The Costs of Trading
There is no commission when you trade the Forex markets with TradeStation. However,
there are transactional costs incurred each time you make a trade.
You will notice that there are two exchange rates for each currency pair: the Bid, which is
the rate at which you can Sell; and the Ask, which is the rate at which you can Buy. The
difference is known as the spread, and determines the transactional cost of the trade. Each
currency pair has its own fixed Bid-Ask spread.
Forex Symbol
EURUSD
USDJPY
GBPUSD
USDCHF
USDCAD
AUDUSD
EURGBP
EURJPY
EURCHF
GBPJPY
Currency Pairs
Euro / U.S. Dollar
U.S. Dollar / Japanese Yen
British Pound / U.S. Dollar
U.S. Dollar / Swiss Franc
U.S. Dollar / Canadian Dollar
Australian Dollar / U.S. Dollar
Euro / British Pound
Euro / Japanese Yen
Euro / Swiss Franc
British Pound / Japanese Yen
Typical Spread
3 Pips
3 Pips
4 Pips
5 Pips
5 Pips
5 Pips
5 Pips
5 Pips
5 Pips
10 Pips

The Costs of Trading
There is no commission when you trade the Forex markets with TradeStation. However,
there are transactional costs incurred each time you make a trade.
You will notice that there are two exchange rates for each currency pair: the Bid, which is
the rate at which you can Sell; and the Ask, which is the rate at which you can Buy. The
difference is known as the spread, and determines the transactional cost of the trade. Each
currency pair has its own fixed Bid-Ask spread.
Forex Symbol
EURUSD
USDJPY
GBPUSD
USDCHF
USDCAD
AUDUSD
EURGBP
EURJPY
EURCHF
GBPJPY
Currency Pairs
Euro / U.S. Dollar
U.S. Dollar / Japanese Yen
British Pound / U.S. Dollar
U.S. Dollar / Swiss Franc
U.S. Dollar / Canadian Dollar
Australian Dollar / U.S. Dollar
Euro / British Pound
Euro / Japanese Yen
Euro / Swiss Franc
British Pound / Japanese Yen
Typical Spread
3 Pips
3 Pips
4 Pips
5 Pips
5 Pips
5 Pips
5 Pips
5 Pips
5 Pips
10 Pips

Placing a Trade
Placing a trade in the Forex market is simple. The mechanics of a trade are virtually
identical to those found in the markets you are trading now.
A Forex trade is a trade in which one currency is valued against another.
Symbols to Trade
Forex Symbol
EURUSD
USDJPY
GBPUSD
USDCHF
USDCAD
AUDUSD
EURGBP
EURJPY
EURCHF
GBPJPY
Currency Pairs
Euro / U.S. Dollar
U.S. Dollar / Japanese Yen
British Pound / U.S. Dollar
U.S. Dollar / Swiss Franc
U.S. Dollar / Canadian Dollar
Australian Dollar / U.S. Dollar
Euro / British Pound
Euro / Japanese Yen
Euro / Swiss Franc
British Pound / Japanese Yen
Terminology
Euro
Dollar-Yen
Sterling
Dollar-Swiss
Dollar-Canada
Aussie
Euro-Sterling
Euro-Yen
Euro-Swiss
Sterling-Yen
The symbol for each Forex contract is based on the two currencies:
EURUSD = Euro Dollar vs. US Dollar.
Also, each Forex contract is a price for the first currency in the symbol name, quoted in
the second currency in the symbol name. In the case of the Euro vs. US Dollar, where the
exchange rate is approximately $1.30, it would take USD 1.30 to purchase 1.00 Euro.
Each Forex contract covers a fixed number of units of the first symbol in the symbol
name, usually 100,000.
Forex exchange rate prices move in fixed minimum price movements called pips. A pip
is the minimum price move an exchange rate can make.

