Intraday Seasonality in Efficiency, Liquidity,
Volatility and Volume: Platinum and Gold Futures inTokyo and New York
Kentaro Iwatsubo
Clinton Watkins
Tao XuNovember 2017
Discussion Paper No.1722

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANIntraday Seasonality in Efficiency, Liquidity, Volatility and Volume:
Platinum and Gold Futures in Tokyo and New York

Kentaro Iwatsubo1, Clinton Watkins and Tao Xu
Kobe University, 2-1 Rokkodai-cho, Nada, Kobe, Japan 657-8501

November 2017

Abstract

We investigate intraday seasonality in, and relationships between, informational
efficiency, volatility, volume and liquidity. Platinum and gold, both traded inoverlapping sessions in Tokyo and New York, provide an interesting comparison
because Tokyo is an internationally important trading venue for platinum but not
for gold. Our analysis indicates that both platinum and gold markets in Tokyo aredominated by uninformed trading, while there is evidence supporting both
uninformed and informed trading in New York. Separating global trading hours
into Tokyo, London and New York day sessions, we also find that uninformed
trading is more prevalent during the Tokyo day session while informed trading
dominates the New York day session for both metals in both locations. Thisevidence suggests that futures markets for the same underlying commodity on
different exchanges have different microstructure characteristics, while bothinformed and uninformed traders choose when to trade depending on market
characteristics in different time zones.

JEL classification: G14, G15, Q02
Key words: intraday patterns, microstructure, efficiency, commodity futures

1

Corresponding author. Email address: iwatsubo@econ.kobe-u.ac.jp
This study is conducted as a part of the Project “Economic and Financial Analysis of
Commodity Markets” undertaken at Research Institute of Economy, Trade and Industry(RIETI). The authors are grateful for helpful comments and suggestions by participants at
ANU-RIET Workshop and the Discussion Paper seminar at RIETI.
1

1. Introduction

Why do multiple exchanges that trade the same commodity exist? A number of futures
exchanges have extended their trading hours to include night sessions, overlapping with eachother. It is now common for different exchanges to trade futures based on the same underlying
commodity at the same time. Arbitrage activity, assisted by the globalisation of commodity
markets and advances in trading technology, encourages commodity futures mid-prices on
different exchanges to be virtually identical after adjusting for contract specifications andexchange rates. A straightforward argument would suggest that market participants prefer totrade on the exchange with superior price discovery, efficiency and liquidity. Therefore, tradein the futures of a particular commodity would be expected to agglomerate to one exchange,
as higher liquidity and scale economies encourage traders to the venue. However, multiple
futures exchanges persist for many commodities.

In this paper, we aim to shed light on why this may be the case. We investigate whether markets
for commodities futures contracts on different exchanges have different microstructure
characteristics. Such differentiated characteristics may be advantageous for certain investors,
and provide a competitive advantage for the exchange. We address this question by estimating
and comparing the intraday seasonality of informational efficiency, volatility, volume andliquidity in platinum and gold futures traded in overlapping sessions on exchanges in Tokyo
and New York.

Platinum and gold futures are traded on the Tokyo Commodity Exchange (TOCOM), while in
New York, platinum futures are listed on the New York Mercantile Exchange (NYMEX) andgold on the Commodity Exchange, Inc. (COMEX). Historically, TOCOM has been an
important global venue for trading platinum futures. In the past, activity in the global market
for platinum has been heavily influenced by the hedging trades of large industrial end
consumers of platinum metal in Japan who access the futures market via TOCOM. Until
recently, the total weight of platinum represented by futures traded on TOCOM far outweighedthat of NYMEX. In 2008 for example, 3.5 million kilograms was traded on TOCOM2, or 4.4
times that of NYMEX. However, annual volume on the Tokyo market has been in long-term
decline, down from over 16 million contracts in 2001 to just over 3.1 million contracts in 2016
2

Refer to http://www.tocom.or.jp/historical/dekidaka.html for TOCOM trading volume.
2

(including both the platinum standard and mini contracts). In 2015 NYMEX was about 2.9
times larger than Tokyo by weight of platinum, and 4.2 times larger in 2016. However, in termsof contract volume, monthly turnover in Tokyo usually exceeds that of New York (see Figure1). The TOCOM contract unit is 500 grams or 16.08 troy ounces of metal for the standard
future and 100 grams for the mini contract, versus the NYMEX standard specification of 50
Troy ounces3. Despite the decline in TOCOM volume, a not insubstantial share of the global
platinum futures trade still occurs on the exchange. Important end users of physical platinum
continue to use TOCOM futures for hedging. Global futures trade in platinum is concentrated
on the two venues TOCOM and NYMEX. This contrasts with gold, for which TOCOM’s
annual futures turnover by weight of metal is small compared to that on COMEX. As also
shown in Figure 1, COMEX gold turnover by number of contracts still dwarfs that on TOCOM
despite the COMEX contract being 100 troy ounces compared with 1 kilogram or about 32.15
troy ounces for the TOCOM standard contract. Gold pricing is considered driven by global risk
and monetary factors, and trading is decentralised (Hauptfleisch et al., 2016). Further, there areno features of the gold business in Tokyo that would suggest the location is particularly
important in the determination of global gold futures prices. Tokyo gold futures trade
represented about 6 percent and 5 percent of COMEX trade by weight in 2015 and 2016,
respectively. Accordingly, platinum and gold futures traded in Tokyo and New York provide
an interesting comparison for the analysis of intraday microstructure patterns.

TOCOM has become a more internationalised market over time. Trade orders originating
outside Japan have been an increasing proportion of total trade on TOCOM since May 2009
after the exchange launched a new trading platform and night session (TOCOM, 2015).
International buy and sell orders make up a substantial portion of both the platinum and gold
trade on TOCOM during our sample period4. Foreign buy and sell trades in the platinum market
made up approximately 36 percent and 45 percent of the total in 2014 and 2015, respectively.
The proportion of foreign transactions in the gold market was higher, with 46 and 51 percent

3

TOCOM contract specifications can be found at
http://www.tocom.or.jp/guide/youkou/platinum.html andhttp://www.tocom.or.jp/guide/youkou/gold.html, NYMEX at
http://www.cmegroup.com/trading/metals/files/platinum-and-palladium-futures-andoptions.pdf, and COMEX at
http://www.cmegroup.com/trading/metals/precious/gold_contractSpecs_futures.html.
4
Data on foreign customer transactions is obtained fromhttp://www.tocom.or.jp/jp/historical/download.html.
3

of both buy and sell trades in 2014 and 2015, respectively. Most foreign orders over this periodoriginated from the United States, Australia, Singapore and Hong Kong.

