CDS index volatility #
This category group includes measures of historic return volatility and related leverage ratios for credit derivative swaps indices, particularly CDX and iTraxx investment-grade and high-yield indices for the U.S. and the euro area.
CDS index volatility #
Ticker : CRXRxEASD_NSA
Label : Estimated annualized standard deviation of CDS index return.
Definition : Annualized standard deviation of CDS index future return, % of notional, based on exponential moving average of daily returns.
Notes :
-
The standard deviation has been calculated based on an exponential moving average of daily returns with a half-time of 11 active trading days.
-
The source of the underlying market quotes is J.P. Morgan/DataQuery.
-
Returns are unfunded swap returns on the underlying CDS index based on J.P. Morgan calculation.
-
A new index series is determined on the basis of liquidity every six months. Positions are rolled accordingly.
Leverage ratio of vol-targeted CDS index position #
Ticker : CRXRxLEV10_NSA
Label : Leverage ratio of CDS index position for 10% annualized vol target.
Definition : CDS index leverage for a 10% annualized vol target, as ratio of contract notional relative to risk capital on which the return is calculated.
Notes :
-
Positions are scaled to a 10% volatility target based on historic standard deviations for an exponential moving average with a half-life of 11 days. Positions are rebalanced at the end of each month.
-
See further the related notes above on “CDS index volatility” (
CRXRxEASD_NSA
).
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:
-
value
giving the latest available values for the indicator -
eop_lag
referring to days elapsed since the end of the observation period -
mop_lag
referring to the number of days elapsed since the mean observation period -
grade
denoting 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.
cids_g2 = ["EUR", "USD"]
cids_cr = ["EIG", "EHY", "UIG", "UHY"]
cids = sorted(cids_g2 + cids_cr)
main = ["CRXRxEASD_NSA", "CRXRxLEV10_NSA"]
econ = ["PCREDITBN_SJA_P1M1ML12", "PCREDITGDP_SJA_D1M1ML12"] # economic context
mark = ["CRXR_NSA", "CRXR_VT10", "CRCRY_NSA", "CRCRY_VT10"] # market links
xcats = main + econ + mark
# Download series from J.P. Morgan DataQuery by tickers
start_date = "2000-01-01"
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()
assert downloader.check_connection()
df = downloader.download(
tickers=tickers,
start_date=start_date,
metrics=["value", "eop_lag", "mop_lag", "grading"],
suppress_warning=True,
show_progress=True,
)
end = timer()
dfd_1 = df[df["cid"].isin(cids_cr)]
dfd_x = df[df["cid"].isin(["EUR", "USD"])]
dfd_2 = df.replace({"^EUR": "EIG", "^USD": "UIG"}, regex=True)
dfd_3 = df.replace({"^EUR": "EHY", "^USD": "UHY"}, regex=True)
dfd = pd.concat([dfd_1, dfd_2, dfd_3])
print("Download time from DQ: " + str(timedelta(seconds=end - start)))
Maximum number of tickers is 48
Downloading data from JPMaQS.
Timestamp UTC: 2023-06-02 23:20:49
Connection successful!
Number of expressions requested: 192
Requesting data: 100%|██████████████████████████| 10/10 [00:03<00:00, 3.20it/s]
Downloading data: 100%|█████████████████████████| 10/10 [00:16<00:00, 1.68s/it]
Download time from DQ: 0:00:22.030507
Availability #
cids_exp = cids_cr
msm.missing_in_df(dfd, xcats=main, cids=cids_exp)
Missing xcats across df: set()
Missing cids for CRXRxEASD_NSA: set()
Missing cids for CRXRxLEV10_NSA: set()
JPMaQS contains four cross sections for CDS index performance data:
-
EHY: European high yield
-
EIG: European investment grade
-
UHY: U.S. high yield
-
UIG: U.S. investment grade
All series’ begin in the early 2000s.
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, 1))
print("Last updated:", date.today())

