Indian asset managers dump government bonds at record pace on oil shock
The contagion from oil to gold to equity to debt market is concerning.
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Clickbait much? 
So, atleast one writer in Reuters appears to believe in this pet-theory.
FWIW, the link between equity and debt is nothing new.
It has occurred repeatedly each time equity dropped significantly in the Indian markets,
atleast over the past decade.
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That’s more like a “hedge” statement: “Some”(how many? 1 or 2) asset managers shift to “short term”… obviously lesser risk.
- Yield spike up is a fact.
- MFs sold more this March than in any of the previous 12 months is also a fact.
Not necessarily. Overnight or TREPS would still give you maybe 1-2% less than the 10Y yield without the added risk.
Which acts in favor of investing in GSECs, not overhyped.
…and in the near short-term, after a bounce-back,
they will divest themselves off-of the riskier instruments (at a profit).
Note: Assumption based on historical pattern that repeats each time during a significant equity market draw-down.
AFAIK, one cannot buy delivery orders using these as collateral.
So, cannot invest in them to later snipe GSECs/SDLs at a discount.
What’s the added risk here? Volatility from time to time?
Only a relevant factor if one does NOT intend to hold till maturity.
The other way to look at it is,
if one doesn’t need the liquidity, i.e. capital preservation in the long-term,
then those additional percentage points of returns are available.
Also, it is not 1-2% additional returns over Overnight/TREPS.
Purchasing TBills/GSECs/SDLs at steep discounts 5-20% over their face-value, offers 10-12% annualized returns.
Elaborate? I can buy Zerodha overnight fund or LIQUIDCASE and sell them to snipe GSEC, whenever I want.
Whenever GSEC yields spike, so does overnight yields as well. It’ll be around 2% difference most of the time.
Really? You’re making the argument since the share of a company dropped so much, it acts in favor of investing in them… without regard to the risk of how much more it can fall or why it is falling in the first place.
Others holding cash in the trading account
will be able to buy the GSCEs/SDLs before you do.
especially using custom terminals or API access/
Leading to being unable to invest, no more asks,
or left with higher asks / lower discounts.
FYI,
over the past few years,
on NSE and BSE there are atleast 2 accounts using automation
active in this segment
with a few Crores of liqudity each.
They can do the same even if they had invested in TREPS. Right? Api sell treps. Api buy gsec. Are you talking about a difference of milliseconds?
However, while returns from such overnight yields will vary over time,
the fixed-rate of returns from GSECs/SDLs (signficantly high when purchased at a discount)
are locked in for the entire druation of the GSEC/SDL.
I am not.
It is the folks (in this case MF asset-managers) trying to time the market,
who are doing that,
by liquidating their sovereign-debt holdings to get exposure to devalued equity.
While they can do so,
“selling liquidity” in GSECs by nabbing them at discount on their face-value,
i.e. “buy low, sell high(on maturity)” sovereign debt
works great to make sovereign-guaranteed high-returns.
As far as risk in INR itself is concerned,
using INR holdings to invest into sovereign debt denominated in INR
doesn’t increase one’s risk/exposure to INR.
Of course,
one is prudent to continue to hold non-INR denominated foreign assets
to hedge one’s risk from exposure to INR.
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Yes. Hence used “snipe” to highlight that.
One of them snipes at 8% or higher yield, the other above 10% yield.
Once they have spent their liquidity,
subsequently others with higher latency will be able to participate, 
But sure, right now, with lot of volumes to go around,
even the second-best ask will provide pretty significant returns.
Everyone is welcome to join! 
Just need to remember that one is selling liquidity.
Very pricey right now (and in the near future).
By the way, how much of the proceeds from sale of assets likes TREPS or LIQUIDCASE are available instantly in one’s trading account (not T+1) for delivery margin (not trading margin) ?
Looking to evaluate whether it is a worthwhile “complexity” for additional returns
that one can add to the system to snipe GSECs/SDLs from the secondary markets.
It used to be an inverse correlation right, hence the old 60-40 equity-debt portfolio allocation. 
Now the two are mostly correlated? 
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IMO, it appears that they are selling corporate bonds more than GSECs.
Probably now that the equity market is beaten down, this might look like an opportunity to invest more in equity (rebalance) by liquidating some of the bonds for funding it.
Also, another reason for selling the bonds right now could be due to the anticipated inflation from the West Asian War and skyrocketing energy prices.
I.e., It is very much possible that the interest rate could be hiked in the near future, which would further bring the bond prices down/spike the yields.
