yield-/smile-curves are !! directly related to greeks

The yield curve and smile curve (or surface) are the output of curve fitting engines, calibrated using a lot of commercial market data, discarding outliers. These are the most valuable soft market data objects. Directly used by decision makers, including trade pricing. Each trading desk guards these curves as highly proprietary trading secrets.

However, these curves are imprecise. You shouldn’t compute the gradient at each point on these curves. The most you do with such gradient is computing the gradient at the anchor vol point, which gives you the skew of the entire curve.

In contrast, the price-vs-impliedYield curve and the valuation-vs-spot curve are a different class of curves. (The valuation-vs-impliedVol curve is another example.) I call these “range of possibilities” curves. They are mathematically precise enough to let us compute gradient at every point. You get duration and delta. These are known as sensitivities, essential soft market data for risk management.


quantitative feel of bond duration – mapping absolute 1% -> relative x%

In the simplest illustration of modified duration, if a bond has modified duration == 5 years, then a 100bps yield change translates to 5% dollar price (valuation) change.

Note that 100 bps is an Absolute 1% change in yield, whereas the 5% is a Relative 5% change in valuation. If original valuation == $90 [1], then 100 bps =>> $4.5 change.

After we clear this little confusion, we can look at dv01. Simply set the absolute yield change to 1 bp. The valuation change would be a Relative 0.05% i.e. $0.045. The pattern is

Duration == 5 years => dv01 == 0.05% Relative change
Duration == 6 years => dv01 == 0.06% Relative change
Duration == 7 years => dv01 == 0.07% Relative change

Note 0.05% Relative change means 0.05% times Original price, not Par price.  Original price can be very different from par price, esp. for zero bonds.

[1] 90 in bond price quote means 90% of par value. For simplicity we would assume par is $100, though smallest unit is $1000 in practice.

(See P10 of YieldBook publication on Duration.)

when is rho important

A: long-dated options.

I was told for both equity options and currency options, rho is usually the least significant price sensitivity greek.

For a customized “structured” option, maturity could be 10 years or longer. I feel the BS assumption of a constant risk-free rate breaks down — Over 10 years, interest rate could quadruple. Stock price could be directly affected even if all other factors remain constant. Implied vol could also be affected. However, to compute theoretical rho, we need to hold all of them constant and simulate a small bump in the time-invariant interest rate. In that context, over 10 years the effect on option valuation is non-negligible.

greeks(sensitivity) – theoretical !! realistic

All the option/CDS/IRS … pricing sensitivities (known as greeks) are always defined within a math model. These greeks are by definition theoretical, and not reliably verifiable by market data. It’s illogical and misleading to ask the question —
Q1: “if this observable factor moves by 1 bp (i.e. 0.01%) in the market, how much change in this instrument’s market quote?”

There are many interdependent factors in a dynamic market. Eg FX options – If IR moves, underlier prices often move. It’s impossible to isolate the effect of just one input variable, while holding all other inputs constant.

In fact, to compute a greek you absolutely need a math model. Without a model, you can say instrument valuation will appreciate but not sure by how much.

2 math models can give different answers to Q1.

theta = a rent to own gamma #my take

* large positive gamma ~ large theta decay. Note theta is always negative since option valuation always decays with time.
* small positive gamma ~ small theta decay.

The extreme cases often help us simplify and better remember the basics —
– Large gamma is characteristic of ATM options
– Small gamma is for deep ITM/OTM options.

Some say “theta is a rent to own gamma”. Imagine you delta hedged your ATM long position — long gamma. Long gamma gives you upside profit potential whether underlier moves up or down. That’s an enviable position, but comes at a “rent” — With every day passing, you position loses value thanks to decay. The loss amount is the daily theta value (always negative). The larger the “upside” (gamma), the higher the daily rent (theta).

Negative gamma is for short option positions {large Negative gamma ~ large Positive theta}. In this blog we focus on long call/put positions either European or American style, so all gammas are positive.

vega roll-up makes no sense #my take

We know dv01, duration, delta (and probably gamma) … can roll up across positions as weighted average. I think theta too, but how about vega?

Specifically, suppose you have option positions on SPX at different strikes and maturities. Can we compute weighted average of vega? If we simulate a 100bps change in sigma_i (implied vol), from 20% pa to 21% pa, can we estimate net change to portfolio MV?

I doubt it. I feel a 100 bps change in the ATM 1-month option will not happen in tandem with a 100 bps change across the vol surface.

– Along the time-dimension, the long-tenor options will have much __lower__ vol changes.
– Along the strikes, the snapshot vol smile curve already exhibit a significant skew. It’s unrealistic to imagine a uniform 100 bps shift of the entire smile (though many computer system still simulates such a parallel shift.)

Therefore, we can’t simulate a 100 bps bump to sigma_i across a portfolio of options and compute a portfolio MV change. Therefore vega roll-up can’t be computed this way.

What CAN we do then? I guess we might bucket our positions by tenor and aggregate vega. Imperfect but slightly better.

2nd differential is the highest differential we usually need

When I first encountered the concept of the 2nd derivative, I thought maybe people will be equally interested in the 3rd derivative or 4th. Now I feel outside physics (+ math itself), folks mostly use first derivative and 2nd derivative. In classical physics, 2nd derivative is useful — acceleration. Higher derivatives are less used.

