Chain pricing


There are many examples of related prices that move in the same way. It is normal because many assets are related (due to technological, behavioral, natural, political reasons) and some events affect many of them simultaneously. But what about the efficiency of these relationships? Latter may be very complex and nonlinear. The idea is simple – to find related pairs of assets that look good separately, but considered together, contain opportunities.

For example, what if the relationship leads to convex dependence between prices? If the first price goes up, the second price responds with a strong upward movement. If the first price goes down, the second responds with a weak downward movement. In this case, it is possible to create arbitrage portfolios – that always win. Even if the relationship is not strong and exists on average or in certain situations, it is possible to make use of it.

In an ideal world, it has to be impossible. But we do not live in an ideal world. The more I look the more I find examples of such type of relationship. They exist because so are economical relationships. Moreover, it looks like processes in the real economy (technological, natural, political, etc) that create such behavior know nothing about arbitrage, free lunch, efficiency, and related conceptions. It seems to be pretty fundamental.

There are a lot of nonlinear usable dependencies between prices. Using financial engineering we can create instruments and portfolios to make profitable use of them.

Theoretical question. What would be the world without these opportunities? If they are not efficient because of arbitrage then someday they will disappear.

Practical question. How to make better use of them?

The following is a hypothesis based on observations and modeling of economical processes. I see reasons why it may be hard to observe but I do not see reasons why it could be impossible to use.


When a national currency falls significantly, some of the local companies nominated in this currency also become cheaper and vice versa. Share price measured in a foreign currency is share price in a national currency multiplied by the exchange rate. In this case, both multipliers change in the same direction. Consequently, this chain effect makes share price to be non-linearly (convexly) dependent on the currency. A portfolio consisting of assets with linear (currency) and convex (stock) returns is not arbitrage-free.

The same thing is to debt instruments with different maturities. The price of a long-term debt instrument can be seen as a multiplication of the price of a short-term debt instrument and a discount for maturity change. The discount reflects the expected short-term rate for that period. Observed and expected short-term rates usually move in the same direction.

In general, such chains can be found almost everywhere. Diverse technological chains contain the same effect. The price of a product is expenses multiplied by parameters reflecting risks, interest rates, profit factors, taxes, etc. But expenses include the prices of other products. There are situations (for example, if the systematic risk/international beta changes) that affect both multipliers. Production chains may be long and entangled. The price of an end product may contain parameters multiplied by themselves many times.

The convexity

First, let us see how it works in theory. Assume we have two assets A and B with prices P^A_t and

P^B_t=P^A_t \cdot c(P^A_t) (1.1)

Let P^A_t and c(P^A_t) move in the same direction. When P^A_t rises, c does the same and vice versa. Consequently, P^B_t = f(P^A_t) is convex function because of multiplication and relation.

We create portfolio with the following value:

V=a \cdot P^A_t + b \cdot P^B_t (1.2)

It is convex with minimum at some point P_min. By choosing a and b we can make P_min=P^A_0. In this case portfolio is always profitable and there is arbitrage opportunity. Coefficients can be chosen from the following equality:

\frac{d}{dP^A_t}\cdot V(P^A_0)=0 (1.3)

For example, it may look like


Fig.1.1 Non-linear portfolio

In real life price models are complex and contain many parameters. Relationships between prices are often more complex than (1.1). But still, some components of price models may follow (1.1). I believe it is possible to extract and enhance such behavior.

Debt and risky investments

Discounting of future cash flow is an exponential process that depends on time period before maturity and interest rate.  It can be also seen as a multiplication of expected annual returns and other ways, but all models describe the same nature of time value of money. The value today of receiving one unit of currency years in the future:

P=e^{r_{t_1, t_3} \cdot (t_3 - t_1)} (2.1)

It has the following important property:

e^{r_{t_1, t_3} \cdot (t_3 - t_1)}=e^{r_{t_1, t_2} \cdot (t_2 - t_1)} \cdot e^{r_{t_2, t_3} \cdot (t_3 - t_2)} (2.2)

Interest rate r_{t_1, t_2} and expected interest rate r_{t_2, t_3} do not have to be equal all the time, term structure of interest rates can change, but still they usually move in the same direction. They reflect risk-free interest rate and risks. If something serious happens we understand that we live in a new reality and both short-term and long-term risks change.