Closing out a Position
An open position is one that is live and ongoing. As long as the position is open, its value
will fluctuate in accordance with the exchange rate in the market. Any profits and losses
will exist on paper only and will be reflected in your margin account.
To close out your position, you conduct an equal and opposite trade in the same currency
pair. For example, if you have bought (“gone long”) one lot of EURUSD (at the
prevailing offer price) you can close out that position by subsequently selling one
EURUSD lot (at the prevailing bid price).

Understanding Bid & Ask
A Bid is what someone is willing to pay for an asset. The Ask, or offer, is what someone
is willing to accept to sell an asset. As a Forex trader, you can Buy at the Ask and Sell at
the Bid.
TradeStation Forex Quote from RadarScreen.
Understanding the Forex exchange quote system is essential to Forex trading. You need
to remember that the first currency listed in the symbol is the base currency, and the
quote is the base currency in terms of the second currency in the symbol.
Major currency pairs are EUR/USD, GBP/USD, USD/JPY, and USD/CHF.
For example:
1.) A price quote of EURUSD at 1.3085 means that one Euro is equal to 1.3085 U.S.
Dollars. When that number increases, it means the Euro is appreciating while the U.S.
Dollar is depreciating and vice versa.
2.) USDJPY is trading at 124.00. It means 1 U.S. Dollar is equal to 124 Japanese Yen.
An increase in the number means that the U.S. Dollar is appreciating while the Japanese
Yen is depreciating, and vice versa.
Again, if a currency quote goes higher, that increases the value of the base currency. A
lower quote means the base currency is weakening.
Cross currency pairs are currency pairs that do not involve the U.S. dollar.
For example:
3.) With EURJPY at a price of 126.34, it means that 1 Euro is equal to 126.34 Japanese
Yen.

Placing a Trade
Placing a trade in the Forex market is simple. The mechanics of a trade are virtually
identical to those found in the markets you are trading now.
A Forex trade is a trade in which one currency is valued against another.
Symbols to Trade
Forex Symbol
EURUSD
USDJPY
GBPUSD
USDCHF
USDCAD
AUDUSD
EURGBP
EURJPY
EURCHF
GBPJPY
Currency Pairs
Euro / U.S. Dollar
U.S. Dollar / Japanese Yen
British Pound / U.S. Dollar
U.S. Dollar / Swiss Franc
U.S. Dollar / Canadian Dollar
Australian Dollar / U.S. Dollar
Euro / British Pound
Euro / Japanese Yen
Euro / Swiss Franc
British Pound / Japanese Yen
Terminology
Euro
Dollar-Yen
Sterling
Dollar-Swiss
Dollar-Canada
Aussie
Euro-Sterling
Euro-Yen
Euro-Swiss
Sterling-Yen
The symbol for each Forex contract is based on the two currencies:
EURUSD = Euro Dollar vs. US Dollar.
Also, each Forex contract is a price for the first currency in the symbol name, quoted in
the second currency in the symbol name. In the case of the Euro vs. US Dollar, where the
exchange rate is approximately $1.30, it would take USD 1.30 to purchase 1.00 Euro.
Each Forex contract covers a fixed number of units of the first symbol in the symbol
name, usually 100,000.
Forex exchange rate prices move in fixed minimum price movements called pips. A pip
is the minimum price move an exchange rate can make.

Closing out a Position
An open position is one that is live and ongoing. As long as the position is open, its value
will fluctuate in accordance with the exchange rate in the market. Any profits and losses
will exist on paper only and will be reflected in your margin account.
To close out your position, you conduct an equal and opposite trade in the same currency
pair. For example, if you have bought (“gone long”) one lot of EURUSD (at the
prevailing offer price) you can close out that position by subsequently selling one
EURUSD lot (at the prevailing bid price).