An important difference between the Tokyo and New York futures markets for both platinum
and gold is the most actively traded maturity. In New York, as with most commodity futures
markets, nearby contract months are the most actively traded, while deferred contract months
tend to be inactively traded. As noted in Kang et al. (2011), platinum and gold in Tokyo are
actively traded in deferred contract months and inactively traded in nearby contract months.
Our analysis uses data for the most liquid contract month for each metal on each exchange.
Accordingly, we use the nearby contract months for platinum and gold in New York, and thedeferred contract months for platinum and gold in Tokyo. Although this introduces a maturity
mismatch, we do not believe this makes a material difference to our analysis. We are interested
in comparing the microstructure characteristics of the most actively traded contract for eachmetal and exchange. Tokyo platinum contract trading volume in the deferred contract exceedsthat of New York in the nearby and the deferred contracts (Kang et al. 2011). Indeed, part ofthe differentiation between exchanges that may be advantageous to a trader transacting inTokyo is the longer horizon on a market with reasonable liquidity.

Tokyo conducts an evening trading session that runs in parallel with most of the New York day
session. New York is also open for trade during the Tokyo day session. Do these markets follow
their own distinct intraday patterns in efficiency, liquidity, volatility and volume, or do they
have a common seasonality? Do relationships between microstructure characteristics suggest
informed 5 or uninformed trading on these exchanges? We estimate a regression model for
intraday seasonality in each microstructure characteristic for each metal on each exchange, anduse the estimates to investigate the extent to which the markets on each exchange follow acommon intraday seasonal pattern. We also analyse the intraday relationships between
5

We differentiate between informed and uninformed traders as is typical in microstructure
modelling (de Jong and Rindi, 2009). Informed traders use costly private information about
the future value of the asset traded with the aim of transacting for a profit. This information
may be research on the asset's expected future value, knowledge of order flow in the market,
or inside information. Uninformed traders such as liquidity traders, noise traders and hedgers
do not possess such private information. Liquidity traders transact only for liquidity reasons
which are not related directly to the future payoffs of financial assets. Noise traders transact
for reasons neither based on liquidity nor fundamental information. Hedgers trade to mitigate
risks that arise from holding other correlated assets. Uninformed traders, particularly liquidity
traders, may or may not have discretion over the timing of their transactions.
4informational efficiency and return volatility, trading volume and liquidity for indications on
the prevalence and patterns of informed versus uninformed trading in the platinum and gold
markets.

We find similarities in intraday informational efficiency and return volatility patterns between
futures for the same metal traded on different exchanges, and differences in volume andliquidity patterns. Relationships between these patterns suggest that, over global trading hours,
the Tokyo markets for platinum and gold are dominated by uninformed trading, while there isevidence of both uninformed and informed trading in New York. During Tokyo’s daytime
session, the markets for platinum and gold in both Tokyo and New York display uninformed
trading characteristics. Conversely, both markets for both metals have characteristics
consistent with informed trading over the hours of New York’s daytime session. Our analysis
suggests that both informed and uninformed traders choose when to trade depending on market
characteristics in different time zones.

The paper proceeds as follows. In the next section, we summarise relevant literature on intraday
patterns in informational efficiency, volatility, volume and liquidity in financial markets, andthe intraday relationships between informational efficiency and volatility, volume and liquidity.
In section three, we describe our platinum and gold data and the regression model. In section
four, we present and discuss our empirical results, and section five concludes.

2. Review of Previous Research
2.1. Intraday Seasonality
Researchers have long sought to confirm the existence of intraday seasonality in security prices
and explain persistent intraday patterns in market microstructure characteristics such as return
volatility, trading volume and liquidity. Most studies conducted during and after the 1990s
analyse intraday patterns over the daytime trading sessions in equity markets, while few studies
examine those in commodity markets.

Intraday trading volume and return volatility are typically characterised as following a Ushaped pattern in empirical studies. Both volume and volatility tend to be relatively high at
market open, relatively low for most of the trading day, and rise into the close. Equity return
volatility is shown to have a U-shaped pattern over the day in Harris (1986), Lockwood andLinn (1990), McInish and Wood (1990a), Werner and Kleidon (1996) and Abhyankar et al.
5(1997). Similarly, equity trading volume has an intraday U-shaped pattern in Jain and Joh(1988), McInish and Wood (1990b), Brock and Kleidon (1992) and Chan et al. (1995). Intraday
patterns have been described as a reverse-J for some markets, where volume or volatility aheadof the close remains substantially lower than at the open but higher than for the middle of the
trading day. Hussain (2011) reports a reverse J-shaped pattern in DAX index return volatility,
while Harju and Hussain (2011) show the same type of pattern in other European equity indices.
Further, L-shaped patterns have been observed in markets where volume or volatility fails torise at the end of the trading day, such as for trading volume in DAX30 equities (Hussain,2011). Abhyankar et al. (1997) report an M-shaped pattern for trading volume in UK stocks.

Bid-ask spreads, a proxy for market liquidity, have also been shown to exhibit intraday Ushaped or reverse-J patterns. Brock and Kleidon (1992) find U-shaped bid-ask spread patterns
in US equities, while Ahn and Cheung (1999) and Ahn et al. (2002) discover U-shaped patternsin Hong Kong and Japanese equities, respectively. Theissen and Freihube (2001) and Hussain(2011) document reverse-J shaped intraday bid-ask spread patterns in German equities, while
Abhyankar et al. (1997) find the same shape in UK equities. Although McInish and Wood
(1992) provide evidence of a reverse-J pattern in New York Stock Exchange bid-ask spreads,
they describe it as relatively crude.

Less research has been conducted on intraday patterns in commodities and other exchange
traded asset classes. Eaves and Williams (2010), one of the few papers to analyse intraday
patterns in a commodity markets, observe U-shaped intraday volume and L-shaped return
volatility on the Tokyo Grain Exchange. Cyree and Winters (2001) find reverse-J intraday
patterns in return variances and volume in the US Fed Funds market. Their results suggest thispattern is the result of trading stoppages rather than activity clustering around the transactions
informed traders.