So, selling now at a better price and buying later at a much cheaper price could also be a reason for current selling in bonds.
Here’s something i read recently regarding this contangian effect, where all known behaviours/patterns no longer hold true, especially when all hell breaks loose.
Under normal conditions, government bonds act as a hedge. When equity markets fall, investors typically move into bonds. Bond prices rise, yields (interest rate on the bond) fall, and bonds act as a cushion in the portfolio. This is why the classic 60/40 portfolio exists. 60% stocks for growth, and 40% bonds for safety.
But every now and then, that relationship breaks.
Take periods of geopolitical shocks driven by energy crises. In times like this, the stock market usually falls. And when war pushes oil prices higher, the expectation for inflation tends to rise, too. And instead of buying bonds, investors start demanding higher yields to compensate for that inflation.
Therefore, bond prices fall at the exact moment they’re supposed to protect portfolios.
Obviously nothing comes risk-free, not even GSECs, but whatever the reason could be for selling them, it is certainly not because of default risk.
Inflation coupled with the depreciating rupees could probably be one among many reasons for looking at alternative investment options, to generate decent returns even during these chaotic times.
It is still paradoxical to me that even during times of war, the US Dollar is appreciating, when logically it should be depreciating.
The US has smartly placed itself in such a way that, by being the default reserve/transaction currency for the entire world, the demand for the US dollar remains irrespective of the economic conditions. Even Iran could probably be contributing towards the US $, by transacting in it for it weapons import.
Forcing the world to accept the US $ as reserve currency is probably the single biggest move any country could have made to get a piece of everyone else’s pie, they are like this middlemen who take a cut out of everyone else’s international trade/transaction.
Just like there was a talk about inherited risk, the US citizens have an Inherited advantage of a stable currency (in comparison to other nations).
Hope this unfair advantage breaks sooner, and De-dollarisation picks up pace.
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One way to read/interpret “inverse correlation” is as follows -
right now, when
equity is offering negative returns,
( typically expected to offer 8-18% returns over the long-term )
debt (with a 6-8% coupon-rate) is offering 10-12% returns
( periodic-interest + capital-gain on maturity ).
For example, a bond maturing in 10 years
of face-value 100 INR
and coupon-rate 7.5% ,
if purchased at 85 INR,
the effective returns from it are -
- 7.5 * 100/85 = 8.8% annual interest. (7.5 INR interest for investing only 85, not 100)
- (100-85)/10 = 1.5% annual gain (15% capital-gain in 10 years)
i.e. ~10.3% annualised returns guaranteed by the issuer of the bond for the next 10 years.
One key aspect to note is that the above returns are not compounding annually
(unless one reinvests them accordingly)
PS: Am not sure this is not what is is being referred to
when one encounters the “inverse correlation” phrase in most discussions.
PPS: Apparently, in the grand scheme of things, this “inverse correlation” is fairly recent,
and a high interest-rate regime reduces (or even eliminates) this inverse-correlation.
[ Source ]
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India yield curve(Rate vs time) current vs 1 year ago(Log time):
Yields spiked up for long duration bonds, while it crashed for short duration, suggesting more people are moving from long duration bonds to shorter duration bonds and T bills.
Source: Global Yield Curves: Explore & Compare Yields — TradingView
No bond is trading at a 10.3% YTM. Not now and have not been for a very long time.
Irrespective of the correlation, a key aspect of Govt bonds which we sometimes ignore is the fact that the bond yields are locked on the day of investment, provided one holds the bonds till maturity.
Unless one has found a better alternative that guarantees a higher return or one is badly in need of liquidity, selling during crisis makes no sense.
Price and yield fluctuations matter only when one wishes to liquidate in the interim, before maturity.
So, the Govt bonds do provide the hedge which they are intended to, irrespective of the market conditions, as the coupon rates are fixed and the face value is guaranteed on redemption.
It is only when we decide to sell during times like this, we end up incurring losses.
Even if the situation was opposite, i.e., the bond prices are rising and yields are falling, would most existing holders sell? If so, are the ones buying at these increased prices stupid?
Long-term bonds are always more volatile, especially when we anticipate inflation and interest rate hikes.
For retail, i believe short and medium term bonds are the logical investment choices.
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Technically Short term bonds crashed more than long term bonds spiked up. The only stable, less volatile ones seem to be the 10Y ones.