Understanding Bid & Ask
A Bid is what someone is willing to pay for an asset. The Ask, or offer, is what someone
is willing to accept to sell an asset. As a Forex trader, you can Buy at the Ask and Sell at
the Bid.
TradeStation Forex Quote from RadarScreen.
Understanding the Forex exchange quote system is essential to Forex trading. You need
to remember that the first currency listed in the symbol is the base currency, and the
quote is the base currency in terms of the second currency in the symbol.
Major currency pairs are EUR/USD, GBP/USD, USD/JPY, and USD/CHF.
For example:
1.) A price quote of EURUSD at 1.3085 means that one Euro is equal to 1.3085 U.S.
Dollars. When that number increases, it means the Euro is appreciating while the U.S.
Dollar is depreciating and vice versa.
2.) USDJPY is trading at 124.00. It means 1 U.S. Dollar is equal to 124 Japanese Yen.
An increase in the number means that the U.S. Dollar is appreciating while the Japanese
Yen is depreciating, and vice versa.
Again, if a currency quote goes higher, that increases the value of the base currency. A
lower quote means the base currency is weakening.
Cross currency pairs are currency pairs that do not involve the U.S. dollar.
For example:
3.) With EURJPY at a price of 126.34, it means that 1 Euro is equal to 126.34 Japanese
Yen.

Benefits of Trading Forex

Benefits of Trading Forex
ZERO Commissions
When trading Forex with TradeStation, you pay NO commissions and NO data exchange
fees. The cost of trading Forex is determined by the amount derived by the dealers and
other third parties from the Bid-Ask spread.
24 Hour Market Action
The Forex currency market is a seamless 24-hour market.
Leverage
Forex trading often allows borrowing leverage up to 100 times your account value.
Remember that while leverage can help build profits quickly, it can also produce large,
catastrophic losses.
High Liquidity
The Forex market, with an average trading volume of over $1.3 trillion per day, is the
most liquid market in the world. This means that a trader can usually enter or exit the
market at will in almost any market condition usually without regard to trade size
limitations.

Benefits of Trading Forex

Benefits of Trading Forex
ZERO Commissions
When trading Forex with TradeStation, you pay NO commissions and NO data exchange
fees. The cost of trading Forex is determined by the amount derived by the dealers and
other third parties from the Bid-Ask spread.
24 Hour Market Action
The Forex currency market is a seamless 24-hour market.
Leverage
Forex trading often allows borrowing leverage up to 100 times your account value.
Remember that while leverage can help build profits quickly, it can also produce large,
catastrophic losses.
High Liquidity
The Forex market, with an average trading volume of over $1.3 trillion per day, is the
most liquid market in the world. This means that a trader can usually enter or exit the
market at will in almost any market condition usually without regard to trade size
limitations.

CONCLUSIONS
We have assessed the behaviour of the spot foreign exchange
market quotations in terms of volatility, average
spread, and the number of quotations within half-hour
intervals, as well as certain informational aspects of these
processes. It seems that a log-linear relationship among
these three processes is a considerably better approximation
to the true data generating process functional form, than the
linear one; however, it is by far worse than the functional
form presented here.
A new variable was introduced: the number of observations
within a speciÞc time interval. This variable plays an
important role in the determination of volatility and average
spread, either directly or through the error terms. The
contemporaneous correlation of the number of quotations
and volatility leads us to hypothesize that the former process
could be a proxy for the volume of trade, or for the
number of transactions in the spot FOREX market, for
which data are unavailable. This is in line with studies in
stock market volume and volatility data [see Gallant, Rossi,
and Tauchen (1990), and Lamoureux and Lastrapes (1990)].
It turns out that informational theories can only partially
explain the facts documented here. Although, high trading
and volatility at the opening of markets can be explained
along the lines of the Admati and Pßeiderer (1988) theory,3
the di¤erent behaviour of the two currencies in di¤erent
markets at the same (and di¤erent) time periods points
towards the need to take into account local and currencyspeci
Þc behaviour. The same can be said for the models of
Foster and Viswanathan (1990), Subrahmanyan (1989), and
Son (1991).
An important result of this paper is that the inclusion of
half-hourly dummies, and taking account of simultaneity
between volatility, average spread, and number of quotations,
considerably reduces the GARCH type e¤ects in the
conditional variance of these two exchange rates. What
remains of such GARCH e¤ects can then probably be
attributed to private information and the uncertainty associated
with it.
Finally, having Þtted weekly, daily and half-hour dummies,
we can identify inter- and intra-day patterns of activity,
volatility and average spread. Some of these, for
example, the impact of the Tokyo lunch hour, we have
previously documented. Others are already well known in
markets, for example, the rise in spreads and decline in
activity on Fridays. But we were surprised by the Þnding of
the continuing high volatility, in both currencies, throughout
the period of US market opening, despite steadily falling
activity, which we had expected. Much of the public information
on economic news in the US is released at, or
before, the market opening, so exactly what keeps volatility
so high during the afternoons in the US is a mystery to us