Foreign exchange markets trade continuously, and despite being an over-the-counter market,
provide a close analogy in terms of trading hours to the markets we analyse in this paper. The
full TOCOM trading day that we refer to as global trading hours includes the Tokyo day session
plus the night session, and spans the normal working hours of Tokyo, London and much of
New York. Most research on intraday patterns in currencies has focussed on return volatility
and bid-ask spreads, for example, Bollerslev and Domowitz (1993) and Hsieh and Kleidon(1996). Ito and Hashimoto (2006) analyse intraday seasonality in quote revision frequency,
6

trading volume, return volatility and bid-ask spreads for the USD/JPY and EUR/USD exchange
markets over a 24 hour trading day, and describe intraday patterns during Tokyo, London and
New York working hours. They find that quote revision frequency, trading volume and return
volatility co-move, while spreads move in the opposite direction. Contrary to what is normally
expected in equity markets, bid-ask spreads are low when volatility is high. Given that Tokyo
hours overlap with London, and London hours overlap with New York, but New York hours
do not overlap with Tokyo, U-shaped patterns in trading volume exist during Tokyo andLondon working hours, but not New York. There is no increase in activity at the end of New
York working hours. Overlapping business hours appear to boost market activity and interregional transactions.

2.2. Relationships between microstructure characteristics
Researchers have also examined the intraday relationships between market microstructure
characteristics. In particular, patterns of intraday informational efficiency may be correlatedwith intraday patterns in return volatility, trading volume and market liquidity. Theoreticalexplanations in the literature justify both positive and negative signs on these correlations basedon whether transactions in the market are those of informed or uninformed traders. A varietyof empirical evidence has been generated to support both interpretations.

Informational efficiency and return volatility may be related positively or negatively. The
efficient markets hypothesis suggests that return volatility results when new information
randomly hits the market. Volatility indicates the adjustment of prices to new information, andin that sense, is associated with informational efficiency. Alternatively, behaviouralists proposethat volatility cannot be explained exclusively by changes in fundamentals. Noise traderstransact irrationally, which leads to volatility in returns. Empirical studies suggest noise traders
contribute to a substantial portion of volatility in asset price returns, for example, Shiller (1981),
French and Roll (1986) and Schwert (1989). Informational efficiency and returns volatility
should be negatively related if volatility resulting from the activities of noise traders dominates.

There are also two views regarding the relationship between volume and informational
efficiency: the “asymmetric information view” and the “inventory control view”. The
asymmetric information view argues that trades are more informative when trading volume ishigh, while the inventory control view holds that trades are less informative when trading

7

volume is high. Theory admits both possibilities, depending on the posited information
structure.

To understand the asymmetric information view, consider the model of Admati and Pfleiderer(1988). To minimize their losses to informed traders, discretionary liquidity traders prefer totrade when they have little impact on prices. More liquidity trading in a given period
encourages informed traders to transact at the same time as liquidity traders. Competition
among informed traders reduces their total profit, benefiting liquidity traders and encouraging
their further participation. An increase in the number of informed traders contributes to more
informed prices because they cause prices to adjust faster to information. In this situation,
trading volume and the informational efficiency of prices are positively related.

Alternatively, trading volume and efficiency may be negatively related. Uninformed traders
adjust their positions from time to time. Market makers operate in commodity futures markets,and as part of their normal business activities, unavoidably take on positions they desire to shed
immediately. The representative model of the inventory control view, developed by Lyons(1997), relies on hot potato trading – passing unwanted positions from dealer to dealer
following an initial customer order, which reduces the informativeness of prices. Information
aggregation by dealers occurs through signal extraction applied to order flow. The greater thenoise relative to signal, the less effective signal extraction is. Passing hot potato trades increases
the noise in order flow and dilutes informational content. Hence, trading volume and theinformational efficiency of prices are negatively linked.

Theoretical arguments and empirical evidence also relate market liquidity with informational
efficiency. Two views propose alternative signs for the relationship. The “transaction cost view”
of liquidity can be described as the situation where greater market liquidity reduces transactions
costs for informed traders, and their trades contribute to informational efficiency. Illiquidmarkets imply high transactions costs for informed traders and thus are less efficient. Kyle
(1985) develops a model where an increase in liquidity leads informed traders to take more riskon existing information, and provides greater incentives for informed traders to gain moreaccurate information. Recent papers provide empirical support for the view that security
mispricing is greater in illiquid markets (Sadka and Scherbina, 2007; Chordia et al., 2008).
Payne (2003) demonstrates that in the USD/DEM market, high volume and liquidity periods

8are associated with relatively low price response, suggesting volume and liquidity are
positively related to informational efficiency.
Alternatively, the “noise trader view” says that liquidity may be a proxy for uninformed trading
and thus is associated with informational inefficiency. As a representative empirical paper tosupport this view, Tetlock (2007) uses data from short-horizon binary outcome securities
traded in online exchanges to show that the most liquid securities markets exhibit significant
pricing anomalies.
3. Data, Variables and Model
3.1. Data
We use 1-minute intraday bid and ask futures prices and trading volume for platinum and gold
futures contracts. The Tokyo prices for both metals are from TOCOM, while the New York
prices for platinum are from NYMEX and those for gold are from COMEX. The TOCOM data
was purchased directly from the exchange. COMEX and NYMEX data was obtained from
Thomson Reuters. The sample spans 128 trading days from 1 September 2014 to 31 March
20156. We use the most traded contract on each exchange, which is the deferred contract foreach metal on TOCOM and the nearby contract in New York. Transactions are denominatedin Japanese yen on TOCOM, and in U.S. dollars on COMEX and NYMEX.
Our analysis is conducted based on the times of TOCOM’s trading sessions. The TOCOM
daytime trading session begins at 9:00 Japan Standard Time (JST) and ends at 15:15. After abreak, the night session begins at 16:30 and ends at 4:00 the next morning7. We refer to the dayplus the night session as global trading hours, which has a total of 1065 minutes of trading.
Accordingly, we have 1065 one-minute price and volume observations for each trading day or
set of global trading hours. We divide TOCOM’s day and night session into nine non-

6

General financial market conditions during our sample period could be described as typicalfor markets following the global financial and European sovereign debt crises. Market
volatility according to the Chicago Board Options Exchange VIX was elevated at times, but
not extreme, due to news such as the Bank of Japan’s surprise decision to extend its
Qualitative and Quantitative Easing program, weak economic data from Europe and China,
and the snap presidential election in Greece.
7
TOCOM extended its trading hours on 20 September 2016, after the sample period for ourstudy. The day session opens 15 minutes earlier at 08:45 JST, and closes at 15:15. The newnight session is 90 minutes longer, and runs from 16:30 to 05:30 the next day (TOCOM,
2016).
9overlapping time intervals denoted TI1 to TI9. TI1 to TI3 represent TOCOM’s daytime trading
session, and TI4 to TI9 represent TOCOM’s night session. The daytime intervals are 125
minutes in duration, while the night intervals are 115 minutes long. Table 1 shows the JST,
London (GMT) and New York (EST) times for each interval. We adjust for summer time asalso shown in Table 1. We refer to TI1 to TI3 as the Tokyo day session, TI4 to TI6 as the
London day session, and TI7 to TI9 as the New York day session. In total, our sample contains
1152 time intervals, comprising nine intervals per day for 128 trading days. We calculate
observations for the variables discussed in the following section for each of the 1152 timeintervals, and this is the data we use in our linear regression model and for our correlation
analysis.

