q𝑗. 𝑤𝑖𝑗 = 0

when i = j.

𝑄1 𝑄2 𝑄3 … 𝑄𝑖 … 𝑄𝑁

𝑄1 𝑤11 𝑤12 𝑤13 … 𝑤1𝑖 … 𝑤1𝑁

𝑄2 𝑤21 𝑤22 𝑤23 … 𝑤2𝑖 … 𝑤2𝑁

𝑄3 𝑤31 𝑤32 𝑤33 … 𝑤3𝑖 … 𝑤3𝑁

… … … … … … … …𝑄𝑖 𝑤𝑖1 𝑤𝑖2 𝑤𝑖3 … 𝑤𝑖𝑖 … 𝑤𝑖𝑁

… … … … … … … …𝑄𝑁 𝑤𝑁1 𝑤𝑁2 𝑤𝑁3 … 𝑤𝑁𝑖 … 𝑤𝑁𝑁

For a specific financial institution Q𝑖,

𝑑𝑖 = ∑ 𝑎𝑖𝑗𝑗

𝑤𝑖 = ∑ 𝑤𝑖𝑗𝑗

And

𝑊 = ∑ 𝑤𝑖𝑖

27

𝑁>𝑠 represents the number of institutions with 𝑑𝑖 > 𝑠, i.e., the number of the elements

in the set A(Q𝑖) = {Q𝑖|𝑑𝑖 > 𝑠}, and

𝐸>𝑠 = ∑ 𝑎𝑖𝑗 , 𝑖, 𝑗 ∈ A(Q𝑖)

𝑊>𝑠 = ∑ 𝑤𝑖𝑗 , 𝑖, 𝑗 ∈ 𝐴(𝑄𝑖)

𝑁>𝑟 represents the number of institutions with 𝑤𝑖 > 𝑟, i.e., the number of the elements

in the set B(Q𝑖) = {Q𝑖|𝑤𝑖 > 𝑟}, and

𝐸>𝑟 = ∑ 𝑎𝑖𝑗 , 𝑖, 𝑗 ∈ B(Q𝑖)

𝑊>𝑟 = ∑ 𝑤𝑖𝑗 , 𝑖, 𝑗 ∈ 𝐵(𝑖Q𝑖)

𝑊𝑙,𝑟𝑎𝑛𝑘 represents the 𝑙th element in a rank of 𝑤𝑖, in which the largest 𝑤𝑖 ranks first

and the smallest 𝑤𝑖 in the end.

B2. Indicators

(1) Distribution function of institutions with various number of trading partners

P(𝑠) = 1 −𝑁>𝑠

2𝑁

-the α, t, and 𝑅2 for the regression

𝑃(𝑥) = α ∙ 𝑥 + C

(2) Topological rich-club coefficient of the trading network

φ(𝑠) =𝐸>𝑠

𝑁>𝑠(𝑁>𝑠 − 1)

-the α, t, and 𝑅2 for the regression

𝜑(𝑥) = α ∙ 𝑥 + C

(3) Weighted rich-club coefficient of the trading network

∅(𝑟) =𝑊>𝑟

∑ 𝑊𝑙,𝑟𝑎𝑛𝑘𝐸>𝑟

𝑙=1⁄

28

-the α, t, and 𝑅2 for the regression

∅(𝑥) = α ∙ 𝑥 + C

(4) Topological clustering coefficient of the trading network

𝑐𝑖 =2

𝑑𝑖(𝑑𝑖 − 1)∑ 𝑎𝑖𝑗

𝑗,𝑘𝑎𝑖𝑘𝑎𝑗𝑘

-Mean and variance of x(𝑐𝑖)

- x(0)、x(1)

where x(𝑐𝑖) =n(𝑐𝑖)

𝑁, and n(𝑐𝑖) represents the number of institution with the parameter value 𝑐𝑖.