CONCLUSIONS
We have assessed the behaviour of the spot foreign exchange
market quotations in terms of volatility, average
spread, and the number of quotations within half-hour
intervals, as well as certain informational aspects of these
processes. It seems that a log-linear relationship among
these three processes is a considerably better approximation
to the true data generating process functional form, than the
linear one; however, it is by far worse than the functional
form presented here.
A new variable was introduced: the number of observations
within a speciÞc time interval. This variable plays an
important role in the determination of volatility and average
spread, either directly or through the error terms. The
contemporaneous correlation of the number of quotations
and volatility leads us to hypothesize that the former process
could be a proxy for the volume of trade, or for the
number of transactions in the spot FOREX market, for
which data are unavailable. This is in line with studies in
stock market volume and volatility data [see Gallant, Rossi,
and Tauchen (1990), and Lamoureux and Lastrapes (1990)].
It turns out that informational theories can only partially
explain the facts documented here. Although, high trading
and volatility at the opening of markets can be explained
along the lines of the Admati and Pßeiderer (1988) theory,3
the di¤erent behaviour of the two currencies in di¤erent
markets at the same (and di¤erent) time periods points
towards the need to take into account local and currencyspeci
Þc behaviour. The same can be said for the models of
Foster and Viswanathan (1990), Subrahmanyan (1989), and
Son (1991).
An important result of this paper is that the inclusion of
half-hourly dummies, and taking account of simultaneity
between volatility, average spread, and number of quotations,
considerably reduces the GARCH type e¤ects in the
conditional variance of these two exchange rates. What
remains of such GARCH e¤ects can then probably be
attributed to private information and the uncertainty associated
with it.
Finally, having Þtted weekly, daily and half-hour dummies,
we can identify inter- and intra-day patterns of activity,
volatility and average spread. Some of these, for
example, the impact of the Tokyo lunch hour, we have
previously documented. Others are already well known in
markets, for example, the rise in spreads and decline in
activity on Fridays. But we were surprised by the Þnding of
the continuing high volatility, in both currencies, throughout
the period of US market opening, despite steadily falling
activity, which we had expected. Much of the public information
on economic news in the US is released at, or
before, the market opening, so exactly what keeps volatility
so high during the afternoons in the US is a mystery to us

