FX tradeability and flexibility #
This category comprises time series’ of binary variables that indicate “untradability” in the main FX forward market or official targeting of the exchange rate. Lack of tradability, covertibility or flexibilty invalidates a time period for the backtesting of certain trading strategies. These periods should be “blacklisted” for the analysis of strategies that are require the local currency market to have some liquidity or flexibility.
Exchange rate target dummy #
Ticker : FXTARGETED_NSA
Label : Exchange rate target dummy.
Definition : Binary variable that takes the value 1 (rather than 0) if the exchange rate is targeted through a peg or any regime that signficantly reduces exchange rate flexibility.
Notes :
-
The exchange rate is considered targeted if the authorities have formally or informally set a peg, target corridor, cap or floor that is known in the market and expected to be defended by intervention and that substantially restricts the variation of the exchange rate.
-
The dummies are updated at the end of each month based on the assessment of J.P. Morgan market makers and/or research. For example, if a target has been announced in the middle of the month, the variable shifts from 0 to 1 at the end of the month.
-
Older history (prior to the launch of JPMaQS) is based on heritage data of Macrosynergy Partners and (prior to that) replication of real-time liquidity and flexibility status based on an expert review.
FX untradability dummy #
Ticker : FXUNTRADABLE_NSA
Label : FX untradability dummy
Definition : Binary variable that takes the value 1 (rather than 0) if either (i) liquidity in the main FX forward market is limited or (ii) convertibility restrictions signficantly distort the link between tradable offshore and untradable onshore contracts.
Notes :
-
Liquidity is considered limited if it is not possible to trade at least USD30 million in one ticket without significant market impact.
-
The dummies are updated once per month at the end of the month based on the assessment of J.P. Morgan market makers and/or research. Brief spells of illiquidity would not be captured by this series.
-
Older history (prior to launch of JPMaQS) is based on heritage data of Macrosynergy Partners and, before that, replication of real-time liquidity and flexibility status based on an expert review.
Imports #
Only the standard Python data science packages and the specialized
macrosynergy
package are needed.
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math
import json
import yaml
import macrosynergy.management as msm
import macrosynergy.panel as msp
import macrosynergy.signal as mss
import macrosynergy.pnl as msn
from macrosynergy.download import JPMaQSDownload
from timeit import default_timer as timer
from datetime import timedelta, date, datetime
import warnings
warnings.simplefilter("ignore")
The
JPMaQS
indicators we consider are downloaded using the J.P. Morgan Dataquery API interface within the
macrosynergy
package. This is done by specifying
ticker strings
, formed by appending an indicator category code
<category>
to a currency area code
<cross_section>
. These constitute the main part of a full quantamental indicator ticker, taking the form
DB(JPMAQS,<cross_section>_<category>,<info>)
, where
<info>
denotes the time series of information for the given cross-section and category. The following types of information are available:
-
valuegiving the latest available values for the indicator -
eop_lagreferring to days elapsed since the end of the observation period -
mop_lagreferring to the number of days elapsed since the mean observation period -
gradedenoting a grade of the observation, giving a metric of real time information quality.
After instantiating the
JPMaQSDownload
class within the
macrosynergy.download
module, one can use the
download(tickers,start_date,metrics)
method to easily download the necessary data, where
tickers
is an array of ticker strings,
start_date
is the first collection date to be considered and
metrics
is an array comprising the times series information to be downloaded.
# Cross-sections of interest
cids_dmca = [
"AUD",
"CAD",
"CHF",
"EUR",
"GBP",
"JPY",
"NOK",
"NZD",
"SEK",
"USD",
] # DM currency areas
cids_dmec = ["DEM", "ESP", "FRF", "ITL", "NLG"] # DM euro area countries
cids_latm = ["BRL", "COP", "CLP", "MXN", "PEN"] # Latam countries
cids_emea = ["CZK", "HUF", "ILS", "PLN", "RON", "RUB", "TRY", "ZAR"] # EMEA countries
cids_emas = [
"CNY",
# "HKD",
"IDR",
"INR",
"KRW",
"MYR",
"PHP",
"SGD",
"THB",
"TWD",
] # EM Asia countries
cids_dm = cids_dmca + cids_dmec
cids_em = cids_latm + cids_emea + cids_emas
cids = sorted(cids_dm + cids_em)
main = ["FXTARGETED_NSA", "FXUNTRADABLE_NSA"]
econ = ["FXCRR_VT10", "FXCRR_NSA"] # economic context
mark = ["FXXRxEASD_NSA", "FXXR_NSA", "FXXR_VT10"] # market links
xcats = main + econ + mark
# Download series from J.P. Morgan DataQuery by tickers
start_date = "1995-01-01"
tickers = [cid + "_" + xcat for cid in cids for xcat in xcats]
print(f"Maximum number of tickers is {len(tickers)}")
# Retrieve credentials
client_id: str = os.getenv("DQ_CLIENT_ID")
client_secret: str = os.getenv("DQ_CLIENT_SECRET")
# Download from DataQuery
with JPMaQSDownload(client_id=client_id, client_secret=client_secret) as downloader:
start = timer()
df = downloader.download(
tickers=tickers,
start_date=start_date,
metrics=["value", "eop_lag", "mop_lag", "grading"],
suppress_warning=True,
)
end = timer()
dfd = df
print("Download time from DQ: " + str(timedelta(seconds=end - start)))
Maximum number of tickers is 259
Downloading data from JPMaQS.
