BFF3231
International Finance
BFF3231 Learning Objectives
1
understand and interpret key macroeconomic indicators for Australia and foreign
economies and recognise definitional and measurement problems associated with
the available data, with a particular focus on the open economy dimension
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2
analyse and evaluate the benefits and disadvantages of the various exchange rate
systems
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critically appraise the extent to which the theories of exchange rate determination
explain exchange rate movements in today's globalised economy
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explain the various tools and approaches for firms in hedging transaction
exchange exposure, and recommend the most appropriate approach to hedging in
a variety of economic and firm-specific circumstances
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analyse the extent to which firms are exposed to economic or operating exchange
risk, evaluate their systems in place to manage it, and recommend alternative
approaches
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appraise the benefits of international portfolio investment vis-a-vis domestic-only
diversification
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apply critical thinking, problem solving and presentation skills to individual and/or
group activities dealing with international finance and demonstrate in an individual
summative assessment task the acquisition of a comprehensive understanding of
the topics covered by BFF3231.
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The (Big) Data Revolution
Data have become increasingly important in business, finance, government,
and research (for example, in finance and economics)
Plenty of resources, books, blogs, courses at uni.
Plenty of free resource, including at Monash. Monash Data Fluency initiative
https://www.monash.edu/data-fluency/home
Even for simple data, it takes time and practice to make sense of certain
datasets, easy to make mistakes and draw wrong conclusions.
Both a competitive advantage, if skilled, but also a necessity for some
employers.
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McKinsey (2021) Future work skills
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Trends
(Big) data
• analysed with (Deep) Machine Learning technologies and more powerful
hardware (Cloud Computing infrastructures, GPUs, etc.)
• Integrate and augment the information offered by aggregated variables
produced by national and international statistical agencies.
• Increase the level of granularity
 Data Science as a field.
At Monash, primarily taught by the Econometrics and Business Statistics
department.
Aggregate, Micro, and New data
There’s a massive amount of data, and progressively more
• At the aggregate/country level, let’s index it by c
• At the micro-level: households, firms, loans, where the unit of observation is an
individual i
AGGREGATE DATA. Traditionally these data have been collected for national
statistics purpose (say the Census currently on in Australia, to measure basic facts
about the population).
• Notice that aggregate statistics are also built on micro data, for example inflation
is measured collecting different individual prices for various products and
aggregating according to certain principles, formulas, etc.
• While we think of these as “measures,” very often they actually rely on statistical
technique to estimate a variable so they are actually measures, and as such,
imprecisely estimated to a degree (say GDP).
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Aggregate, Micro, and New data
MICRO DATA. More recently (past 30 years), more and more to understand
individual behaviour and the effect of policies say for example household surveys, or
the enterprise surveys of the Worldbank
NEW DATA. Even more recently (past 5 years) “new” data, for example, GPS
location, credit card transactions, textual analysis/measures, etc
[HISTORICAL DIGITISED DATA]. Less relevant for us, but a massive trend of
digitising historical data from printed sources, say bank level data for Australia’s
1893 banking crisis and depression.
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Structure of datasets: a recap
• Cross-section: at one point in time the observation point of a set of
particular variables is an individual: country, firm, household, worker, bank
For example:
= , … ,
• Time series: A variable for a particular individual is measured over time t
(minutes, days, weeks, months, but more likely quarters and years).
For example:
= [1960 − 2021]
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Structure of datasets: a recap
• Panel (longitudinal data): One or a set of variables for a group of individuals
(say countries) is measured over time (say the Trade Balance for all
advanced economies for the period between 1960 and 2021)
For example; : with = , … , = [1960 − 2021]
Over a certain period, a panel can be
– Balanced: a dataset in which each individual is observed every period
– Unbalanced: a dataset in which at least one panel member is not
observed every period.
• Repeated cross-section: In panel data, variables are collected on the same
individuals at multiple times. In repeated cross sections the same variables
are collected in multiple periods but for different samples of individuals at
each time period, so individuals cannot be linked over time
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Interesting descriptors of time-series data
In my experience in doing empirical work, even simple descriptive analysis, it’s
extremely easy to misunderstand the data we work with. It takes some thinking
and experience.
Focusing on time series (say
), say you’re plotting it, things you
want to ask yourself:
- Am I looking at levels of growth rate?
- What kind of growth rate is it? Quarter-on-quarter? Year-on-year? Annual?
- What currency is it measured in?