TEMPORAL HALF-HOURLY EFFECTS
The temporal dummies capture events (publicly announced
news releases, market openings and closings) whose timing,though not generally their exact scale, is known in advance.
Public new related to macroeconomic variables is simultaneously
announced to all traders, at a time known in advance
since the scheduled time of all economic related news
is predetermined, and reported on another part of the
Reuters system, the FXNB page. The stochastic element in
such cases is the actual announcement, not the timing of it.
In general, the majority of the US announcements are
around 13:30 hours British Summer Time (BST), and the
German ones around 10:00 hours BST. Consequently, the
relationship between the dummy variables and the characteristics
of interest to us in the market predominantly reßect
response of these variables to publicly known events. Per
contra, the relationship between these variables, after conditioning
on such temporal constants, will primarily reßect
private information to a somewhat greater extent.
Notice that the constant represents the last half hour of
the last Friday in the sample. During this half hour all the
main markets are closed and only a few traders, if any at all,
input quotations. Therefore, the constant in the estimation
reßects, on average, the smallest number of observations in
the sample, but not necessarily the lowest level of volatility
or the smallest average spread. Let us now concentrate on
these dummy e¤ects.
The estimated dummy coe¦cients, for both currencies
and per equation, are not presented here because of space
considerations.2 Let us consider the half hour dummies Þrst.
In graphs 1a to 3b in Figure 1 the values of the estimated
dummy coe¦cients for both currencies are presented. They
reveal an interesting feature. In the last part of the day BST
time, from about the closing time of the European exchanges
and until the closing time of the New York exchange,
volatility is unusually high. Notice that this takes
place in both currency markets.
During this period there are few, or no, economic (or
other public) announcements from Europe or Asia (considering
only Japan). Most US economic announcements are
made before the opening of the New York Stock Exchange,
at 13.30 BST. There is a small spike at the relevant half hour
(27), but this remains quite small compared with the higher
volatilities apparent later on in the US market day.
Hence, it seems that public news is not the explanation of
this volatility increase. Furthermore, this increase seems
even more di¦cult to explain in the light of the Admati and
Pßeiderer (1988) theory. During this period we certainly
have a reduction in the number of traders in the market, as
only the New York exchange is in operation, so this increase
can hardly be attributed to an increase in the number of
liquidity traders.
There is then an apparent decrease in volatility for both
currencies, during the early morning period between 1:30
and 3:30 (BST). Most of the economic-related news for the
Japanese economy is announced either early in the Japanese
morning, i.e. around 1:00 BST, or in the late Japanese
afternoon, i.e. 6:00 BST. The same time period is characterized
by high spread and screen activity. However, it appears
that Japanese economic-related news has no e¤ect on the
volatility of the JPY currency. Although in line with the
results of Ito and Rolley (1987), this remains peculiar. Furthermore,
the same is true for the Deutschemark in relation
to German economic announcements, which are mostly
released either around 9:30 or 14:00 BST. Hence, it seems
that only US economic news a¤ects the variability of DEM
and JPY exchange rates.
There is a further curiosity in the half-hourly dummies
which is worth mentioning. During the Tokyo lunch time
break (4:00Ð5:00 BST) there is a dramatic decrease of volatility
coupled with an increase in spread and a decrease in
the number of quotations in the Þrst half-hour period (between
4:00Ð4:30 BST), followed by an increase in volatility
coupled with a decrease in spread which cannot be explained
by public information theories. Perhaps traders who
come back early from lunch take ÔwildÕ positions to make
their early return worthwhile. On the other hand this volatility
increase could be a statistical artefact due to the small
number of quotations during that period; that is, a few
observations out of Ôequilibrium levelÕ can have a dramatic
increase in the sample variance of the rate.