If this is the case then it is possible to create an arbitrage portfolio from (2.1) and (2.2) in the same way as in (1.2).

The following figures depict how 30-year Treasury security yields (y-axis) are related to 5-year Treasury security yields (x-axis).


Fig.2.1 30-years T-security yields (y) and 5-year T-security (x) from 03/15/2010 to 03/13/2020. Data from


Fig.2.2 30-years T-security yields (y) and 5-year T-security (x) from 03/15/2010 to 06/25/2013. Data from


Fig.2.3 30-years T-security yields (y) and 5-year T-security (x) from 06/26/2013 to 10/05/2016. Data from


Fig.2.4 30-years T-security yields (y) and 5-year T-security (x) from 10/06/2016 to 03/13/2020. Data from


Fig.2.5 30-years T-security yields and 5-year T-security from 03/15/2010 to 03/13/2020. Data from

As you can see, on average, yields move in the same direction.

Chain pricing

To produce goods and provide services companies use other goods and services. They buy from suppliers and transform (add value) to create their products. Thus production chains are created. If something happens that affects all parts of a chain then the effect is increasing as it goes through this chain. It affects the first company, then the second company becomes affected by the first company and by the event.

Let asset B price contains price of asset A (P^A), proportional expenses (e^B), constant expenses (E^B), risks (c^B) and risk-free rate (r^B):

P^B = (P^A+E^B)\cdot x^B (3.1)

where x^B=e^B \cdot c^B\cdot e^{r^B\cdot t}

Price of asset A and expenses (fares, taxes, etc.) are investments. After period T one expect to have risk-free return increased by a risk factor (or cost of insurance).

There is the second company that buys asset B and transforms it into asset C using the same process (3.1) (production or transportation). Both companies operate in the same conditions, to which they are perfectly fit. Their pricing model includes the same parameters. Price of asset C:

P^C = (P^B+E^C)\cdot x^C (3.2)

P^C = (P^A+E^B)\cdot x^B \cdot x^C+E^C\cdot x^C (3.3)

if x^B and x^C change in the same direction then (3.3) is convex. A portfolio consisting of P^B and P^C is not arbitrage-free.

Many parameters affect prices in a chain way. For example, risks, taxes, oil prices, global events, etc.

Practical issues

The main issue is that real-life pricing is far more complex than described above. Price is determined by supply and demand. There are many parameters influencing prices, not all of them can create nonlinearities. The necessary condition that “two multipliers in a price model move in the same direction” usually holds on average, but not for every price change. But still, more or less hidden, it exists. It is a matter of financial engineering to make efficient use of it. From the practical point of view, those parameters that do not create convex behavior create noise. But hopefully, there are strategies for noise cancellation.

Hedging and diversification could help. The influence of linear parameters can be removed by adding proper instruments to the portfolio. We can also use derivatives like exotic options.

Different instruments reflect the chain effect differently. For example, if one invests in bonds then return depends on the implied interest rate and time (time to maturity decreases as he holds a security). Instead, he can buy a forward contract that price depends only on the interest rate.

The best way would be the creation of instruments with certain behavior that follow a simple model (like mentioned above). For example, something that only depends on a risk with constant and simple term structure. The convexity seems to be very natural as it reflects normal processes in the economy. Consequently, it is possible to create instruments that reflect this effect and are free of noise.


We keep in mind that theoretical efficient market and the real one are not identical. But to what extent? It seems that described above chain effect is not efficient because it creates opportunities that should not exist. But it reflects processes in the real world: technological, natural, political, etc. It means that an efficient market might not be what we think of it.

Arbitrage opportunity cannot always exist because it allows unlimited risk-free profit and somebody has to pay for this (be a source of money). So, to find out what limits arbitrage we need to follow the money.