3.2. Variables
We are interested in comparing market efficiency, volatility, volume and liquidity
characteristics and relationships between the markets in New York and Tokyo. Accordingly,
we construct five relevant variables from our intraday price and volume data for each of thefour futures contracts: TOCOM Platinum, NYMEX Platinum, TOCOM Gold and COMEX
Gold. The five variables are Lo and MacKinlay’s (1988) variance ratio (VR), realised volatility(Vol), trading volume (TV), quoted half-spread (Sp), and Amihud’s (2002) measure ofilliquidity (ILLIQ). The prices used in constructing the variance ratio, realised volatility, spreadand illiquidity are in local currency terms. Fluctuation in the U.S. dollar / Japanese yen
exchange rate means that the variance ratio and realised volatility of a metal will not be equal
across exchanges. The variables are defined as follows.

Lo and MacKinlay (1988) use a ratio of variance estimators to provide evidence against random
walks in stock price formation. They note that an important property of a random walk is thatthe variance of the increments of the random walk is a linear function of the observationinterval of the increments. Returns that do not adhere to this property suggest that prices are
not formed according to a random walk. The distance of Lo and MacKinlay’s (1988) varianceratio from one indicates relatively greater informational inefficiency due to the existence ofeither positive or negative serial correlation in the returns.

We compute the variances of 1-minute and 5-minute continuously compounded (log) returns,
rt, for mid-quote prices as defined below in equations (1) and (2), respectively. The subscript trefers to time in minutes.
10𝑟𝑡 = ln 𝑝𝑡 − ln 𝑝𝑡−1

(1)

𝑟𝑡 (5) = ln 𝑝𝑡 − ln 𝑝𝑡−5(2)

We define our statistic as the absolute value of one minus the variance ratio, since we areinterested in departures from a random walk in either direction, according to the formula inequation (3). The total number of minutes during each time interval, denoted as T, is equal to
125 and 115 minutes for the TOCOM day and night sessions, respectively. The term  isdefined as the mean one-minute return over the time interval. Equation (3) is interpreted as ameasure of inefficiency.
1

𝑉𝑎𝑟[𝑟 (5)]𝑡
𝑉𝑅 = |1 − 5∗𝑉𝑎𝑟[𝑟| = |1 − T−5
]
𝑡

2
∑𝑇
𝑡=5(𝑟𝑡 +𝑟𝑡−1 +⋯+𝑟𝑡−4 −5𝜇)5
∑𝑇 (𝑟 −𝜇)2𝑇−1 𝑡=1 𝑡|(3)Realised volatility (Vol) in each time interval is constructed using returns based on mid-quote
prices to reduce spurious volatility due to bid-ask bounce. We multiply squared one-minutereturns by 1065, representing the total number of minutes in global trading hours. Volatility isinterpreted as daily percentage volatility during the time interval.

1065

𝑉𝑜𝑙 = 100√ 𝑇 ∑𝑇𝑡=1 𝑟𝑡2(4)

Trading volume (TV) represents the average number of contracts traded per minute in eachtime interval. Ct represents the number of contracts traded on the relevant exchange during
each minute t of our sample.
1

TV = T ∑Tt=1 𝐶𝑡(5)

We gauge market liquidity using two different approaches. The quoted half-spread (Sp) is
defined as follows.

1000

𝑝𝑎𝑠𝑘 −𝑝𝑏𝑖𝑑𝑡𝑡
𝑆𝑝 = 2𝑇 ∑𝑇𝑡=1 ((𝑝𝑎𝑠𝑘)+𝑝𝑏𝑖𝑑 )⁄2
𝑡

(6)

𝑡

11

In addition, we construct the measure of illiquidity (ILLIQ) suggested by Amihud (2002), andreferred to as Amihud’s ILLIQ. This measure can be thought of as quantifying the sensitivityof returns to trading volume. The more illiquid a market is, the greater is the impact of aparticular level of trade volume on a security’s return. It is calculated as an average for eachtime interval of the absolute value of 5-minute returns (𝑟𝑡 (5)𝑘 ) divided by 5-minute trading
volume by weight of metal in kilograms (Vk) for futures contract k. Using weight of metal in
the denominator facilitates comparison between New York and Tokyo on an amount of metal
basis. We use 5-minute returns, as quoted prices do not always change within each minute.
5

𝑇/5 |𝑟 (5) |𝐼𝐿𝐿𝐼𝑄 = 106 × 𝑇 ∑𝑘=1 𝑡𝑉 𝑘

(7)𝑘

3.3. Analysing intraday seasonality and microstructure relationships
We employ two approaches to analyse the intraday patterns and relationships between
informational efficiency and volatility, volume and liquidity. Estimates from a linear regression
model for each microstructure variable are used to characterise intraday seasonal patterns ineach futures market, controlling for daily effects. We also examine Pearson correlation
coefficients between the intraday informational efficiency and volatility, volume and liquidity
variables for global trading hours as well as the Tokyo, London and New York day sessions.

We estimate the linear regression model represented by equation (8) for each of the five
microstructure variables defined above: the variance ratio (VR), realized volatility (Vol),
trading volume (TV), quoted half-spread spread (Sp), and Amihud’s ILLIQ (ILLIQ).
𝑦𝑘,𝑖,𝑗 = 𝛼𝑘,1,1 + ∑9𝑖=2 𝛽𝑘,𝑖 𝑇𝐼𝑖 + ∑128
𝑗=2 𝛾𝑘,𝑗 𝐷𝐷𝑗 + 𝜀𝑘,𝑖,𝑗(8)

One regression model is estimated for each microstructure variable and futures contract
combination. The dependent variable yki,j is the microstructure variable for futures contract k
at time i,j, where i refers to the time interval and j to the day. We regress the dependent variable
on an intercept (k,1,1), dummy variables for the time intervals (TIi) for i equal to two to nine
(TI2 to TI9), and daily dummy variables (DDj) for each of the j days in our sample from day
two to day 128.