Timestamp UTC: 2023-06-02 16:19:44
Connection successful!
Number of expressions requested: 1036
Download time from DQ: 0:00:57.368440
Availability #
cids_exp = sorted(list(set(cids) - set(cids_dmec))) # cids expected in category panels
msm.missing_in_df(dfd, xcats=main, cids=cids_exp)
Missing xcats across df: set()
Missing cids for FXTARGETED_NSA: {'USD'}
Missing cids for FXUNTRADABLE_NSA: {'USD'}
JPMaQS holds information on FX tradeability and flexibility from 1999 for all currency areas.
For the explanation of currency symbols, which are related to currency areas or countries for which categories are available, please view Appendix 1 .
xcatx = main
cidx = cids_exp
dfx = msm.reduce_df(dfd, xcats=xcatx, cids=cidx)
dfs = msm.check_startyears(
dfx,
)
msm.visual_paneldates(dfs, size=(18, 2))
print("Last updated:", date.today())
Last updated: 2023-06-02
xcatx = main
cidx = cids_exp
plot = msm.check_availability(
dfd, xcats=xcatx, cids=cidx, start_size=(18, 4), start_years=False
)
xcatx = main
cidx = cids_exp
plot = msp.heatmap_grades(
dfd,
xcats=xcatx,
cids=cidx,
size=(18, 2),
title=f"Average vintage grades from {start_date} onwards",
)
xcatx = main
cidx = cids_exp
msp.view_ranges(
dfd,
xcats=xcatx,
cids=cidx,
val="eop_lag",
title="End of observation period lags (ranges of time elapsed since end of observation period in days)",
start=start_date,
kind="box",
size=(16, 4),
)
msp.view_ranges(
dfd,
xcats=xcatx,
cids=cidx,
val="mop_lag",
title="Median of observation period lags (ranges of time elapsed since middle of observation period in days)",
start=start_date,
kind="box",
size=(16, 4),
)
History #
Exchange rate target dummy #
Less than a third of the currencies in the JPMaQS sample have at any time been pegged or strictly targeted during the history recorded in JPMaQS. Only China and Singapore have been targeted throughout.
xcatx = ["FXTARGETED_NSA"]
cidx = cids_exp
msp.view_timelines(
dfd,
xcats=xcatx,
cids=cidx,
start=start_date,
title="Indicator variable for exchange rate targeting, since 1999",
title_adj=1.02,
ncol=4,
same_y=True,
size=(12, 7),
aspect=1.7,
all_xticks=True,
)
Untradable FX dummy #
Over the last two decades, it has been rare to see sustained lack of tradability in the major FX forward markets. Malaysia and Turkey have become notable exceptions in the recent past.
xcatx = ["FXUNTRADABLE_NSA"]
cidx = cids_exp
msp.view_timelines(
dfd,
xcats=xcatx,
cids=cidx,
start=start_date,
title="Indicator variable for FX forward market untradeability, since 1999",
title_adj=1.02,
ncol=4,
same_y=False,
size=(12, 7),
aspect=1.7,
all_xticks=True,
)
Importance #
Research Links #
“(For) a fixed currency…technical analysis is not very useful, because fluctuations of the rate lack continuity, liquidity is low, and there’s not enough data to feed to the indicators in order to generate timely and meaningful signals.” ForexFraud
“Carry trade returns vary with exchange rate regimes. Average excess returns of the unconditional carry trade are entirely driven by returns to the carry strategy conditioned on the sample of currency pairs in the floating exchange rate regime. We term this strategy the floating carry trade. In comparison, the carry strategy conditioned on the sample of currency pairs in the fixed exchange rate regime (the fixed carry trade) generates zero returns on average.” Accominotti, Cen, Chambers and Marsh
Empirical clues #
Since 2000, roughly more than a third of the currencies covered by JPMaQS have seen their FX forward market affected either by illiquidity, an official exchange rate target, or convertibility-related distortions.
xcatx = ["FXTARGETED_NSA", "FXUNTRADABLE_NSA"]
cidx = cids_exp
msp.view_timelines(
dfd,
xcats=xcatx,
cids=cidx,
start=start_date,
title="Sustained market disruptions from FX targeting or illiquidity",
title_adj=1.02,
title_xadj=0.44,
xcat_labels=["Targeted", "Untradeable"],
ncol=7,
same_y=True,
size=(12, 10),
aspect=1,
)
Blacklisting often gives a clearer picture of the statistical relations in the FX space. For example, it removes periods of “unattainable” carry, such as in Malaysia in the early 2000s, which would overstate the predictive power of FX carry for returns.