There are different types of market actors: manufacturers, resellers, investors, etc. They all do their business for profit. If someone has excess profit then another one has a loss. No one wants to lose money to make others wealthier. This moves the market to equilibrium and arbitrage-free state.

This logic is related to everybody on a market except end consumers. They buy things not for profit but to consume. Their pricing model is not based on the return they want to make, but the individual consumer value of the products they buy. If no other explanation is found then end consumers should be the source for described above opportunities. It should be they who can to limit such opportunities.

Thoughts about the relativity of prices.

Relativity of prices


Here I present a hypothesis that price efficiency is a relative conception. Efficiency depends on what you want to maximize. On efficient market prices are optimized to the goal. For example, price should not allow “free lunch”, i.e. making more than risk-free profit without taking a risk. If price is higher then demand rises and price goes down. If price is lower then demand falls and price goes up. But what if different people have different goals? If you want to make more euro then you have increased expectations of return from investments into USA companies comparing to EU companies. Moreover, is amount of currency what do you want the most? Maybe you want to consume more goods and services instead of currency? If so then in theory there are strategies that allow you to earn what you want.

It should be noted that all written below correspond with arbitrage theory.

It is all about the probabilities

Consider a security which payoff at moment T H_T depends on some event at that moment. If there is only one scenario of future then price of a security is equal to discounted payoff at moment T:

H_0^A=P^A\cdot H_T^A   (1)

P^A is a discount factor, H_T is a payoff, A is a numeraire.

If there are different scenarios but derivative pays off only in one of them (and zero otherwise) then coefficient should be added to the previous equation:

H_0^A=P^A\cdot H_T^A\cdot p^A   (2)

This security can be used to construct other securities with a complex payoff:

H_0^A=P^A\cdot \sum_{i=1}^n(H_T^A(i)\cdot p_i^A)   (3)

Scenarios can be following: “price on underlying asset is equal to X or is within range I”, “next president will be Mr.T” or other events.

As we can see, p_i^A has a role of scenario i probability. If p_i^A differs from the probability that we think is real then we expect return to be above the risk-free rate for asset A.

Let’s take a look from a different point of view and change numeraire from A to B – we are interested in maximizing asset B rather than A. Then

H_0^B=P^B\cdot \sum_{i=1}^n(H_T^B(i)\cdot p_i^B)   (4)

But there is an exchange rate between A and B. Payoffs and securities can be exchanged:

H_T^B(i)=H_T^A(i)\cdot N^AB_T(i)   (5)

H_0^B=H_0^A\cdot N^AB_0 (6)

N^AB_t(i) is an exchange rate between asset A and B at the moment t.

To hold (3), (4), (5) and (6) p^A(i) and p^B(i) have to be connected:

p^B=p^A\cdot \frac{P^A}{P^B}\cdot \frac{1}{N^AB_T^B}\cdot \frac{1}{N^AB_0^B}   (7)

If N^AB_T(i) is not constant then pi^A and pi^B are different. So, they are not real probabilities. Real probabilities that reflect situation in the real world are not equal to pi^A, pi^B or both. Consequently, it is possible to construct a portfolio with average return exceeding the risk-free rate for asset A, B or both.

How to use this

Excess average return exist if value of numeraire changes. Every price is a ratio: value of asset divided by value of numeraire. Assume you hold a long position. If numeraire drops by 90% then price will be 10 times more then it was. It numeraire goes up (+90%) then price will be only 2 times (almost) less. In average you win.

You may ask why would you need such prize if numeraire falls? But this question is more complex than it may look like.

You can construct a strategy that almost always wins in the long run and has a limited loss. With 90% probability you win one unit of domestic currency, with 10% – lose the same one unit. You lose if value of domestic currency rises significantly. Loss of one unit of domestic currency is equal to loss of many more units of other currencies. If it does not matter to you then you are ok. For example, if your income from other activities is not affected by domestic currency rise you may win even in this situation, because imported goods become cheaper.

Another option takes place if consumer value of two assets are equal to you no matter how their prices change. You can use them both as numeraire. There are strategies that guarantee you excess return at least in one numeraire.