(5) Weighted clustering coefficient of the trading network

𝑐𝑖𝑤 =

2

𝑤𝑖(𝑑𝑖 − 1)∑

𝑤𝑖𝑗 + 𝑤𝑖𝑘

2𝑎𝑖𝑗

𝑗,𝑘𝑎𝑖𝑘𝑎𝑗𝑘

-Mean and variance of x(𝑐𝑖𝑤)

- x(0)、x(1)

where x(𝑐𝑖𝑤) =

n(𝑐𝑖𝑤)

𝑁, and n(𝑐𝑖

𝑤) represents the number of institution with the parameter value

𝑐𝑖𝑤.

(6) Proportion of transactions between high value trading institutions

∅(𝑠) =𝑊>𝑠

𝑊

-the α, t, and 𝑅2 for the regression

∅(𝑥) = α ∙ 𝑥 + C

29

Appendix C List of Principal Components of indicators

C1 Trading volume and value

Indictors Principal Component 1 Principal Component 2

1 1_Trading Val Mean -0.113 -4E-04

2 1_Trading Val Sd -0.1053 0.1445

3 1_Trading Val Max -0.1131 0.0221

4 1_Trading Val Min -0.1123 -0.0326

5 3_Trading Val Bank Mean -0.1129 0.0037

6 3_Trading Val Bank Sd -0.1053 0.1464

7 3_Trading Val Bank Max -0.1131 0.0236

8 3_Trading Val Bank Min -0.1123 -0.0297

9 3_Trading Val Fund Mean -0.1089 -0.1024

10 3_Trading Val Fund Sd -0.1052 -0.0262

11 3_Trading Val Fund Max -0.1099 -0.0707

12 3_Trading Val Fund Min -0.1075 -0.1183

13 3_Trading Val Non-Bank Financial Mean -0.097 -0.0621

14 3_Trading Val Non-Bank Financial Sd -0.0817 -0.0218

15 3_Trading Val Non-Bank Financial Max -0.0923 -0.0585

16 3_Trading Val Non-Bank Financial Min -0.1012 -0.0937

17 3_Trading Val Corporate Mean -0.0897 -0.2417

18 3_Trading Val Corporate Sd -0.0771 -0.2439

19 3_Trading Val Corporate Max -0.0795 -0.2523

20 3_Trading Val Corporate Min -0.0923 -0.236

21 4.1_Trading Val Americas Mean -0.1082 -0.0105

22 4.1_Trading Val Americas Sd -0.1094 0.0704

23 4.1_Trading Val Americas Max -0.1088 0.0122

24 4.1_Trading Val Americas Min -0.1071 -0.0336

25 4.1_Trading Val APAC Mean -0.0926 0.2412

26 4.1_Trading Val APAC Sd -0.0719 0.3314

27 4.1_Trading Val APAC Max -0.0894 0.2565

28 4.1_Trading Val APAC Min -0.0979 0.1531

29 4.1_Trading Val EMEA Mean -0.1121 -0.029

30 4.1_Trading Val EMEA Sd -0.1074 0.1029

31 4.1_Trading Val EMEA Max -0.1127 -0.0092

32 4.1_Trading Val EMEA Min -0.1111 -0.0561

33 4.2_Trading Val UK Mean -0.1126 -0.0026

34 4.2_Trading Val UK Sd -0.1035 0.1462

35 4.2_Trading Val UK Max -0.1128 0.0197

36 4.2_Trading Val UK Min -0.1118 -0.0403

30

37 4.2_Trading Val US Mean -0.1122 -0.012

38 4.2_Trading Val US Sd -0.1118 0.0556

39 4.2_Trading Val US Max -0.1126 0.009

40 4.2_Trading Val US Min -0.1118 -0.0343

41 1_Trading Vol Mean -0.1126 0.0144

42 1_Trading Vol Sd -0.112 0.0559

43 1_Trading Vol Max -0.1127 0.0225

44 1_Trading Vol Min -0.1126 0.012

45 2_No of TP Mean -0.1059 -0.0457

46 2_No of TP Sd -0.0932 0.058