dfb = dfd[dfd["xcat"].isin(["FXTARGETED_NSA", "FXUNTRADABLE_NSA"])].loc[
:, ["cid", "xcat", "real_date", "value"]
]
dfba = (
dfb.groupby(["cid", "real_date"])
.aggregate(value=pd.NamedAgg(column="value", aggfunc="max"))
.reset_index()
)
dfba["xcat"] = "FXBLACK"
fxblack = msp.make_blacklist(dfba, "FXBLACK")
fxblack
{'BRL': (Timestamp('2012-12-03 00:00:00'), Timestamp('2013-09-30 00:00:00')),
'CHF': (Timestamp('2011-05-02 00:00:00'), Timestamp('2016-06-30 00:00:00')),
'CNY': (Timestamp('1999-01-01 00:00:00'), Timestamp('2023-06-01 00:00:00')),
'CZK': (Timestamp('2014-01-01 00:00:00'), Timestamp('2017-07-31 00:00:00')),
'ILS': (Timestamp('1999-01-01 00:00:00'), Timestamp('2005-12-30 00:00:00')),
'INR': (Timestamp('1999-01-01 00:00:00'), Timestamp('2004-12-31 00:00:00')),
'MYR_1': (Timestamp('1999-01-01 00:00:00'), Timestamp('2007-11-30 00:00:00')),
'MYR_2': (Timestamp('2018-07-02 00:00:00'), Timestamp('2023-06-01 00:00:00')),
'PEN': (Timestamp('2021-07-01 00:00:00'), Timestamp('2021-07-30 00:00:00')),
'RON': (Timestamp('1999-01-01 00:00:00'), Timestamp('2005-11-30 00:00:00')),
'RUB_1': (Timestamp('1999-01-01 00:00:00'), Timestamp('2005-11-30 00:00:00')),
'RUB_2': (Timestamp('2022-02-01 00:00:00'), Timestamp('2023-06-01 00:00:00')),
'SGD': (Timestamp('1999-01-01 00:00:00'), Timestamp('2023-06-01 00:00:00')),
'THB': (Timestamp('2007-01-01 00:00:00'), Timestamp('2008-11-28 00:00:00')),
'TRY_1': (Timestamp('1999-01-01 00:00:00'), Timestamp('2003-09-30 00:00:00')),
'TRY_2': (Timestamp('2020-01-01 00:00:00'), Timestamp('2023-06-01 00:00:00'))}
xcatx = ["FXCRR_NSA", "FXXR_NSA"]
cidx = cids_exp
cr = msp.CategoryRelations(
dfd,
xcats=xcatx,
cids=cidx,
freq="Q",
lag=1,
xcat_aggs=["last", "sum"],
xcat_trims=[40, 30],
start=start_date,
years=None,
)
cr.reg_scatter(
title="Real FX forward carry and subsequent cumulative returns without blacklisting across all markets since 2000",
labels=False,
coef_box="upper left",
ylab="FX forward return next quarter, %",
xlab="Real FX carry, % ar",
)
FXCRR_NSA misses: ['USD'].
FXXR_NSA misses: ['USD'].
xcatx = ["FXCRR_NSA", "FXXR_NSA"]
cidx = cids_exp
cr = msp.CategoryRelations(
dfd,
xcats=xcatx,
cids=cidx,
freq="Q",
lag=1,
xcat_aggs=["last", "sum"],
xcat_trims=[40, 30],
blacklist=fxblack,
start=start_date,
years=None,
)
cr.reg_scatter(
title="Real FX forward carry and subsequent cumulative returns with blacklisting across all markets since 2000",
labels=False,
coef_box="upper left",
ylab="FX forward return next quarter, %",
xlab="Real FX carry, % ar",
)
FXCRR_NSA misses: ['USD'].
FXXR_NSA misses: ['USD'].
Appendices #
Appendix 1: Currency symbols #
The word ‘cross-section’ refers to currencies, currency areas or economic areas. In alphabetical order, these are AUD (Australian dollar), BRL (Brazilian real), CAD (Canadian dollar), CHF (Swiss franc), CLP (Chilean peso), CNY (Chinese yuan renminbi), COP (Colombian peso), CZK (Czech Republic koruna), DEM (German mark), ESP (Spanish peseta), EUR (Euro), FRF (French franc), GBP (British pound), HKD (Hong Kong dollar), HUF (Hungarian forint), IDR (Indonesian rupiah), ITL (Italian lira), JPY (Japanese yen), KRW (Korean won), MXN (Mexican peso), MYR (Malaysian ringgit), NLG (Dutch guilder), NOK (Norwegian krone), NZD (New Zealand dollar), PEN (Peruvian sol), PHP (Phillipine peso), PLN (Polish zloty), RON (Romanian leu), RUB (Russian ruble), SEK (Swedish krona), SGD (Singaporean dollar), THB (Thai baht), TRY (Turkish lira), TWD (Taiwanese dollar), USD (U.S. dollar), ZAR (South African rand).