TEMPORAL HALF-HOURLY EFFECTS
The temporal dummies capture events (publicly announced
news releases, market openings and closings) whose timing,though not generally their exact scale, is known in advance.
Public new related to macroeconomic variables is simultaneously
announced to all traders, at a time known in advance
since the scheduled time of all economic related news
is predetermined, and reported on another part of the
Reuters system, the FXNB page. The stochastic element in
such cases is the actual announcement, not the timing of it.
In general, the majority of the US announcements are
around 13:30 hours British Summer Time (BST), and the
German ones around 10:00 hours BST. Consequently, the
relationship between the dummy variables and the characteristics
of interest to us in the market predominantly reßect
response of these variables to publicly known events. Per
contra, the relationship between these variables, after conditioning
on such temporal constants, will primarily reßect
private information to a somewhat greater extent.
Notice that the constant represents the last half hour of
the last Friday in the sample. During this half hour all the
main markets are closed and only a few traders, if any at all,
input quotations. Therefore, the constant in the estimation
reßects, on average, the smallest number of observations in
the sample, but not necessarily the lowest level of volatility
or the smallest average spread. Let us now concentrate on
these dummy e¤ects.
The estimated dummy coe¦cients, for both currencies
and per equation, are not presented here because of space
considerations.2 Let us consider the half hour dummies Þrst.
In graphs 1a to 3b in Figure 1 the values of the estimated
dummy coe¦cients for both currencies are presented. They
reveal an interesting feature. In the last part of the day BST
time, from about the closing time of the European exchanges
and until the closing time of the New York exchange,
volatility is unusually high. Notice that this takes
place in both currency markets.
During this period there are few, or no, economic (or
other public) announcements from Europe or Asia (considering
only Japan). Most US economic announcements are
made before the opening of the New York Stock Exchange,
at 13.30 BST. There is a small spike at the relevant half hour
(27), but this remains quite small compared with the higher
volatilities apparent later on in the US market day.
Hence, it seems that public news is not the explanation of
this volatility increase. Furthermore, this increase seems
even more di¦cult to explain in the light of the Admati and
Pßeiderer (1988) theory. During this period we certainly
have a reduction in the number of traders in the market, as
only the New York exchange is in operation, so this increase
can hardly be attributed to an increase in the number of
liquidity traders.
There is then an apparent decrease in volatility for both
currencies, during the early morning period between 1:30
and 3:30 (BST). Most of the economic-related news for the
Japanese economy is announced either early in the Japanese
morning, i.e. around 1:00 BST, or in the late Japanese
afternoon, i.e. 6:00 BST. The same time period is characterized
by high spread and screen activity. However, it appears
that Japanese economic-related news has no e¤ect on the
volatility of the JPY currency. Although in line with the
results of Ito and Rolley (1987), this remains peculiar. Furthermore,
the same is true for the Deutschemark in relation
to German economic announcements, which are mostly
released either around 9:30 or 14:00 BST. Hence, it seems
that only US economic news a¤ects the variability of DEM
and JPY exchange rates.
There is a further curiosity in the half-hourly dummies
which is worth mentioning. During the Tokyo lunch time
break (4:00Ð5:00 BST) there is a dramatic decrease of volatility
coupled with an increase in spread and a decrease in
the number of quotations in the Þrst half-hour period (between
4:00Ð4:30 BST), followed by an increase in volatility
coupled with a decrease in spread which cannot be explained
by public information theories. Perhaps traders who
come back early from lunch take ÔwildÕ positions to make
their early return worthwhile. On the other hand this volatility
increase could be a statistical artefact due to the small
number of quotations during that period; that is, a few
observations out of Ôequilibrium levelÕ can have a dramatic
increase in the sample variance of the rate.

Interaction between quotations, spread, and volatility in FOREX

In the number of quotations equation (Equation 2.c)
volatility and average spread are highly insigniÞcant. This
implies that there may be some kind of ÔcausationÕ from the
number of quotations to volatility and some kind of feedback
relationship between volatility and average spread.
However, the number of observations is not weakly
exogenous to the system as the variance covariance matrix
of the residuals is not diagonal. In fact, the correlation
matrix of the residuals of the system (Equation 2.a to 2.c) is
presented in Table 4.
Hence, we conclude that, apart from the residual e¤ects,
volatility and average spread are simultaneously determined
and there may be a feedback rule between number of
quotations and volatility. However, the number of quotations
a¤ects the average spread process through volatility
only. This relationship is stronger for the Yen than for the
Deutschemark.
Furthermore, notice that the second lagged volatility in
Equation 2.a is insigniÞcant, and the coe¦cient estimate of
the Þrst lag has a very low value (around 0.2 for both
currencies), which implies a very weak autoregressive conditional
heteroskedasticity e¤ect. However, this is not the case
when average spread and number of observations are excluded
from this equation. In such a case the OLS estimates
of the Þrst and second lag volatility, of the regression of
volatility on Dummies and 2 lagged volatilities, equal 0.322
(6.079), and 0.070 (1.746) for the Mark and 0.319 (7.237), and
0.0717 (2.206) for the Yen (the robust t-statistics are in
parentheses). This implies that these two variables take out
a considerable amount of the conditional heteroskedasticity
e¤ect observed in exchange rate time series. This points out
to the fact that heteroskedasticity type e¤ects, which captured
by ARCH or GARCH type models in a univariate
setups, are mainly due to missing variables in the econometrician
Õs information set.
Moreover, the addition of our dummy variables further
reduces the second order ARCH type e¤ect in the series. If
the SES (Equations 2.a to 2.c) is estimated without the
dummy variables the results exhibited in Table 3 are
obtained.
Now the Þrst lag estimated coe¦cient takes a considerably
higher value than in the case where dummy variables
are included, and the second lag coe¦cient becomes signiÞ-
cant. Notice also that now in the number of quotations
equation volatility has a strong negative e¤ect, something
which is also documented in Bollerslev and Domowitz
(1991), where the dummy variables are excluded from their
model.
To conclude this section we can say that the simultaneity
and the inclusion of dummy variables capture a considerable
part of heteroskedasticity type e¤ect, observed exchange
rate markets. This in e¤ect is due to unobservable
news reßected either in the bid-ask spread or in the dummy
variables which are responsible for changes in tradersÕ desired
inventory positions with the result of changing
spreads, according with the theories of OÕHara and OldÞeld
(1986) and Amihud and Mendelson (1980). These changes in
spread can explain a considerable part of volatility movements,
and consequently decreasing the heteroskedasticity
type e¤ects.