47 2_No of TP Max -0.106 -0.0339

48 2_No of TP Min -0.1052 -0.0558

49 3_Trading Vol Bank Mean -0.1126 0.0153

50 3_Trading Vol Bank Sd -0.1119 0.0578

51 3_Trading Vol Bank Max -0.1126 0.0235

52 3_Trading Vol Bank Min -0.1126 0.0132

53 3_Trading Vol Fund Mean -0.1096 -0.0748

54 3_Trading Vol Fund Sd -0.1095 -0.0252

55 3_Trading Vol Fund Max -0.11 -0.0626

56 3_Trading Vol Fund Min -0.1085 -0.0907

57 3_Trading Vol Non-Bank Financial Mean -0.1125 -1E-04

58 3_Trading Vol Non-Bank Financial Sd -0.1111 0.0141

59 3_Trading Vol Non-Bank Financial Max -0.112 -0.0092

60 3_Trading Vol Non-Bank Financial Min -0.1124 -0.0172

61 3_Trading Vol Corporate Mean -0.0984 -0.1946

62 3_Trading Vol Corporate Sd -0.0971 -0.1331

63 3_Trading Vol Corporate Max -0.1001 -0.1901

64 3_Trading Vol Corporate Min -0.0928 -0.2288

65 4.1_Trading Vol Americas Mean -0.1123 -0.0027

66 4.1_Trading Vol Americas Sd -0.1118 0.0091

67 4.1_Trading Vol Americas Max -0.1117 -0.0111

68 4.1_Trading Vol Americas Min -0.112 -0.0075

69 4.1_Trading Vol APAC Mean -0.0996 0.1894

70 4.1_Trading Vol APAC Sd -0.0856 0.266

71 4.1_Trading Vol APAC Max -0.0972 0.2133

72 4.1_Trading Vol APAC Min -0.1033 0.1389

73 4.1_Trading Vol EMEA Mean -0.1124 0.002

74 4.1_Trading Vol EMEA Sd -0.1115 0.0517

75 4.1_Trading Vol EMEA Max -0.1125 0.015

76 4.1_Trading Vol EMEA Min -0.1124 8E-04

77 4.2_Trading Vol UK Mean -0.1125 0.0147

78 4.2_Trading Vol UK Sd -0.1111 0.061

79 4.2_Trading Vol UK Max -0.1124 0.0267

31

80 4.2_Trading Vol UK Min -0.1125 0.0103

81 4.2_Trading Vol US Mean -0.1129 0.0022

82 4.2_Trading Vol US Sd -0.1125 0.0107

83 4.2_Trading Vol US Max -0.1126 -0.0071

84 4.2_Trading Vol US Min -0.1125 -0.0042

85 6_The number of trading party pairs Mean -0.1094 -0.0274

86 6_The number of trading party pairs Sd -0.1058 0.0471

87 6_The number of trading party pairs Max -0.1094 -0.0148

88 6_The number of trading party pairs Min -0.1088 -0.0298

C2 Market concentration

Indictors Principal Component

1

Principal Component

2

Principal Component

3

Principal Component

4

1 5_Trading Val Herfindahl Index Mean -0.2763 -0.266 0.0835 0.0696

2 5_Trading Val Herfindahl Index Sd -0.2586 -0.1016 -0.3296 -0.2559

3 5_Trading Val Herfindahl Index Max -0.2835 -0.2238 -0.0767 -0.0441

4 5_Trading Val Herfindahl Index Min -0.2381 -0.2986 0.249 0.2618

5 5_Trading Val Market Concentration (CR5) Mean -0.276 -0.2326 0.0768 0.0334

6 5_Trading Val Market Concentration (CR5) Sd -0.2172 -0.3331 -0.1769 0.3504