Average return depends on what numeraire you and others use (asset you want to maximize). Generally speaking, we all are different and interested in maximizing different assets (e.g. currencies, goods, services). We work harder to consume more goods and services. But value of goods and services is individual. One may want an island, another – plane. Our goals are different. Consequently, excess average return is a normal thing.

It seems that there should be only one numeraire in the world with “normal” average return. Every price is an exchange rate – a ratio of values of two assets. Price is a relative thing. We measure value of a single asset by comparing it with other assets (e.g., with one chosen as a numeraire). If we imagine some extremely stable asset with no interest rate then it could be used as a universal measure. If this asset is numeraire then its average return have to be equal to zero. Arbitrage opportunity exist otherwise.

Arbitrage conditions

Arbitrage opportunity may occur if dependencies between prices are not efficient. There are limitations on N^AB_T(i). For example, the sum of p^X(i) has to be equal to one for every numeraire X:

\sum_{i=0}^{n} p^X(i)=1   (8)

Arbitrage opportunity exist otherwise. p^X(i) should be probabilities in mathematical sense, but they may be not if market decides so.

In the example above (90%/10% game), you may want to buy shares of importers. They win from domestic currency rise. You may try to use their stock to compensate loss. But there should be no arbitrage opportunities on an efficient market. Consequently, value of importers should behave in such a way to make this strategy not working. The same situation is with insurance (of currency rise) and hedging. But do they behave in such way in the real world?

Dependencies between assets may be nonlinear and complex. This, in turn, may lead to potential inefficiencies and corresponding opportunities. Let’s take a look on equations (7) and (8). Only such N^AB_T(i) is efficient that do not brake (8). Are all dependencies between prices efficient? I don’t think so. Dependencies between prices reflect real world processes (technological, natural, etc). Such things as changes in interest rates, referendums, election results, natural disasters and even season change affect many prices simultaneously. Most companies are connected to many other companies and form long chains. It is like a three-dimensional spider’s web.

Here is an investigation of dependencies between prices or “chain pricing” effect.

Let’s conduct an experiment

If we have positive average return and make many independent operations then we diversify result and transform positive average into almost certain profit – the probability of profit tends to one. Let’s demonstrate this. We have two assets: A and B with prices x^A_j and x^B_j, respectively. Let

x^A_0=x^B_0=1   (9)

Let ratios of two consequent elements be normally distributed:

x^A_{j+1}/x^A_j\sim N(1,\sigma^2)   (10)

x^B_{j+1}/x^B_j\sim N(1,\sigma^2)   (11)

\sigma is a standard deviation.

Numeraire is asset A. So we deal with price

S_j=\frac{x^B_j}{x^A_j}   (12)

Mean value for any long position is positive. Let V_j be a value of portfolio at the moment (iteration)$latex  j$. V_0=1. Every iteration we calculate position – amount of asset B that we hold from j until j+1 moment:

pos_j=\frac{c}{S_j} \cdot V_j   (13)

c is a coefficient. Division by S_j is added for diversification purposes (to make individual results independent of price). All positions are long. The maximum loss is the initial value V_0.

Value V_{j+1} is as following:

V_{j+1}=V_j + pos_j \cdot (S_{j+1}-S_j)   (14)

Modeling of 100 cases gives following results


Fig.1 Value of portfolio


Fig.2. Value of portfolio (logarithmic scale)

Figures look good, but no arbitrage opportunities exist here. At least, not with random data. As the first experiment with real data, I decided to test exchange rates between 150 stocks (first 150 from NYSE alphabetical list) and Dow Jones Industrial Average  (using SPDR Dow Jones Industrial Average ETF – DIA) with a little bit adapted strategy. Numeraire is USD.


Fig.3. Real data

This is a very simple strategy that does not consider real limitations of trading like transaction costs. However, it shows that dependencies between prices are complex and contain opportunities that can be used. I think the reason is that processes in the real economy – technological, political, natural and others, do not always follow financial market laws, but they should.