bond yield – liquidity premium ^ credit spread

I guess an issuer A’s bond may trade at a Higher yield than an issuer B’s bond, even if A’s credit quality is higher. Paradox?

One reason i can imagine is liquidity preference. Suppose you are an investor. Suppose the 2 bonds have the same coupon and other features. You as well as the market know that bond A is (slightly) less likely to default than B, but you may still be willing to pay a bit more for B, because it’s easier to sell it when you need cash.

Bond A may be an unknown entity, traded on very few markets (including bank’s private distribution networks), so there are far fewer bids of A then B. The lower market access leads to lower buyer competition, lower bid prices when you are forced to sell. 
Therefore, a prudent investor may be willing to pay more for bond B.
Since many investors behave similarly, B gets higher buyer competition, higher valuation and lower yield (spread).

Similarly, McDonald burgers may be smaller but more expensive than the no-name burger next door — liquidity

## which skill/field CAN be self-taught

Domain knowledge is easier to “fake” than c#/c++. It’s hard to fake the mileage…

Q: which fields can be truly self-taught to in-depth? For example, mobile apps, desktop apps.

Goal #1: pass job interviews
Goal #2: reach the professional level of Theoretical expertise
Goal #3: reach the professional level of Practical expertise

* Quant? too dry. Too many doubts to be clarified only by professor. However the basic math part is standard and well-defined. Goal 3 is invalid.
* high-frequency, algo? Many books but none on real tricks. Compare swing books! Very few jobs.
* network/linux optimization? need machine to try. Extreme optimization techniques are top secrets.
* FIX? Many practical issues hard to imagine. No focus.


* swing, wpf? — tricks can be self-taught through experiment. Practical books are available. But practical problems are unknown.
* c# and core libraries? Real obstacle is the IDE. Practical books abound.
risk-mgmt? impossible to experiment.
coherence? too many subtopics. no focus. Hard to experiment.
tibco? no book at all. Hard to experiment
threading? Can hit Goal 2, not 3

WCF (and related) jargon, a short list + 1-line notes

background — I used wcf many times but a clear understanding doesn’t come automatically.

See other blog posts about the “Service” jargon.

A TYPICAL wcf service is characterized by these items
– always remote;
– always requires serialization across network
– always uses a public url (part of the so-called service endpoint)
– protocol — typically http or tcp
– serializer/formatter/encoding — typically xml or binary
– channel
– there must exist a local proxy INSTANCE for some remote “object”, though the “service” concept isn’t strongly object-oriented.
– hosting process — any process that stays up, such as —
** console host
** winforms, wpf
** windows service (similar to linux daemons, visible on services applet)
** web server

A wcf service must be hosted — basically a server process. In contrast, the client process can be short-lived. The host is the realization of a lot of the above abstract concepts.

wcf and soap? wcf is frequently implemented using soap + other technologies

wcf and the remoting technologies? see separate post

fwd curve ^ spot curve – different x-axis!

ICAP terminology —
effDate := accrual starts. Therefore, fwd-starting deals have effDate long after execution date.
maturity := accrual ends.
tenor := accrual period length := maturity – effDate

A Fwd curve consisting of 22 points describes 22 FRA deals with identical accrual length, but 22 different accrual-start dates. (Both Mark Hendricks and NYU.)

spot rate r(t)= rate of a loan starting today, with maturity = t
fwd rate f(t) = rate of a FRA starting at t, with a standard and Implicit tenor (say, 6M)

* spot curve with 33 numbers describes loans with 33 different loan maturities
* fwd curve with 22 numbers describe FRA deals with Identical accrual length, but 22 different fwd-start dates.

So the x-axis has different meanings! Relationship between the 2 curves are described by Mark Hendricks + Bruce Tuckman with an intuitive explanation!.

I believe the fwd curve (with infinitesimal tenor) is based on the theoretical concept of instantaneous fwd rate (IFR)… but let’s not get bogged down with technicalities.

fwd px ^ px@off-mkt eq-fwd

fwd price ^ price of an existing eq-fwd position. Simple rule to remember —
QQ) not $0 — fwd price is well above $0. Usually close to the current price of the asset.
EE) nearly $0 — current “MTM value” (i.e. PnL) of an existing fwd contract is usually close to +-$0. In fact, at creation the contract has $0 value. This well-known statement assumes both parties negotiated the price based on arb pricing.

Q: With IBM fwd/futures contracts, is there something 2D like the IBM vol surface?

2 contexts, confusing to me (but not to everyone else since no one points them out) —

EE) After a fwd is sold, the contract has a delivery price “K” and also a fluctuating PnL/mark-to-market valuation “f” [1]. Like a stock position (how about a IRS?) the PnL can be positive/negative. At end of day 31/10/2015, the trading venue won’t report on the MTM prices of an “existing” contract (too many), but the 2 counter-parties would, for daily PnL report and VaR.

If I’m a large dealer, I may be long/short a lot of IBM forward contracts with various strikes and tenors — yes a 2D matrix…

[1] notation from P 109 [[hull]], also denoted F_t.

QQ) When a dealer quotes a price on an IBM forward contract for a given maturity, there’s a single price – the proposed delivery price. Trading venues publish these live quotes. Immediately after the proposed price is executed, the MTM value = $0, always

The “single” price quoted is in stark contrast to option market, where a dealer quotes on a 2D matrix of IBM options. Therefore the 2D matrix is more intrinsic (and well-documented) in option pricing than in fwd contract pricing.


In most contexts in my blog, “fwd price” refers to the QQ case. However, in PCP the fwd contract is the EE type, i.e. an existing fwd contract.

In the QQ context, the mid-quote is the fwd price.

Mathematically the QQ case fwd price is a function of spot price, interest rate and tenor. There’s a simple formula.

There’s also a simple formula defining the MTM valuation in EE context. Its formula is related to the QQ fwd quote formula.

Both pricing formulas derived from arbitrage/replication analysis.


EE is about existing fwd contracts. QQ is about current live quotes.

At valuation time (typically today), we can observe on the live market a ” fwd price”. Both prices evolve with time, and both follow underlier’s price S_t. Therefore, both prices are bivariate functions of (t,S). In fact, we can write down both functions —

QQ: F_t = S_t / Z_t ….. (“Logistics”) where Z_t is the discount factor i.e. the T-maturity discount bond’s price observed@ t
EE: p@f = S_t – K*Z_t

( Here I use p@f to mean price of a fwd contract. In literature, people use F to denote either of them!)

To get an intuitive feel for the formulas, we must become very familiar with fwd contract, since fwd price is defined based on it.

Fwd price is a number, like 102% of current underlier price. There exists only one fair fwd price. Even under other numeraires or other probability measures, we will never derive a different number.

In a quiz, Z0 or S0 may not be given to you, but in reality, these are the current, observed market prices. Even with these values unknown, F_t = S_t / Z_t formula still holds.

Black’s model – uses fwd price as underlie, or as a proxy of the real underlier (futures price)

Vanilla call’s hockey stick diagram has a fwd contract’s payoff curve as an asymptote. But this “fwd contract’s payoff curve” is not the same thing as current p@f, which is a single number.