This statement only makes sense if we expect a recovery. Granted in most cases, it recovers and the default risk is low. But it’s not a absolute guarantee. Just because India or for that matter, any government, did NOT default for 70+ years doesn’t mean they won’t default today. In any asset, it’s not that the risk wasn’t there, but people become complacent about the risk and that’s when the crash happens and market teaches a lesson. People selling at the lows maybe wrong 9999 out of 10000 times, but if they are right that one time, they are the only ones who will remain liquid.
-
by “No bond” above,
i assume you are specifically referring to central government fixed-rate sovereign bonds (GSECs), since that’s what we started early-on in this discussion. Right? -
by “long time”,
how long into the recent past are you referring to?
While we began by talking specifically about GSECs earlier in this topic-thread,
subsequent posts also included SDLs and non-sovereign bonds
as the discussion has moved to equity vs. debt.
( Calling this out here
as it seems like others reading this topic-thread may easily misses this nuance.
Central-govt. “GSECs” are just one subset of “Bonds”.)
Absolutely. 9,999 is not 10,000.
For most folks, extreme odds aren’t intuitive.
By ignoring the difference as “close enough”, one end’s up with a sub-optimal decision. 
Also, on the topic of something being “risk-free”,
Risk isn’t a single fungible number.
An asset maybe free of a specific type of risk? Sure.
A recent discussion evaluating different types of risk in Equity vs Gold.
Maybe can do that now for also “… vs. Debt” ? 
However, for 9999/10000 odds (continuing with the example odds shared above),
are folks sufficiently invested upto 99.9%# of their expected debt fraction (40%) of their portfolio?
Based on what i read, i believe this is very unlikely.
Hence, i have been choosing to highlight these aspects of debt (especially sovereign) quite often.
# - Ensuring survival always supersedes maximizing your expected value.
To handle a total wipe-out in case of a loss, apply the Kelly criterion
to further limit exposure to a particular asset with some non-zero risk.
As an example,
consider the following approach
to determine the composition of the debt fraction (say the 40%) of one’s portfolio…
Multi-Asset Kelly Criterion: GSEC vs Corporate Bonds (vs. Cash)
Note: Numbers in parenthesis refer to % of total-portfolio.
(currently assuming a 40% debt allocation) .
| GSEC Yield | Corp Yield | GSEC Alloc. | Corp Alloc. | Cash Alloc. |
|---|---|---|---|---|
| 6% | 10% | 99.8% (39.9%) | 0.0% ( 0.0%) | 0.2% ( 0.1%) |
| 6% | 12% | 99.8% (39.9%) | 0.1% ( 0.0%) | 0.1% ( 0.0%) |
| 6% | 14% | 99.7% (39.9%) | 0.2% ( 0.1%) | 0.1% ( 0.0%) |
| 8% | 10% | 99.9% (40.0%) | 0.0% ( 0.0%) | 0.1% ( 0.0%) |
| 8% | 12% | 99.8% (39.9%) | 0.1% ( 0.0%) | 0.1% ( 0.0%) |
| 8% | 14% | 99.8% (39.9%) | 0.1% ( 0.0%) | 0.1% ( 0.0%) |
| 10% | 10% | 99.9% (40.0%) | 0.0% ( 0.0%) | 0.1% ( 0.0%) |
| 10% | 12% | 99.9% (40.0%) | 0.0% ( 0.0%) | 0.1% ( 0.0%) |
| 10% | 14% | 99.8% (39.9%) | 0.1% ( 0.0%) | 0.1% ( 0.0%) |
Table generated using the following Python script - kelly-bonds.py, for reference.
import math
def calculate_single_kelly(win_prob, win_payoff_ratio):
"""Calculates the Kelly Criterion fraction for a single asset."""
loss_prob = 1 - win_prob
kelly_fraction = win_prob - (loss_prob / win_payoff_ratio)
return max(0, kelly_fraction)
def calculate_multi_kelly(p1, b1, p2, b2):
"""
Calculates the Kelly Criterion for two independent simultaneous bets.
Uses a grid search to find the optimal fractions that maximize expected log wealth.