12

The k,1,1 and k,i estimates are used to represent intraday seasonal patterns in the particular
microstructure variable for contract k. The estimate for k,1,1 represents the first time intervalon the first trading day, and those for the k,i represent the differential to k,1,1 for each timeinterval of the day. The daily dummies are included to account for day effects, which control
for to preclude large shocks from influencing intraday patterns and increasing correlations
between variables. Our results do not change substantially if the daily dummies are omitted
from the model. The model is estimated by ordinary least squares.

4. Empirical Results
4.1. Summary statistics
Summary statistics for each variable are shown in Tables 2.1 and 2.2 for platinum and gold,
respectively. Statistics for the variables related to contracts on TOCOM are denoted “TY”, andthose relating to contracts on the New York exchanges are denoted “NY”. The mean andmedian variance ratio statistics for both platinum and gold are lower in New York than Tokyo,
suggesting that the New York markets are more efficient on average. Realised volatility inTokyo is on average lower than in New York for both metals, however the distribution ofrealised volatility for Tokyo is much more leptokurtic. Tokyo platinum mean and mediantrading volume exceeds those of New York in contract terms, and are substantially morevariable. New York dwarfs Tokyo in trading volume for gold. The average of the bid-ask
spread measures in the platinum markets are similar over the two exchanges, although Tokyo
spreads appear tighter and less variable but with greater likelihood of extreme observations.
The average spread on gold is much higher in Tokyo than in New York. For platinum,
Amihud’s ILLIQ tells a different story to the bid-ask spread, suggesting that the Tokyo market
is notably less liquid than New York, since the measure normalises by trading volume in weightof metal. Both Amihud’s ILLIQ and the spread suggest that gold market liquidity is
substantially greater in New York than Tokyo.

4.2. Intraday Seasonality
Figures 2.1 to 2.5 show the intraday seasonal patterns for each microstructure variable andfutures contract combination implied by the estimates from equation (8). The intraday seasonalpattern estimates are k,1,1 for TI1 and the sum of k,1,1 and the appropriate k,i for TI2 to TI9.
The left pane of each figure shows the estimates for platinum and the right pane shows those13

for gold. Tables A1.1 and A1.2 in the Appendix show the regression estimates, their statisticalsignificance, and the adjusted coefficient of determination for each regression.

The estimates for the variance ratio models (see Figure 2.1) suggest that the first time intervalof the day, TI1, is by far the least informationally efficient period of global trading hours inboth the platinum and gold markets on both exchanges. Inefficiency peaks again in TI4 during
the open of the London day session, albeit at a lower level than at the beginning of the Tokyo
day. In contrast, the open of the New York day session (and immediately prior) is a relatively
efficient time for both platinum and gold on both exchanges. The Tokyo and London day
sessions are similar in that at the open the markets are relatively inefficient and are relatively
more efficient later in their respective sessions. Conversely, the New York day session isdifferent in that the market is relatively efficient at the open and is less efficient later in thesession. Over global trading hours, the evolution of the variance ratio model estimates loosely
resemble a W shape for each of the markets. Inefficiency rises at the end of the trading day inall of the markets, particularly in platinum on both the Tokyo and New York exchanges. While
Tokyo appears marginally less efficient than New York in the platinum market over most of
global trading hours, this differential between exchange efficiency is notably greater in the gold
market, particularly after TI1.

Figure 2.2 shows the estimates for the realised volatility models. These peak at the open of theTokyo, London and New York day sessions for each market. Tokyo and New York open (TI1
and TI7, respectively) are the most volatile times. After the open, volatility during the Tokyo
day is relatively low for both metals on both exchanges. Over global trading hours, volatility
follows a U-shaped pattern over TI1 to TI4, and then rises into the New York open and thenfalls in an inverted U-shaped pattern from TI5 to TI9. During the Tokyo, London and New
York day sessions, intraday volatility takes an L-shape in Tokyo hours, a U-shape in London
hours, and declines in a linear fashion through New York hours. Patterns across the two metals
are similar, while volatility is greater on the New York exchanges during most time intervals,
and particularly during the New York day session.

The intraday patterns indicated by the estimates from the trading volume models (see Figure2.3) show greater differentiation across the four metal-exchange combinations than is the casefor the variance ratio and realised volatility. In the markets for both metals, trading volume oneach exchange is concentrated during that exchange’s day session. Most platinum trade in
14

Tokyo occurs soon after the open of the Tokyo trading day during the first time interval (TI1),
when the market is at its least efficient and volatility is at its highest level. Platinum volume inTokyo over the Tokyo day session is greater than the volume in Tokyo during either of the
London or New York day sessions. Similarly, most platinum trade on the New York exchange
occurs during the New York day session. Tokyo trading volume exceeds New York trading
volume from TI1 to TI5, while the opposite is true from TI6 to TI9. During each of the Tokyo
and London day sessions, TOCOM trading volume follows a reverse J-shaped pattern, butduring the New York day session Tokyo trading volume falls off after peaking at the New York
open. Gold trading volume in New York peaks at the New York open, and is at a relatively
high level from TI6 to TI9. New York’s trading volume in gold is far greater than Tokyo’s,
and trading volume in Tokyo fluctuates less over the nine time intervals.

The estimates for the spread regressions, shown in Figure 2.4, broadly trend up over global
trading hours (TI1 to TI9) for platinum and gold in Tokyo. Spreads on platinum in New Yorkare at their lowest during the New York day, while New York gold spreads are relatively stable
over the day. Estimates for the gold spread equations vary noticeably less than for platinum.
Also in the platinum markets, the estimates for the spread models are clearly lower during eachexchange’s day session, and higher otherwise, which makes sense as spreads would be
expected to be lower during higher trading volume periods. The estimates for TOCOM arelower during TI1 to TI3 and higher thereafter, while spreads are lower over TI7 to TI9 for the
New York exchange. Consistent with the relative importance and relative trading volume ofTokyo and New York in the global gold market, the estimates for spread in Tokyo are muchlarger than (about double) those for New York. In contrast, platinum spread model estimatesfor Tokyo are lower than those on New York during the Tokyo day session, while this situation
reverses during the New York day session. During the London day session, platinum spread
estimates for the Tokyo and New York exchanges are about the same.