Note on notation. f” is (inherently) a function in the black-box sense that for each input value, there’s an output value. This function derives from the original function f. We write f”(x) in that context. However, f” can be usefully treated as a independent variable just like x,y and t, so we write f” without the (x). In this context, we aren’t concerned about how f” depends on x. That dependency might be instrumental in our domain, but at least for the time being we endeavor to ignore that and concentrate on how f” as an independent variable affects “downstream”

Graphically, 2nd derivative describes concavity or convexity —
^ When f” is positive, curve is concave upwards, regardless whether f(x) is positive or negative, whether f(x) is rising or falling, whether f'(x) is positive or negative. However, f’ is rising for sure.
^ When f” is Negative, curve is concave Downwards, regardless.

This observation is relevant to portfolio gamma. Take a short put for example. This position’s delta is always Positive but Falling with S, towards 0. The PnL graph is concave downward, so this gamma is always Negative. (See https://www.thinkorswim.com/tos/displayPage.tos?webpage=lessonGreeks) It’s important to clarify a few points and assumptions implicit in the above context —

* This PnL graph is purely theoretical. Underlier (S) has just one price of $88 right now, and it won’t become $1 even though the graph includes that price on the S axis.
* PnL graph is about the Current valuation (and PnL) but with Imaginary S prices. It shows “what-if” underlier price S becomes $1 in the next moment — that’s the meaning of the $1 on the x-axis.
** However, the most useful part of the PnL curve is the region around the current S — $88. This region reveals our sensitivity to underlier moves. It shows how much our short put valuation (and PnL) would gain or suffer When (not “if”) underlier moves a tiny bit in the next moment.

* The delta curve is purely theoretical. At the current S = $88, our delta is, say 0.51 or 0.47 or whatever. It won’t suddenly become 0.01 even though you may see this delta value at a high S. That 0.01 delta means “if S becomes so high tomorrow, our delta would be 0.01”

* there’s no “evolution over time” depicted in any of these graphs. Time is not an axis. These curves are pure mathematical functions describing “what if S is at this level”. In this sense the delta curve is very similar to the price/yield curve. Even the standard yield curve and forward curve are similarly Unrelated to so-called time-series graphs.

If you are confused about “on the far right put is OTM, but on a smile curve OTM puts are on the far Left”, read my other blog posts about OTM put.

beta, briefly

Beta is calculated using regression analysis, and you can think of beta as the tendency of a security’s percentage Returns (not the continuously compounded return) to respond to swings in the market (represented by a benchmark). A beta of 1 indicates that the security’s price will move at the same magnitude with the market. A beta of less than 1 means that the security will be less volatile than the market. A beta of greater than 1 indicates that the security’s price will be more volatile than the market. For example, if a stock’s beta is 1.2, it’s theoretically 20% more volatile than the market.

For example, many utilities stocks have a beta of less than 1. Conversely, most high-tech stocks have a beta of greater than 1, offering the possibility of a higher rate of return, but also posing more risk.

Zero beta means 0 correlation with the index (i.e. the market), i.e. independent, insulated.

Negative beta means anti-correlation, or bucking the market.

If the market is always up 10% and a stock is always up 20%, the correlation is one (correlation measures direction, not magnitude). However, beta takes into account both direction and magnitude, so in the same example the beta would be 2 (the stock is up twice as much as the market).

I feel Beta is more important to the buy-side than the sell-side. Note many sell-side megabanks have buy-side units too.

Beside the standard beta on Return, there’s also what I call “vol-space” beta — where a beta of 1 means IBM realized vol over the past 2 years has identical magnitude of ups and downs as s&p (the benchmark) realized vol. This vol-space beta is calculated using 2 years of historical volatility numbers.

computing delta value from BS formula – unrealistic

Delta is the option-valuation change due to a small change in underlier. We are asking “using the BS formula as a prediction of real market, if there’s a $.01 change in underlier, what’s the change in bid/ask of this listed option?”

Assumption — all the bid/ask quoters in the option market use roughly the same BS formula. Obviously unrealistic. I don’t feel this assumption would help make delta a random variable.

Assumption — large number of bid/ask quoters responding to the underlier change. Realistic? I doubt it. Many quotes may ignore the spot change. I guess a small number of dealers/funds might _concentrate_ on a sector and are responsible for a disproportional part of the order book on that option. If they ignore the spot change, then bid/ask will stay constant and delta is 0!

Assumption — no change in i-vol when underlier changes. I feel real players in the market are emotional and may respond emotionally to whatever event causing the $.01 change. These emotional reactions can /effect/ a change in i-vol. I think some trend recognition machines may recognize this small change as part of a trend. I don’t feel such a market response is random.

What if the change is not $.01 but a 2% change?

Assumption — underlier price change is fairly slow and small. Obviously unrealistic. Therefore the change in option bid/ask in response to underlier change doesn’t always follow math model.

More important factor — An option is often held along with an underlier position as part of a strategy. When underlier moves, the holder may want to adjust her option position. One choice is adjusting her option quotes (limit orders). If she is a powerful market maker, then her new quote can move the best bid/ask. Therefore option valuation as measured by mid price may not move according to any math model.

dv01 ^ duration – software algorithm

Q: Do dv01 and duration present the same level of software complexity? Note most bonds I deal with have embedded options.

I feel answer is no. dv01 is “simulated” with a small (25 bps?) bump to yield… Eff Duration involves complex OAS. See the Yield Book publication on Durations.

In AutoReo, eff duration is computed in a separate risk system — a batch system… No real time update.

By contrast, eq option (FX option probably similar) positions need to have their delta and other sensitivities updated more frequently.