:param p1: Probability of winning Asset 1
:param b1: Payoff ratio of Asset 1
:param p2: Probability of winning Asset 2
:param b2: Payoff ratio of Asset 2
:return: Tuple of (optimal_fraction_1, optimal_fraction_2)
"""
best_f1, best_f2, max_g = 0.0, 0.0, -float('inf')
# Grid search with 0.1% resolution
steps = 1000
for i in range(steps + 1):
f1 = i / steps
for j in range(steps + 1 - i): # Constraint: f1 + f2 <= 1.0 (No leverage)
f2 = j / steps
# The 4 possible states of the world:
w1 = 1 + f1*b1 + f2*b2 # Both win
w2 = 1 + f1*b1 - f2 # Asset 1 wins, Asset 2 defaults
w3 = 1 - f1 + f2*b2 # Asset 1 defaults, Asset 2 wins
w4 = 1 - f1 - f2 # Both default
# If any outcome results in total ruin (wealth <= 0), this allocation is invalid
if w4 <= 0:
continue
# Calculate Expected Log Wealth (Geometric Growth Rate)
g = (p1 * p2 * math.log(w1) +
p1 * (1 - p2) * math.log(w2) +
(1 - p1) * p2 * math.log(w3) +
(1 - p1) * (1 - p2) * math.log(w4))
if g > max_g:
max_g = g
best_f1, best_f2 = f1, f2
return best_f1, best_f2
# --- Portfolio Allocation ---
PORTFOLIO_DEBT_FRACTION = 0.40
# --- Asset 1: GSEC / SDL ---
p_gsec = 9999 / 10000 # 99.99% chance of success
# --- Asset 2: Corporate Bond ---
p_corp = 90 / 100 # 90.00% chance of success
print("Multi-Asset Kelly Criterion: GSEC vs Corporate Bonds\n")
print(f"Assuming {PORTFOLIO_DEBT_FRACTION*100:.0f}% of portfolio is allocated to debt.\n")
print(f"{'GSEC Yield':>10} | {'Corp Yield':>10} | {'GSEC Alloc (Port %)':<20} | {'Corp Alloc (Port %)':<20} | {'Cash (Risk-Free)'}")
print("-" * 90)
# Model GSEC returns (6%, 8%, 10%) and Corp returns (10%, 12%, 14%)
for gsec_return in [6, 8, 10]:
for corp_return in [10, 12, 14]:
b_gsec = gsec_return / 100.0
b_corp = corp_return / 100.0
# Calculate optimal multi-asset allocation
f_gsec, f_corp = calculate_multi_kelly(p_gsec, b_gsec, p_corp, b_corp)
cash = 1.0 - (f_gsec + f_corp)
gsec_str = f"{f_gsec*100:>5.1f}% ({f_gsec*100*PORTFOLIO_DEBT_FRACTION:>4.1f}%)"
corp_str = f"{f_corp*100:>5.1f}% ({f_corp*100*PORTFOLIO_DEBT_FRACTION:>4.1f}%)"
cash_str = f"{cash*100:>5.1f}% ({cash*100*PORTFOLIO_DEBT_FRACTION:>4.1f}%)"
print(f"{gsec_return:>9}% | {corp_return:>9}% | {gsec_str:<20} | {corp_str:<20} | {cash_str}")
Few observations:
- At 10% Corporate Yield:
- The expected value is negative (0.9 * 0.10 - 0.1 * 1.0 = -0.01).
- Kelly allocates 0% to the corporate bond.
- At 12-14% Corporate Yield:
- The corporate bond becomes +Expected Value,
So, the Kelly-criterion logic, allocates a tiny portion (0.1%) to it,
while keeping the vast majority in GSECs.
- The corporate bond becomes +Expected Value,
- Cash Buffer:
- The model always leaves a tiny fraction in cash to prevent total ruin in the 1/10000 chance both default.
Next steps:
- Update the above script to account for inflation (an equal negative offset across all assets)
- Modify the probabilities and payoffs for each of the assets and check how the ideal allocation changes. I can bet that some of the results are going to be very non-intuitive for most folks who try this out.
So please do.Here are a couple of quick experiments (click to expand)
Q1. What is the probability of success for Corp. bonds
at which the Kelly-Criterion suggests a ~85-15 split between Corp. bonds and GSECs?GSEC Yield Corp Yield GSEC Alloc. Corp Alloc. Cash Alloc. 6% 14% 84.7% (33.9%) 15.2% ( 6.1%) 0.1% ( 0.0%) Q2. If the probability of success for GSECs increases/decreases by 10x,
what are the ideal split-ups of investment in GSECs vs. Corporate bonds,
according to the Kelly criterion? - Expected-Value is NOT Expected-Utility.
Optimizing for one (Kelly-Criterion) doesn’t always optimize for the other. - Explore why diversifying across asset-classes alone
cannot account for all Systemic Tail Risks and Liquidity Risks.
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Bond Market’s Big 2026 Fed Bet Flipped on Its Head by Oil Surge