Our estimates for the ILLIQ models are shown in Figure 2.5 and display more intraday
variation, telling a more interesting story about intraday liquidity than the estimates from the
spread models. The ILLIQ estimates show that each exchange is more liquid during its own
day session. While the Tokyo platinum market is more liquid than New York during the Tokyo
day, from TI5 onward New York is the more liquid market. In contrast, the Tokyo and New
York gold markets start global trading hours at about the same level of liquidity, after which
the Tokyo market becomes more illiquid while the New York market becomes more liquid.
15

4.3. Relationships between microstructure characteristics
Tables 3.1 and 3.2 show the correlations between the variance ratio variable and each of return
volatility, trading volume, spread and ILLIQ, for platinum and gold, respectively. We reverse
the signs of the correlations between the variance ratio and both realised volatility and trading
volume. Accordingly, the results can be read more intuitively as the correlations between
informational efficiency and volatility, and efficiency and volume. No such transformation isrequired to interpret the remaining correlations as being between efficiency and liquidity. The
first two columns of Tables 3.1 and 3.2 refer to correlations between the variables over global
trading hours (TI1 to TI9). These correlations are calculated over 1152 time interval
observations. The subsequent three pairs of columns to the right reflect the Tokyo day session(TI1 to TI3), the London day session (TI4 to TI6) and the New York day session (TI7 to TI9),
which are calculated over 384 time intervals. Correlations in bold are significant at the 10
percent level or less.

All global trading hours correlations for the Tokyo exchange are negative in both the platinum
and gold markets, and all except the correlation between efficiency and volume for gold are
significant. This suggests that over global trading hours, the gold and platinum markets on
TOCOM are dominated by uninformed traders. New York platinum and gold market
correlations for global trading hours provide evidence for both informed and uninformed
trading. Efficiency is negatively correlated with volatility suggesting the transactions of
uninformed traders, likely those with little discretion over when they trade, give rise tovolatility. However, efficiency is positively correlated with volume, as hypothesised under
Admati and Pfleiderer’s (1988) asymmetric information view where informed anddiscretionary uninformed traders prefer to trade together such that they have low market impact.
The correlations between efficiency and the two liquidity measures are not significant.

We find a more nuanced view of the intraday relationships between efficiency and volatility,
volume and liquidity in the correlations for the Tokyo, London and New York day sessions.
Consistent with anecdotal evidence from market participants that firms with large physicalplatinum exposures enter the market during the Tokyo morning to hedge via TOCOM, thecorrelations for both the Tokyo and New York platinum markets support uninformed trader
interpretations during the Tokyo day. Efficiency is significantly negatively correlated withvolatility, volume and the two liquidity measures for platinum traded on TOCOM. Similarly,
all correlations are negative for platinum traded in New York during the Tokyo day. All except
16

the correlation between efficiency and liquidity (spread) are significant. Efficiency is also
negatively and significantly correlated with volatility for gold in both Tokyo and New York,with volume in New York only, and with ILLIQ in Tokyo only. While there is support for
uninformed trading in both metals during the Tokyo day, the evidence for platinum is stronger
than that for gold.

During the London day session, the correlations are less definitive. The correlation between
efficiency and liquidity (ILLIQ) supports a noise trader view for the TOCOM platinum market.
However, in the New York platinum market the correlations with volume and liquidity (ILLIQ)
are both positive and significant, supporting the asymmetric information view and thetransactions cost view, respectively. The correlation with volatility is negative and significant,suggesting uninformed trading. For the gold markets, negative correlations between efficiency
and liquidity (ILLIQ) in Tokyo, and efficiency and volatility in New York, provide some
support for uninformed trading during the London day session.

Our results for the New York day session support the dominance of informed trading for both
metals traded on both exchanges. The evidence is stronger for gold than platinum in terms of
the size of the correlations for both trade in Tokyo and New York, and in terms of the numberof significant correlations for Tokyo. Correlations between efficiency and volatility are positiveand significant over all four markets, and those between efficiency and volume are positiveand significant for platinum and gold in New York, and gold but not platinum in Tokyo. These
correlations suggest that uninformed traders are likely absent from the Tokyo market during
the Tokyo night session when volume and liquidity are low. This concurs with anecdotal
evidence that large hedgers of physical platinum enter the market primarily during the Tokyo
morning. Informed traders appear to trade in Tokyo at night while overlapping with the New
York day, despite the relatively low liquidity compared with the Tokyo day session. They arelikely arbitraging between the two markets during the New York day.

The evidence from the correlation analysis is consistent with the patterns evident in the chartsof the regression estimates. Relatively high variance ratio (Figure 2.1) and realised volatility(Figure 2.2) model estimates occur in TI1 and TI4. This implies informational efficiency isnegatively related to volatility, implying that volatility is the result of the market transactionsof uninformed traders. From around the New York open (TI7) low variance ratio model
estimates are associated with high realised volatility estimates, consistent with the rational
17

adjustment of prices to new information. Taken together, Figures 2.1 and 2.3 suggest efficiency
is negatively related to trading volume during the Tokyo day, particularly for platinum on
TOCOM. TI1 is the busiest trading time for platinum on TOCOM, and also the least efficienttime. By the New York day session, the regression model estimates suggest a positive
relationship between efficiency and volume for both metals on both exchanges. The spread
model estimates (Figure 2.4) do not show a great deal of intraday variation, and accordingly
their relationship with efficiency is less clear than for the preceding variables. However, it
would appear that efficiency and liquidity are negatively related, at least for platinum and gold
on TOCOM over global trading hours, suggesting liquidity is a proxy for uninformed trading.
Similarly, the variance ratio and ILLIQ (Figure 2.5) estimates broadly suggest a negative
relationship between efficiency and liquidity in the Tokyo markets and a positive relationshipin New York over global trading hours.

5. Conclusion
This paper examines the intraday seasonality of informational efficiency, return volatility,
trading volume and market liquidity in the platinum and gold futures markets on exchanges inTokyo and New York using high frequency 1-minute data covering global trading hours from
September 2014 to March 2015. Platinum and gold provide an interesting comparison as Tokyo
is an internationally important centre for platinum trading but not for gold. We also examine
the relationships between market microstructure characteristics to determine whether tradingin these markets is predominantly informed or liquidity driven. The article aims to contributeto the understanding of commodity futures market microstructure, on which there has been
little research to date.