Interaction between quotations, spread, and volatility in FOREX

In the number of quotations equation (Equation 2.c)
volatility and average spread are highly insigniÞcant. This
implies that there may be some kind of ÔcausationÕ from the
number of quotations to volatility and some kind of feedback
relationship between volatility and average spread.
However, the number of observations is not weakly
exogenous to the system as the variance covariance matrix
of the residuals is not diagonal. In fact, the correlation
matrix of the residuals of the system (Equation 2.a to 2.c) is
presented in Table 4.
Hence, we conclude that, apart from the residual e¤ects,
volatility and average spread are simultaneously determined
and there may be a feedback rule between number of
quotations and volatility. However, the number of quotations
a¤ects the average spread process through volatility
only. This relationship is stronger for the Yen than for the
Deutschemark.
Furthermore, notice that the second lagged volatility in
Equation 2.a is insigniÞcant, and the coe¦cient estimate of
the Þrst lag has a very low value (around 0.2 for both
currencies), which implies a very weak autoregressive conditional
heteroskedasticity e¤ect. However, this is not the case
when average spread and number of observations are excluded
from this equation. In such a case the OLS estimates
of the Þrst and second lag volatility, of the regression of
volatility on Dummies and 2 lagged volatilities, equal 0.322
(6.079), and 0.070 (1.746) for the Mark and 0.319 (7.237), and
0.0717 (2.206) for the Yen (the robust t-statistics are in
parentheses). This implies that these two variables take out
a considerable amount of the conditional heteroskedasticity
e¤ect observed in exchange rate time series. This points out
to the fact that heteroskedasticity type e¤ects, which captured
by ARCH or GARCH type models in a univariate
setups, are mainly due to missing variables in the econometrician
Õs information set.
Moreover, the addition of our dummy variables further
reduces the second order ARCH type e¤ect in the series. If
the SES (Equations 2.a to 2.c) is estimated without the
dummy variables the results exhibited in Table 3 are
obtained.
Now the Þrst lag estimated coe¦cient takes a considerably
higher value than in the case where dummy variables
are included, and the second lag coe¦cient becomes signiÞ-
cant. Notice also that now in the number of quotations
equation volatility has a strong negative e¤ect, something
which is also documented in Bollerslev and Domowitz
(1991), where the dummy variables are excluded from their
model.
To conclude this section we can say that the simultaneity
and the inclusion of dummy variables capture a considerable
part of heteroskedasticity type e¤ect, observed exchange
rate markets. This in e¤ect is due to unobservable
news reßected either in the bid-ask spread or in the dummy
variables which are responsible for changes in tradersÕ desired
inventory positions with the result of changing
spreads, according with the theories of OÕHara and OldÞeld
(1986) and Amihud and Mendelson (1980). These changes in
spread can explain a considerable part of volatility movements,
and consequently decreasing the heteroskedasticity
type e¤ects.