- Is it in current currency (nominal) or constant currency (real) ? (i.e. has
inflation been removed?)
- Am I looking at relative prices and what is relative to what? (say FX, 1A$ for
XU$, or 1U$ for XA$)
- What kind of interest rate am I looking at? Maturity? Yield? Spread?
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Interesting descriptors of time-series data
• Is it positive, negative or at times positives and at time negatives?
• What does the trend look like? Upward, downward? Not much of a trend?
• If a trend is discernible, is there an obvious structural break (change of
slope) that can be discerned?
• Does it comove with the business cycle? In a positive or negative way?
• How volatile is this time series?
• Can I identify particular patterns in particular periods that are interesting
(say the Covid-19 crisis or the 2007-9 Global Financial Crisis)?
• Are there missing data and what to do about it?
These are questions worth considering when we focus on an individual time
series? Looking at different time series for the same country (Say business
lending and GDP) or the same time series for different countries (say GDP for
Australia and Indonesia)
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Publicly Available Macro
and Finance data,
some micro.
Primarily country-level
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International Organisations - 1
International Organisations
• IMF (International Monetary Fund) known for macro and public
finance/government data as well as open economy macro data (lectures 2
and 3)
• Worldbank  known for macro and micro data with a focus on
development/developing countries.
They run many surveys (household, firms, financial inclusion) that are
comparable across countries
• BIS (Bank of International Settlements) Known for FX, international
banking statistics, derivative, monetary policy and cross-border finance
(lectures 1 and 2)
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International Organisations - 2
International Organisations
• WTO (World Trade Organisation): Trade and Intellectual Property
• Unit Nations Agencies.. Plenty, for example:
– UNCTAD (UN commission on Trade and Development): FDI,
Multinationals, Trade data
– ILO (International Labour Organisation): Labour, unemployment, etc
data
• Other regional institutions:
– OECD: Organisation for Economic Co-operation and Development is
an intergovernmental economic organisation with 38 member countries
 Much more partial view, known for certain trade data, certain interest
data, productivity measures, and industry level data but
– ADB (Asian Development Bank)  Asian countries
– EBRD (European Bank for Reconstruction and Development): Eastern
Europe and Central Asia
– … others
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Central Banks
Typically, Central Banks Collect Macro and Finance data, as well as some
micro
Data focus, typically on their country
The ones with the best data:
• Federal Reserve
– Board of Governors
– FRED
• European Central Bank
• Bank of England
• Reserve Bank of Australia
More or less easy to use depending on the country
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IMF
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IMF
Widest coverage of countries and also quite long time series.
• government finance … examples?
• monetary relations
• financial stability
• exchange rates
• balance-of-payments and
• global economic prospects forecast.
IMF data can be accessed via:
• Datastream (a commercial platform available in most business schools) for
integrating IMF data with other international data series
• UKDS.Stat platform for time series downloads https://stats.ukdataservice.ac.uk/
• IMF data homepage IMF Data Home Page - IMF Data
There’s always supporting docs: long and boring but key to understand what we are
doing
More and more user friendly
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https://www.imf.org/en/Data#
global
• global economic prospects
forecast.
• financial stability
• government finance / fiscal
policy
Database links below e.g. (note
there are more such as exchange
rate data, financial sector statistics
etc.
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For example GDP and its component for Australia
Annual vs
Quarterly
Real vs. Nominal
Scale
Missing data
…etc
Even a “simple”
time series like
GDP can be a bit
complicated to
choose and
describe
For example, the Exchange Rates tab:
SDRs per Currency unit (e.g. $ 1.00 = 0.67353 SDR)
(e.g. $ 1.00 = 0.67353 SDR)
last five days
Current Month
Archives
Representative Rates for Selected Currencies
Normally quoted as currency units per U.S. dollar, are reported daily to the Fund by the issuing central
bank.
Latest
Current Month
Archives
Annual Report on Exchange Arrangements and Exchange
Restrictions
Currency units per SDR
(e.g. $ 1.48472 = 1 SDR)
last five days
Current Month
Archives
Exchange Rate Archives
1995 to the present
Even something “simple” like
Exchange Rates can be
pretty complex
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Data Mapper
https://www.imf.org/external/datamapper/NGDP_RPCH@WEO/OEMDC/ADVEC/WEOWORLD
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Data Mapper
Top: The cross-section of
countries in 2021, mapped, using
colours as a scale
Bottom: the time series of two
selected countries, Australia and
China, with some options. Notice
that it even has an IMF forecast
after 2021
• Data can be plotted or
downloaded.