Last updated: 2023-06-03
xcatx = main
cidx = cids_exp
plot = msm.check_availability(
dfd, xcats=xcatx, cids=cidx, start_size=(18, 1), start_years=False
)

xcatx = main
cidx = cids_exp
plot = msp.heatmap_grades(
dfd,
xcats=xcatx,
cids=cidx,
size=(18, 1),
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 #
CDS index volatility #
Average return volatility of high-yield indices has been near 7% (annualized) and for high-grade indices below 2%. In periods of market turmoil, high-yield volatility has soared to 35-40%.
xcatx = ["CRXRxEASD_NSA"]
cidx = cids_exp
msp.view_ranges(
dfd,
xcats=xcatx,
cids=cidx,
sort_cids_by="mean",
start=start_date,
kind="bar",
title="Means and standard deviations of CDS index volatilities, since 2000",
xcat_labels=["Standard deviation of weighted CDS index future returns"],
size=(16, 8),
)

xcatx = ["CRXRxEASD_NSA"]
cidx = cids_exp
msp.view_timelines(
dfd,
xcats=xcatx,
cids=cidx,
start=start_date,
title="CDS index volatility across key market segments",
title_adj=0.88,
cumsum=False,
ncol=2,
same_y=False,
size=(12, 7),
aspect=2,
all_xticks=True,
)

Leverage ratio of vol-targeted CDS index positions #
High-yield indices required on average more than 5 times the leverage of investment grade indices to pursure the same volatility target. Proclivity to high leverage was much greater prior to the subprime and great financial crises.
xcatx = ["CRXRxLEV10_NSA"]
cidx = cids_exp
msp.view_ranges(
dfd,
xcats=xcatx,
cids=cidx,
sort_cids_by="mean",
start=start_date,
title="Means and standard deviations of leverage ratios in vol-targeted CDS index positions",
xcat_labels=["Leverage ratios"],
kind="bar",
size=(16, 8),
)

xcatx = ["CRXRxLEV10_NSA"]
cidx = cids_exp
msp.view_timelines(
dfd,
xcats=xcatx,
cids=cidx,
start=start_date,
title="Vol-targeted leverage ratio of CDS index positions across key market segments",
title_adj=0.88,
cumsum=False,
ncol=2,
same_y=False,
size=(12, 7),
aspect=2,
all_xticks=True,
)

Importance #
Research Links #
“Since CDSs are financial instruments, their spreads can reflect market liquidity, counterparty risks, and technical factors, such as the high leverage inherent in swaps…A CDS is different from a bond, …Part of the risk is that the counterparty might not be around to honor the contract…In addition, since there are low margin requirements for credit derivatives, a small change in the instrument’s price could produce a large loss, forcing a firm to wind down its position quickly. That could exacerbate price volatility.” Vincent Ryan
“‘Reach for yield’ describes regulated investors’ preference for high-risk assets within the confines of a rule-based risk metric (such as credit ratings or VaR). Bo Becker and Victoria Ivashina provide evidence that U.S. insurance companies act on this principle and show that conditional on ratings, insurance portfolios are systematically biased toward higher yield bonds”. ‘Reach for yield’ would be a form of regulatory arbitrage, a source of inefficiency, and a reward for unaccounted risk of securities and issuers.” Macrosynergy
Empirical Clues #
Historically, higher required leverage ratios have tended to entail higher vol-adjusted returns, possibly as compensation for tail risk.
There is some evidence to suggest that larger percentage changes in leverage ratios are associated with higher cumulative vol-adjusted returns.
xcatx = ["CRXRxLEV10_NSA", "CRXR_VT10"]
cidx = cids_exp
cr = msp.CategoryRelations(
dfd,
xcats=xcatx,
cids=cidx,
freq="Q",
lag=0,
xcat_aggs=["mean", "sum"],
start="2000-01-01",
xcat1_chg="pch",
xcat_trims=[1.5, 20],
)
cr.reg_scatter(
title="Percentage changes in leverage ratios and concurrent quarterly vol-targeted CDS index returns, since the early 2000s",
labels=True,
coef_box="lower right",
xlab="% change in leverage ratio based on 10% vol target",
ylab="Cumulative vol-adjusted return",
)