We find the following regularities in intraday market characteristics. Informational inefficiency
in both the platinum and gold markets conforms to a W-shape over global trading hours. The
Tokyo and London open, and later in the New York day, show the highest levels of inefficiency.
Day sessions in Tokyo and London start relatively inefficient and become more efficient. In
the New York day session, the markets start relatively efficient and become inefficient by late
in its session. Volatility also follows a similar pattern for both metals and exchanges. It isrelatively high at the open of the Tokyo, London and New York day sessions, being L-shapedin Tokyo hours, U-shaped in London hours, and declining approximately linearly in New York
hours. In contrast, intraday volume and liquidity patterns differ between the metals and/or
exchanges. Trading volume in platinum is concentrated on each of the Tokyo and New York18

exchanges during their day sessions, while comparatively more platinum volume on TOCOM
occurs through the New York day than NYMEX volume through the Tokyo day. Tokyo
platinum volume shows reasonably clear reverse-J and L-shaped patterns during the Tokyo andLondon day sessions, respectively. Volume in all the markets declines monotonically during
the New York day session. In the gold market, volume builds to a peak at the New York open
on COMEX that dwarfs the volume on TOCOM. Liquidity on each exchange is greatest duringthat exchange's day session, and Amihud's ILLIQ shows Tokyo liquidity deteriorating overglobal trading hours.

Relationships uncovered between informational efficiency and volatility, volume and liquidity
suggest that over global trading hours both the platinum and gold markets in Tokyo aredominated by uninformed trading, while there is evidence supporting both uninformed andinformed trading in New York. During the Tokyo day session, uninformed traders dominate
the platinum and gold markets in both Tokyo and New York. Conversely, during the New Yorkday session, informed traders dominate.

Arbitrage means the intraday patterns of return related market characteristics, such as
efficiency and volatility, remain closely related across exchanges trading the same commodity.
However, market activity measures such as volume and liquidity can differ substantiallybetween the exchanges over the course of global trading hours. The Tokyo and New York
exchanges examined in this research have overlapping trading sessions. Night sessions
supplement the exchanges' daytime trading hours. This overlap of trading influences intraday
microstructure patterns on each exchange over global trading hours, and the absence of an
overlap between day trading sessions at the end of global trading hours also influences
microstructure patterns. The resulting intraday patterns share similarities with the intraday
patterns seen in the continuously traded foreign exchange markets, as identified by Ito andHashimoto (2006). Market participants in different time zones and different geographicallocations have different motivations for trading. Uninformed trading is prevalent during theTokyo day session when most volume goes through Tokyo markets. While during New York
hours, informed trading dominates during the New York exchanges' busiest times. Thisevidence suggests that futures markets for the same underlying commodity on different
exchanges have different microstructure characteristics, while both informed and uninformed
traders choose when to trade depending on market characteristics in different time zones.

19

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22

Figures

Figure 1: Monthly Contract Volume

Figure 2.1: Variance Ratio

Figure 2.2: Realised Volatility

23

Figure 2.3: Trading Volume

Figure 2.4: Spread

Figure 2.5: Illiquidity24

Tables

Table 1: Time Intervals
TimeInterval

Duration(minutes)

SessionDescribed As

TI1
TI2
TI3
TI4
TI5
TI6
TI7
TI8
TI9

125
125
125
115
115
115
115
115
115

Tokyo Day
Tokyo Day
Tokyo Day
London Day
London Day
London Day
New York Day
New York Day
New York Day

Tokyo (JST)London (GMT)

London (DST)(London Summer)

New York (EST)New York (DST)(New York Summer)

Start Time

End Time

Start Time

End Time

Start Time

End Time

Start Time

End Time

Start Time

End Time

09:00:00
11:05:00
13:10:00
16:30:00
18:25:00
20:20:00
22:15:00+00:10:00+02:05:00

11:04:59
13:09:29
15:14:59
18:24:59
20:19:59
22:14:59+00:09:59+02:04:59+03:59:59

0:00:00
02:05:00
04:10:00
07:30:00
09:25:00
11:20:00
13:15:00
15:10:00
17:05:00

02:04:59
04:09:59
06:14:59
09:24:59
11:19:59
13:14:59
15:09:59
17:04:59
18:59:59

01:00:00
03:05:00
05:10:00
08:30:00
10:25:00
12:20:00
14:15:00
16:10:00
18:05:00

03:04:59
05:09:59
07:14:59
10:24:59
12:19:59
14:14:59
16:09:59
18:04:59
19:59:59-19:00:00-21:05:00-23:10:00
02:30:00
04:25:00
06:20:00
08:15:00
10:10:00
12:05:00-21:04:59-23:09:59
01:14:59
04:24:59
06:19:59
08:14:59
10:09:59
12:04:59
13:59:59-20:00:00-22:05:00
00:10:00
03:30:00
05:25:00
07:20:00
09:15:00
11:10:00
13:05:00-22:04:59
00:09:59
02:14:59
05:24:59
07:19:59
09:14:59
11:09:59
13:04:59
14:59:59

25

Table 2.1: Summary Statistics for Platinum
Statistic
MeanMedianMinimum
MaximumStandard
Deviation
Skewness
Kurtosis
Observations

Variance
Ratio
TY
0.252
0.239
0.000
0.965

Variance
Ratio
NY
0.234
0.209
0.000
0.932

Realised
Volatility
TY
0.983
0.872
0.245
9.823

Realised
Volatility
NY
1.096
0.976
0.233
9.152

Trading
VolumeTY
14.22310.4000.287
131.040

Trading
VolumeNY
10.1276.625
0.528
60.278

0.170

0.165

0.628

0.656

13.082

0.644
0.159
1152

0.842
0.567
1152

6.412
73.1001152

4.685
44.1661152

SpreadTY

SpreadNY

Illiquidity
TY

Illiquidity
NY

1.841
1.875
1.077
3.447

1.854
1.802
0.900
3.890

19.142
12.6130.858
180.336

10.6548.603
0.833
52.357

9.471

0.261

0.416

20.105

7.855

2.939
13.860
1152

1.741
3.469
1152

0.109
3.208
1152

0.729
0.830
1152

2.914
12.853
1152

1.255
1.725
1152

SpreadTY

SpreadNY

Illiquidity
TY

Illiquidity
NY

1.277
1.268
1.082
1.755

0.539
0.532
0.442
1.286

4.958
3.377
0.544
63.842

0.843
0.366
0.074
56.556

Table 2.2: Summary Statistics for Gold
Statistic
MeanMedianMinimum
MaximumStandard
Deviation
Skewness
Kurtosis
Observations

Variance
Ratio
TY
0.270
0.256
0.000
0.879

Variance
Ratio
NY
0.225
0.193
0.000
0.907

Realised
Volatility
TY
0.845
0.716
0.229
12.630Realised
Volatility
NY
0.924
0.781
0.192
11.587