• Chats can be plotted and
downloaded
• Even shared on social media
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For you to try
• Try to explore a bit the IMF data and the Data mapper pages before
the assignment
• For example, construct trade balance and Balance of Payment
positions for Australia, if available in both dollars and % of GDP
• Try for example to replicate variables some other charts seen in
class (lectures 2 and 3), say AUD exchange rate
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Worldbank
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https://data.worldbank.org/
• Known for macro and micro data with a focus on development/developing
countries.
• They run many surveys (household, firms, financial inclusion).. Micro
datasets… that are comparable across countries
• The combine micro and macro data to produce indicators in an accessible
way for a large set of countries over time… for example, they would have
macro data like the IMF but also measures constructed from their micro
datasets, say a country-level measure of financial inclusion contructed from
their triennial survey called Global Findex (and conducted by Gallup across
all countries)
• Also – unlike the IMF - a “data dump” page (will explain)
• An open data policy: everything they collect, they publish
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https://data.worldbank.org/
• Databank
• Microdata
• Data Catalog
highlights
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First tab: DataBank
DataBank is an analysis
and visualisation tool that
contains collections of
time series data on a
variety of topics. You can
create your own queries;
generate tables, charts,
and maps; and easily
save, embed, and share
them.
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For example, non performing loans as a share
of bank assets in Italy
Second tab: Microdata
Not so relevant for us in
this course but very
interesting
Third tab: Data Catalog
All their databases one by
one… what I think of as a
“data dump”… it’s
complete, you can get
everything they have that
is publicly available but it’s
difficult to navigate
Searchable by country,
topic, keyword but difficult
to navigate
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Bank of International
Settlements
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https://stats.bis.org
Known for
• FX
• international banking statistics
• derivatives,
• monetary policy measures
• Credit measures
• cross-border finance
Features
• More complex data to understand and use
• Much less data, but difficult to find elsewhere
• For example: International banking statistics are collected as locational
statistics and consolidated banking that are conceptually difficult
• Various variables may have very different country (i) or time (t) coverage
• Possibly a bit of less user-friendly interface
Data collected
• International
Banking
• Debt Securities
• Derivatives
• Total Credit
• Debt service ration
• Property Prices
• Consumer Prices
• Exchange Rates
• Policy Rates
Estimates of
• Credit Gaps
• Global Liquidity
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Consider banking statistics
For example, to obtain the chart in the following slide. From
https://www.bis.org/statistics/index.htm
> BIS Statistics Explorer Locational b
> Locational banking statistics
> A5 By location of reporting bank
Click on Australia
Click on 627.214
To obtain: https://stats.bis.org/statx/srs/table/A5?c=AU&p=
or
Banks located in Australia
Positions reported by banking offices located in the specified country regardless of the
nationality of the controlling parent
in millions of US dollars
Claims
taht
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Banks located in Australia. Positions reported by banking offices located in the specified
country regardless of the nationality of the controlling parent
in millions of US dollars. Claims
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For you to try
• Try to explore the banking statistics and to plot cross-border loans in and
out of Australia with respect to different counterparties, and understand
what you’re plotting
• Try to plot the AUD exchange rate wrt the dollar using the BIS data
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FRED at the St. Louis Fed
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FRED (Federal Reserve Economic Data)
 Fred is different from the international organisation data banks.
 It’s been around since the mid 1990, a bit of a visionary enterprise at the St.
Louis Fed, it has inspired many other data repositories
 It’s the original “data dump” (I mean it in a friendly way, I love it)
 It’s the original “data dump” (I mean it in a friendly way, I love it)
 Massive dataset, searchable, tags, etc
 Simple to use but occasionally difficult to navigate
 Widely used
 A bit of US bias but more and more global
 Mostly free data collected from hundreds of sources
 Tricky because it also contains variables that have been discontinued
 Allows some data manipulation (ratios between variables, multiplications etc)
 Collects research datasets as well
 It has neat MS Excel extension to use for repeated download/updates
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FRED
I would say:
• Use it if you know exactly what you need
• If you need to update series that you know and have
• Primarily for US data
• If you want to simple manipulations directly on the platform
• If you want to do quick plots to download.
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For example, among popular series…
https://fred.stlouisfed.org/series/FEDFUNDS