7 5_Trading Val Market Concentration (CR5) Max -0.2626 -0.28 -0.1774 0.1185

8 5_Trading Val Market Concentration (CR5) Min -0.206 0.0582 0.3782 -0.2222

9 5_Trading Vol Herfindahl Index Mean -0.2738 0.2808 0.0805 0.0166

10 5_Trading Vol Herfindahl Index Sd -0.2106 -0.0462 -0.3656 -0.6684

11 5_Trading Vol Herfindahl Index Max -0.2844 0.2351 -0.0067 -0.0739

12 5_Trading Vol Herfindahl Index Min -0.2624 0.2993 0.142 0.1647

13 5_Trading Vol Market Concentration (CR5) Mean -0.2639 0.2896 0.1279 0.0964

14 5_Trading Vol Market Concentration (CR5) Sd -0.2569 0.3224 -0.0357 0.0904

15 5_Trading Vol Market Concentration (CR5) Max -0.2833 0.2086 0.0323 -0.0371

16 5_Trading Vol Market Concentration (CR5) Min 0.0056 -0.2752 0.6525 -0.4164

C3 Trading network

Indictors Principal

Component

1

Principal

Component

2

Principal

Component

3

Principal

Component

4

Principal

Component

5

Principal

Component

6

Principal

Component

7

1 LC_Phi_alpha 0.0037 -0.049 0.3127 -0.4179 -0.3594 -0.0833 0.4454

2 LC_Phi_t_stats_intercept -0.2719 0.2328 0.0152 0.0645 -0.0429 0.0265 -0.0435

3 LC_Phi_t_stats_s -0.2763 0.013 0.0328 0.0201 -0.1127 -0.3416 -0.1758

4 LC_Phi_R_squared -0.098 -0.0715 0.3081 0.1225 -0.0802 -0.6586 -0.3145

32

5 Ps_alpha 0.1669 0.3491 -0.1205 -0.2825 0.0729 -0.1664 -0.1652

6 Ps_t_stats_intercept -0.2779 0.0693 -0.2565 -0.0035 -0.1071 -0.0745 0.0686

7 Ps_t_stats_s -0.2545 -0.0728 0.1997 -0.0518 0.2727 0.0931 -0.0169

8 Ps_R_squared 0.2433 0.2417 0.0728 -0.1973 0.0431 -0.0553 -0.0535

9 UC_Phi_r_alpha -0.1191 -0.0373 0.119 0.0581 0.3992 -0.4357 0.5257

10 UC_Phi_r_t_stats_intercept 0.0418 0.0384 -0.2457 0.3802 0.2743 -0.0905 0.443

11 UC_Phi_r_t_stats_s 0.1995 0.2704 -0.1546 0.06 0.0512 0.0793 0.1247

12 UC_Phi_r_R_squared -0.142 -0.3003 0.2706 -0.0573 -0.3048 0.2359 0.2714

13 UC_Phi_s_alpha -0.1582 -0.2818 0.0591 0.4268 -0.0173 0.1814 -0.1675

14 UC_Phi_s_t_stats_intercept -0.2835 -0.0167 -0.044 -0.1648 0.2348 0.1342 -0.0834

15 UC_Phi_s_t_stats_s 0.2899 0.0207 0.0723 0.1599 -0.2023 -0.1162 0.0725

16 UC_Phi_s_R_squared -0.1221 -0.0124 0.1722 -0.4205 0.45 0.1117 -0.0662

17 r_Mean -0.0574 0.4121 0.314 0.2282 -0.0427 0.1222 0.0133

18 r_Sd -0.2485 0.2652 -0.1167 -0.003 -0.1831 0.0046 0.077

19 r_Max -0.2222 0.1555 -0.2539 -0.0884 -0.2265 -0.0438 0.1234

20 r_Min -0.0076 0.4124 0.3456 0.1878 -0.0513 0.1066 0.0711

21 s_Mean -0.2206 0.2379 0.2966 0.1256 0.1103 0.1704 -0.0122

22 s_Sd -0.302 0.0303 -0.1609 -0.0508 -0.0305 0.0045 0.0077

23 s_Max -0.2765 0.0971 -0.242 -0.0659 -0.1594 -0.0528 0.0414