Trading
VolumeTY
34.45626.7561.826
219.360

Trading
VolumeNY
125.895
81.2390.328
807.609

0.177

0.169

0.613

0.652

27.120121.566

0.086

0.076

5.587

3.901

0.568
-0.152
1152

1.042
0.960
1152

9.136
146.0101152

5.913
72.775
1152

2.190
7.169
1152

2.024
5.044
1152

1.200
3.590
1152

4.170
29.2491152

4.231
26.0061152

8.833
10.3941152

26

Table 3.1: Correlations between Efficiency and Volume, Volatility and Liquidity for Platinum
Correlation of Efficiency with
Volatility (Realised Volatility)
Volume (Trading Volume)
Liquidity (Spread)
Liquidity (Illiquidity)

All Sessions (Global Trading Hours)
Tokyo
New York-0.151 ***-0.219 ***-0.144 ***0.098 ***
-0.019-0.127 ***0.031-0.087 ***Tokyo Day Session
Tokyo
New York-0.370 ***-0.486 ***-0.223 ***-0.174 ***-0.029-0.120 **-0.144 ***-0.187 ***London Day Session
Tokyo
New York-0.004-0.087 *0.007
0.100 **
-0.056-0.030-0.129 **0.090 *New York Day Session
Tokyo
New York
0.113 **
0.091 *
0.059
0.099 *
-0.013-0.0470.022
0.081

Note: ***, **, and * denote significance of the Pearson correlation coefficient at the 1, 5 and 10 percent levels, respectively.

Table 3.2: Correlations between Efficiency and Volume, Volatility and Liquidity for Gold
Correlation of Efficiency with
Volatility (Realised Volatility)
Volume (Trading Volume)
Liquidity (Spread)
Liquidity (Illiquidity)

All Sessions (Global Trading Hours)
Tokyo
New York-0.104 ***-0.222 ***-0.0410.150 ***
0.026
-0.123 ***0.037-0.116 ***Tokyo Day Session
Tokyo
New York-0.302 ***-0.503 ***-0.069-0.133 ***
0.062
0.065
0.059
-0.108 **Note: ***, **, and * denote significance of the Pearson correlation coefficient at the 1, 5 and 10 percent levels, respectively.

27

London Day Session
Tokyo
New York-0.078-0.170 ***
0.019
0.028
-0.012-0.031-0.024-0.115 **New York Day Session
Tokyo
New York
0.209 ***
0.156 ***
0.118 **
0.163 ***
-0.051-0.0160.013-0.009Appendix

Table A1.1: Estimates for Platinum
Variable

Constant

TI2

TI3

TI4

TI5

TI6

TI7

TI8

TI9

Variance Ratio TY
Variance Ratio NY
Realised Volatility TY
Realised Volatility NY
Trading Volume TY
Trading Volume NY
Spread TY
Spread NY
Illiquidity TY
Illiquidity NY

0.357 ***
0.352 ***
1.430 ***
1.499 ***
35.717 ***5.558 ***
1.540 ***
1.874 ***
4.647 ***
14.749 ***-0.122 ***-0.141 ***-0.796 ***-0.837 ***-23.321 ***-2.818 ***0.097 ***
0.067
3.766 **
3.627 ***

-0.111 ***-0.143 ***-0.761 ***-0.808 ***-16.639 ***-2.144 **0.089 ***
-0.004
1.106
1.529 **

-0.084 ***-0.072 ***-0.313 ***-0.263 ***-20.457 ***2.300 ***
0.309 ***
-0.0307.560 ***
-3.323 ***-0.106 ***-0.136 ***-0.626 ***-0.591 ***-26.708 ***1.067
0.369 ***
0.064
15.287 ***-3.944 ***-0.150 ***-0.155 ***-0.471 ***-0.354 ***-26.488 ***6.199 ***
0.399 ***
0.096 *
18.280 ***-5.746 ***-0.145 ***-0.171 ***-0.206 ***-0.002-22.494 ***17.579 ***0.467 ***
-0.086 *14.129 ***-11.411 ***-0.110 ***-0.123 ***-0.310 ***-0.230 ***-25.967 ***14.574 ***0.428 ***
-0.207 ***23.996 ***-11.087 ***-0.118 ***-0.121 ***-0.519 ***-0.537 ***-31.370 ***4.360 ***
0.557 ***
-0.07446.335 ***-6.501 ***Adjusted
R2
0.053
0.083
0.151
0.196
0.425
0.505
0.489
0.039
0.456
0.391

Note: ***, **, and * denote significance of the Pearson correlation coefficient at the 1, 5 and 10 percent levels, respectively.

Table A1.2: Estimates for Gold
Variable

Constant

TI2

TI3

TI4

TI5

TI6

TI7

TI8

TI9

Variance Ratio TY
Variance Ratio NY
Realised Volatility TY
Realised Volatility NY
Trading Volume TY
Trading Volume NY
Spread TY
Spread NY
Illiquidity TY
Illiquidity NY

0.397 ***
0.381 ***
1.142 ***
1.210 ***
63.094 ***50.712 ***1.207 ***
0.550 ***
1.529 ***
1.513 ***

-0.111 ***-0.188 ***-0.590 ***-0.707 ***-33.170 ***-13.753
0.018 **
-0.018 *0.989 *
-0.298-0.115 ***-0.160 ***-0.568 ***-0.660 ***-24.951 ***-9.2320.010-0.0090.429-0.294-0.075 ***-0.092 ***-0.267 ***-0.247 ***-30.503 ***39.694 ***0.069 ***
0.000
2.429 ***
-0.471-0.164 ***-0.186 ***-0.433 ***-0.447 ***-39.068 ***35.948 ***0.071 ***
0.005
3.488 ***
-0.361-0.178 ***-0.205 ***-0.241 ***-0.152 **-29.068 ***107.688 ***0.084 ***
0.002
3.305 ***
-0.838 *-0.174 ***-0.205 ***-0.0180.162 **
-15.764 ***264.237 ***0.099 ***
-0.016 *2.421 ***
-1.256 ***-0.170 ***-0.195 ***-0.173 **-0.127 *-34.962 ***166.044 ***0.085 ***
-0.028 ***5.219 ***
-1.316 ***-0.156 ***-0.175 ***-0.389 ***-0.391 ***-50.256 ***86.021 ***0.195 ***
-0.028 ***12.584 ***-1.196 **Note: ***, **, and * denote significance of the Pearson correlation coefficient at the 1, 5 and 10 percent levels, respectively.

28

Adjusted
R2
0.093
0.139
0.104
0.172
0.243
0.513
0.421
0.019
0.408
0.007