AUTHOR
essaygo
PUBLISHED ON:
2022年3月7日
PUBLISHED IN:

## 1 Data

You can collect data from a variety of sources. For example, you can get data from:

• Global Financial Data1
• FRED
• OECD Stats
• WEO Database
• Refinitiv
• Bloomberg

When monthly data are not available, use quarterly/yearly data and retrieve monthly
observations by forward filling, i.e., keep the latest observation available until a new one

## 2 Exercise 1 [30%]

Using at least 30 years of monthly (end-of-month) data on major currency pairs (for ex
ample, AUD, CAD, CHF, DEM-EUR, GBP, JPY, NOK, NZD, and SEK relative to USD),
construct the following monthly rebalanced strategies2

• Carry trade strategy: On each month t, you buy the top 3 high-yielding currencies
and sell the top 3 low-yielding currencies. This is equivalent to sorting on the basis
of the one-month forward premia at time t.

• Mmomentum strategy: On each month t, you buy the top 3 winner currencies and
sell the top 3 loser currencies. This is equivalent to sorting on the past one-month
exchange rate returns, i.e., the exchange rate return between months t and t − 1.
Put differently, you buy (sell) those currencies that have appreciated (depreciate)
the most over the past month.

• Value strategy: On each month t, you buy the top 3 undervalued currencies and sell
the top 3 overvalued currencies. This is equivalent to sorting on the past five-year
exchange rate returns, i.e., the exchange rate return between months t and t − 60.
Put differently, the currencies that have appreciated (depreciated) the most over the
past five years are likely to be overvalued (undervalued), and you want to sell (buy)
them.

### 2.1 Questions

1. Can you present summary statistics and plot the cumulative excess returns?

2. How do these strategies behave during periods of high and low volatility?

3. Would an equally-weighted combined strategy outperform any individual strategy?

## 3 Exercise 2 [30%]

Take the USD/EUR currency pair, and consider the following specifications

1. RW with drift (i.e., your benchmark model),

2. TRa

Setup. Using a 10-year rolling window, generate out-of-sample (OOS) forecasts using
the following predictive regression:

yt = α + βxt−1 + εt

where yt is the log exchange rate return between months t − 1 and t, and xt−1 is a one
month lagged predictor observed at time t.

• For RW, set xt−1 as in Slide 17 (Lecture 6),

• For TRa, set xt−1 as in Slide 18 (Lecture 6), extract the potential output using a linear
trend, and compute the output gap as ln(real output) minus ln(potential output).

You may also like: