One of the first and biggest concerns of Mathematical Finance is to study the prices of a suitable set of risky financial assets of any type, including stocks, indexed bonds, variable rate deposits, derivative securities, and so on. Usually, financial assets are modeled as random variables on some state sets, which are supposed to be the same for every asset in the considered market.

In earlier models, such as the one leading to the celebrated Black, Scholes, and Merton formula for option pricing ( Black & Scholes, 1973 ; Merton, 1973 ), the market is supposed to be a perfect one; in particular, no frictions are featured and no bid-ask spreads or commissions affect prices. Consequently, as it is clearly shown, for instance, in Dothan (1990) , Pliska (1997) , and Björk (1999) , asset prices turn out to be linear with respect to assets themselves, in the sense that the price of the sum of two positions exactly equals the sum of the two separated prices. In such a setting, a central result is found, called the Fundamental Theorem of Asset Pricing, of which dozens of variants are featured in the literature (besides the cited books see, for instance, Delbaen & Schachermayer, 1994 , for quite a general version), which essentially is a representation theorem: roughly speaking, the market prices do not allow for arbitrages (i.e., free gains without risks) if and only if there exist a probability measure called the risk neutral probability―and a discount factor such that prices themselves are (discounted) expected values of the future asset values.

The perfect market model, however, quickly proves to be unfit to provide a good description of realistic markets. For instance, it may be impossible to obtain the exact replication of a given pay-off, and therefore no linear price can be given for it. In such a case, as investigated by Davis & Clark (1994) , the investor may naturally aim at super-hedging it, i.e., at getting at least as much as needed (possibly more) at the minimum possible price. There are also cases when, due to market frictions, a dynamical strategy that exactly replicates the given pay-off may turn out to be more expensive than a super-hedging one: see, e.g., Hodges & Neuberger (1989) . El Karoui & Quenez (1995), Jouini & Kallal (1995), Jouini (1997), and Cvitanić et al. (1999) among others, had investigated such a setting and found a representation theorem: in any case, super-hedging prices turn out to be the maximum of a family of linear prices, which, for instance, Cvitanić et al. (1999) interpreted as prices in “shadow markets”. Moreover, Pham (2000) studied the properties of super-hedging price functionals to find them sublinear: additivity was replaced by subadditivity, meaning that the price of the sum of two positions might be cheaper than the sum of the two separated prices. Since sublinearity entails positive homogeneity besides subadditivity, it turns out to be perfectly fit to describe markets affected by proportional transaction costs (such as taxes or percentage commissions): see, e.g., Pham, Touzi, & Touzi (1999) . Furthermore, sublinearity turned out to be interesting for risk management purposes as well, being the foundational point for the celebrated paper by Artzner et al. (1999) on coherent risk measures.

A class of risk measures more general than the sublinear (i.e., coherent) ones of Artzner et al. (1999) is proposed by Föllmer & Schied (2002) , who replace sublinearity with the weaker convexity¹. Inspired by their work, we started wondering whether convex price functions may sensibly be adapted to financial markets: we realized that this was naturally the case, for instance, if unit asset prices are supposed to increase with respect to the traded amount. A representation result can be found, stating that convex price functionals are the upper envelope of a family of affine prices, which admit an interpretation similar to the “shadow markets” of Cvitanić et al. (1999) .

Another, further generalisation, may require average unit asset prices to be increasing, instead of “marginal” ones. This may be the case, for instance, when an agent can choose to buy an asset on several different markets, featuring different increasing unit prices: of course, the purchase will be conducted in such a way that the overall price (or, which is the same, the average unit price) is as low as possible. This leads to a totally new class of price functions, which we name star-shaped because their epigraph turns out to be a star-shaped set with respect to the origin, in the sense of Stewart & Tall (1983) . A representation result can be given for this class of functionals as well, with an interesting economical interpretation.

In the remaining part of this section, the notation used throughout the entire paper is stated, and the current state of the literature about linear and sublinear prices is briefly summarized. Although in different notation, everything exposed here can be found, for instance, in Dothan (1990) , Pliska (1997) , and Björk (1999) for the linear setting, and in Jouini & Kallal (1995) and Koehl & Pham (2000) for the sublinear case. Some examples, in a simple discrete setting, are also given, in order to allow the reader for familiarising with the phenomena under study. Remarkably, we emphasise that, as soon as the price functional is no longer linear, market efficiency is no longer guaranteed by absence of arbitrages only, and that another class of inefficiencies, namely the convenient super-hedgings (roughly speaking, the opportunity to get a better pay-off at a lower price), have to be taken into account.

Section 2 is dedicated to introducing and examining the convex case. After observing that convex functions naturally pop out when pricing by super-hedging by means of assets whose unit price is increasing, we give a generalisation of the Fundamental Theorem of Asset Pricing, and give an interpretation of the representation in terms of market efficiency. It turns out that the market is fully efficient, i.e., that no convenient super-hedging is possible, if all of the “shadow markets” are efficient, whereas absence of arbitrage is guaranteed by a local, less restrictive property.

Star-shaped prices are analysed in Section 3. We show that such functionals are the result of pricing by super- hedging by means of assets whose average price is increasing, and show that such a requirement is actually a proper generalisation of the previous, convex case. We also introduce a new pricing technique, which we may call “super-hedging by chunks” and that mathematically corresponds to the inf-convolution of the price functionals of the “shadow markets”. We show that convexity and star-shape are in some sense “stable” under super-hedging, either in the classical sense or in the “by chunks” one, and analyse the representation of star-shaped functionals in terms of market efficiency.

Finally, Section 4 is dedicated to summarising and comparing the main properties and the efficiency conditions of the four analysed market types and Section 5 features some concluding remarks.

Although sublinear prices can indeed capture several features of prices in the “real world”, they still feature unit prices which do not depend on the traded amount. Who trades on actual markets, instead, knows well that unit prices tend to increase with respect to the amount bought, and to decrease with respect to the amount sold. Suppose, for instance, that we are set to buy 1000 units of some asset. Having a look at the offer prices, we see that someone is selling up to 100 units at 3?each, someone else up to 500 units at 3.1?each, someone else up to 600 units at 3.2?each, and so on. This way, we are facing increasing unit prices, and to buy all of the 1000 units we have to pay: it is immediate to realise that, generally speaking, total price needed to buy units of an asset turns out to be a(n increasing and) convex function of (and piecewise affine, in our example, but this is not necessary: the price is a convex function of the traded amount every time that the marginal price is increasing, which is the standard hypothesis of the classical law of supply and demand). We want to show that a natural way to model such a situation is to take into consideration a convex price functional (which is of course a generalisation of the sublinear case, because every sublinear functional is convex as well): in order to do so, let us see how a convex price functional comes out in a very natural way.

Suppose that, in an exchange list under consideration, the assets are included, such that, for every, the supply and demand function is increasing and convex. Of course, the set of all attainable pay-offs is the linear space spanned by the traded assets:. The only reasonable way of assigning a price to every is to use the super-hedging technique seen at the end of the previous section:

It is immediate to show that such functional is increasing⁵; since, the monotonicity of implies its positivity, which, from the financial point of view, means that the “cheapest super-hedging” price functional does not allow neither for arbitrages nor for convenient super-hedgings. It is also possible, although a little technical, to show that is convex, i.e., that for every and every ⁶: the convexity of the single supply and demand functions “propagates” to the entire pricing functional.

Fenchel’s Theorem ensures that a convex functional can be represented as the pointwise maximum of the affine functionals that it dominates, where an affine functional is the translation of a linear functional: is affine if there exist linear and such that. In greater detail:

Proposition 1 (Fenchel’s Theorem). Let be convex. Then the set is non-empty, closed and convex and such that for every.

Since every can be written as with linear and, and since every linear functional can be represented as in (1), the convex functional can be represented as

Note that implies that all of the constants are, and that at least one of them is null,

because.

Example 3. Take into consideration the same two assets of Examples 1 and 2, with pay-off matrix, , and suppose that they are exchanged on the market as follows:

• has unit price 4 for (short) sales or purchases up to 10 units, 4.2 for purchases up to 50 units and 4.4 beyond 50 units;

• has unit price 5 up to 20 units, 5.5 up to 80 units and 6 beyond 80 units.

Such prices split the portfolio space into nine regions, identified by four vertices (see Figure 1).

Let, and consider the portfolio. Of course,; therefore,.Since is defined as the cheapest (super)hedge of W,. On the other hand, the fact that all of the functions are convex w.r.t. implies that. By transitivity, , i.e. , is convex.Note that this implies that the inequalities (3) hold for every, not just for the listed assets.•, which yields the pay-off and costs;•, which yields the pay-off and costs (recall thefirst 20 units of are bought at the cheaper price 5, and only the 60 subsequent units are bought at the higher price 5.5;•, which yields the pay-off and costs;•, which yields the pay-off and costs.Inside each region, the unit prices remain constant (shown in Figure 1 as well), and therefore the price functional is affine: we may write,.In greater detail: there have to be nine vectors and nine (non positive) constants

such that, if with , then. Every vector identifies a discount factor and a risk-neutral probability, and therefore this model

identifies at least nine risk-neutral measures; however, as already pointed out, the risk-neutral measures turn out not to be as important as the properties of the price functional in order to investigate market efficiency.

Note that, if both X and belong to the same region, then: it is then straightforward to realise that, for every, the vector is easily determined by solving the usual linear system. The constants, , are calculated as the amount “saved” by buying the “first” units at a price smaller than:

• In, the effective prices are the lowest ones: therefore, (we could argue the same conclusion from the fact that);

• In, the price of is 5.5, but the first 20 units are bought at the price, thus “saving”

: therefore, (as a double check, consider for instance that the portfolio yields the pay-off and costs, and pre-

cisely yields);

• In, the price of is 6, but the first 20 units are bought at less and the subsequent () 60 at less, for a total “saving” of: therefore, (note that such a saving can also be calculated as the one achieved in the “previous” region, i.e., , plus the additional saving of 0.5 on all of the first 80 units:);

• In, the price of is 4.2, but the first 10 units are bought at less, thus “saving”: therefore,;

• In, both the first 10 units of and the first 20 units of are bought at a lower price: the savings of and add up, and therefore;

• In, the savings of and add up, and therefore;

• In, the first 10 units of cost less than the “full” price, and the subsequent () 40 cost less: the total saving is: therefore,;

• In, the savings of and add up, and therefore;

• Finally,.

Now, the price of every pay-off can be calculated as. For instance, for we get:

(the maximum price is emphasised). Note that, indeed, X is yielded by the portfolio, and the price of a is.

As already mentioned, the price functional is convex. We want nevertheless to strike out that, generally

speaking, it is neither sub- nor superadditive: for instance, consider again the pay-off. It is possible to check that and, and therefore

. On the other hand, it is also, and therefore. We want to point out that this second inequality

does not correspond to a convenient super-hedging: indeed, it is not possible to buy simultaneously two portfo-

lios yielding the claim because, when doubling the position, the unit prices of the traded assets increase.

It is still possible to show that, if does not take infinite values, the set is compact and

convex. Mathematically speaking, the set is the union of the sub differentials of at all points; note that, if is sublinear, then.

In perfect analogy to what happens for sublinear functionals, is increasing if and only if every is positive. The natural technique of pricing any pay-off Z (either belonging to or not) by super-hedging, as seen in Section 1.3, can still be applied, even in the case when allows for convenient super-hedgings, and it can be shown that the cheapest super-hedging price is a convex functional if the “original” is. Furthermore, the set corresponding to the set identified by turns out to be precisely the set.

Example 4. On consider the two assets:

•, at the unit price of 4.5, which increases at 4.7 for purchases of more than 10 units;

•, traded at 3.4 per unit, which increases at 3.7 for purchases of more than 20 units.

This way,. The prices split the portfolio space into four regions, corresponding to the four “quadrants” identified by the portfolio (see Table 1); note that the portfolio yields the pay-off and costs. The vectors and the constants, calculated as in

Table 1. The four regions of the portfolio space in Example 4.

previous Example 3, are also shown.

There are positive vectors in (such as, for instance); yet, the negative components of and indicate the possibility of a convenient super-hedging. It is quite clear that such possibilities apply to all of the portfolios belonging to the regions and.

Consider, for instance, the portfolio, whose pay-off is and whose price is. It is immediate to check that the pay-off can be super-hedged by means of the portfolio, whose pay-off is and whose price is: a convenient super-hedging is found, and the cheapest super-hedging price functional will be such that (indeed, it can be shown that the equality holds).

Analogously, the portfolio yields the pay-off at the price, but a convenient super-hedging is given by, whose pay-off is and whose price is. Therefore, (and, as before, the equality holds, indeed).

It is possible to prove⁷ that the “adjusted” functional, calculated after exploiting all of the convenient super-hedgings, is calculated as the maximum of six affine functionals as shown in Table 2. Notably enough, the convex set identified by the six vectors exactly amounts to the subset of the positive vectors contained in the “original” set (see Figure 2). Note also that not all of the vectors, , induce risk neutral measures, because some of them have negative components; however, each of the six “vertices” of the “restricted” set can again be seen as the product of a risk neutral measure and a discount factor (for instance, corresponds to the degenerate probability such that and to the discount fac-

Table 2. The four regions of the portfolio space in Example 4 after taking advantage of the convenient super-hedging opportunities.

tor). Nevertheless, we remark that such elements are not as important as the js themselves to investigate market efficiency.

When it comes to positivity, things get a little more complicated: indeed, the fact that contains no positive functionals at all is still sufficient, but no longer necessary, in order to allow for arbitrages. Consider the following (and quite minimal) example.

Example 5. Again on, suppose that the asset is sold at the unit price 0.4 regardless of the amount, and that is sold at unit price up to 5 units, and 0.5 for more than 5 units. The portfolio space is trivially split into two regions (and in each of them, since, it is trivially):

It is clear that arbitrages are possible, because buying with has a negative price for every

. Nevertheless, the set contains the positive vector.

It is still possible to define the cheapest super-hedging price functional: it turns out that it simply amounts

to replace, in the region, and with and. As a consequence, it is no longer: indeed, , which precisely indicates the possibility to get a free gain of 3 without risk. It is

also worth pointing out that such an arbitrage is just “local” in the spirit of Castagnoli et al. (2011) , in the sense that there is an upper bound to the gains that can be obtained by means of arbitrages.

The point is that, as already mentioned, an arbitrage is nothing but a convenient super-hedging of the null vector. In the sublinear case, positive homogeneity ensures that such a convenient super-hedging (meaning both its positive pay-off and its negative price) can be multiplied by an arbitrary positive constant and still remain an arbitrage: this way, if arbitrages are possible, the region of the arbitrage portfolios is always unbounded. In the “granular” convex case, instead, positive homogeneity no longer holds, and therefore arbitrages may be confined to a bounded region, as it happens in Example 5.

As a matter of fact, it is possible to show that a linear functional matters in determining whether allows for arbitrages or not only if is relative to a “region” of the portfolio space that contains the null pay-off: we call such a subset of ⁸. In other terms, while is the union of all subdifferentials of, we are here only interested in the subdifferential of at the origin. Briefly, a convex functional is positive if and only if there exists a positive.

For sublinear functionals, it can be proven that the subdifferential at each point is by necessity a subset of the subdifferential at 0, or, in other words that. This, besides the “unbounded” nature of arbitrages in sublinear markets, provides a further argument in favour of the fact that, unlike what happens for convex markets, in sublinear markets absence of arbitrages and absence of convenient super-hedgings are properties of the same set L.

The results of this section can be summarized and formalized in the following

Theorem 1. Let be a linear space of financial assets, and be a convex pricing functional such that. Define and, for every, call and, so that. Let and. Then:

1. L is non-empty, closed and convex;

2. for every,;

3. for every, and is linear; furthermore, there exists such that;

4. and are non-empty, closed and convex, and;

5. is increasing if and only if every is positive;

6. is positive if and only if there exists a positive.

Suppose that is such that the supply and demand function is convex. If, as it is natural to suppose, , then it turns out that

the first inequality comes from the convexity property, because (being) it is; it is furthermore possible to see that the two inequalities are equivalent to each other⁹.

A possible reason why inequalities (3) are sensible in ordinary markets can be seen as follows. Take such that, so that. The first of the two inequalities (3) above is equivalent to

in other words, the supply and demand function of an satisfies (3) if and only if the average unit price of X is increasing with respect to the traded amount. This is why we deem reasonable such a property: indeed, when aiming at purchasing some quantity of something, it is rational to buy it at the lowest possible overall price, which of course coincides with the lowest average unit price.

Suppose, for instance, that three agents sell the same asset X on the market. The first one sells it at 4 per unit, but can only provide up to 30 units. The second one sells it at 5 per unit (for any amount). The third one sells it at 4.5 per unit, but only for a minimum order of 50 units. It is clear that the best price that can be obtained to buy units of X are:

for instance, to buy 50 units of X, the unit price of 4.5 may be obtained, but it is less expensive to buy 30 units from the first agent and 20 from the second, at a total price of instead of. Note that the average price obtained with the “separate” purchase is.

We shall call star-shaped a supply and demand function that satisfies inequalities (3) and, in general, any function, with any real linear space, that does the same. Note the difference with “granular” pricing functionals, which feature an increasing marginal price with respect to the traded amount: of course every convex function is star-shaped as well, but the converse need not be true.

Example 6. The function defined as

is star shaped, because (and) its “average value” is increasing. Nevertheless, f is not convex (and not even continuous).

A geometrical interpretation of the star-shaped property is easily deduced from the monotonicity of average prices. Recall that, given any real linear space, a function is convex if and only if, whenever two points are given “above” the “graph” of f, i.e., such that , then the entire segment adjoining and remains “above” the graph of f (which translates into for every). The property that average prices are increasing for star-shaped functions translates into the fact that whenever is given “above” the “graph” of f, i.e., such that, then the entire segment adjoining and remains “above” the graph of f (which translates into for every). In Figure 3 the typical appearance of the four functions examined in this paper is depicted for functions.

Suppose that, in an exchange list under consideration, the assets are included, such that, for every, the supply and demand function is increasing and star-shaped. Once again, we define on the set of all attainable pay-offs a price functional by super-hedging:

As usual, such a functional immediately turns out to be increasing (and therefore positive, because); moreover, it can be proved that is star shaped as well, i.e., that inequalities (3) hold for every. So to say, the star-shape of the single supply and demand functions “propagates” by super-hedging.

An adaptation of a result by Chateauneuf & Aouani (2008) , still unpublished (see Castagnoli et al., 2009 ), shows that a star-shaped functional can be represented as the pointwise minimum of the convex functions that dominate it. In detail:

Proposition 2. Let be a star-shaped functional. Then the set is closed and convex, and for every.

It is indeed possible to prove that only the convex functionals such that can be taken into consideration, i.e., that if, then (is closed and convex as well and) as well. By applying Proposition 1, we can write each as in (2), and therefore represent the star-shaped functional as

To give an economical interpretation of such a representation, think that several agents are available to sell X on the market. Each agent, corresponding to a, assigns a convex price to X (i.e., has an increasing marginal price), and we are free to choose the agent we want to buy the asset X from (i.e., the cheapest one).

It is worth mentioning that the class of star-shaped functionals is closed under pointwise sup and inf, even for an infinite family of functionals; more explicitly, if is any real linear space and is a family of star-shaped functionals such that, then and.

Example 7. Take into consideration the same two assets of Examples 1, 2, and 3, with pay-off matrix

We suppose that the investor can call her/his demands on three different markets, each run by a (representative) agent with her/his own convex price system: this way, three convex price functionals, and are given. Suppose that the first one is the same of Example 2, with nine portfolio region as follows:

The second agents gives the granular price functional identified by¹⁰

The third agent simply gives a linear price:,.

Let us take into consideration some random variables: the details of the calculations (which amount to see in which region the price of the given pay-offs is maximum) are left to the reader.

• For, it is, , and: therefore, is bought from the second agent and.

• For, it is, , and: therefore, is bought from the first agent and.

• For, it is, and: therefore, is bought from the third agent and.

It is then clear that each of the three price systems has a chance to prove the cheapest one and, therefore, that it is effective in determining as the pointwise minimum of, , and. This can happen even for a

single asset: for instance, the three demand functions for in the three mar-

kets turn out to be:

and. It is straightforward (but very laboured) to see that

note that the marginal price is not increasing (for instance, around, it decreases from 15.925 to

15.075), whereas the average one is.

It is still true that the star-shaped price functional does not allow for convenient super-hedgings if and only if it is increasing.

Notably, if this is the case, instead of the set of Proposition 2 or the set of (4), the subsets and can be taken into consideration in order to represent. Define indeed, for every, the functional by: the fact that g is convex ensures that such a minimum exists. It is clear that is increasing and that; furthermore, it is possible to prove (by taking into consideration the monotonicity of) that still.¹¹ This way, it is straightforward to realise that and, therefore, that for every.

Since it is trivial that the minimum of a family of increasing functionals remains increasing, the above considerations allow us to conclude that a star-shaped functional does not allow for convenient super-hedgings if, and only if, it can be represented as the pointwise minimum of a family of increasing functionals. Note that this is quite analogous to what happened for convex and, before, for sublinear functionals: allows for no convenient super-hedgings if, and only if, all of the functionals in the representation of are increasing.

In order to deal with arbitrages, one more consideration is needed. Since, in the economical interpretation we gave, we are free to choose, among several agents, the best one to buy the pay-off X we want to detain, it is reasonable to think that we may prefer to buy several different portfolios from the different agents, in such a way that the overall position matches or, better, super-hedges X. Mathematically speaking, we are dealing with the following object:

Definition 1. Let be a real linear space, and a family of real functionals on. The inf-convolution of G is the functional defined by, for every,

This way, if we are given a set of price functionals, each ideally corresponding to a different market (or agent), taking their inf-convolution amounts to buying a finite number of positions, on the markets respectively, in such a way that the overall pay-off super-hedges the desired pay-off X. Note that, as a consequence, the inf-convolution of a family of functionals always turns out to be smaller than their pointwise minimum. (Note also that, if the are all increasing, the condition can be equivalently replaced by.)

The following proposition holds true, with evident consequences from the financial point of view.

Proposition 3. Let be a real linear space and be a family of real functionals on such that every is increasing and such that. Let be the inf- convolution of G: then F is finite-valued, and

1. if every is convex, then F is convex;

2. if every is star-shaped, then F is star-shaped.

The fact is that, even if every is such that, it is not necessary that. Economically speaking, even if the “original” markets do not allow either for arbitrages nor for convenient super- hedgings, a “cross-market” arbitrage may still be possible.

Example 8. Consider again the three markets of Example 7: we saw that can be bought from the second agent at the price 276. It is immediate to check that for it is, , and: therefore, can be sold to the third agent for a gain of 306, thus making a “cross-market” arbitrage of 30.

As a consequence, the inf-convolution of will be star-shaped and increasing, because of

Proposition 3, but such that. (The inequality is indeed strict: for instance, , and).

Although the technical details for a complete proof become too complex to be reported here, the key feature can be guessed from the following (and last) example.

Example 9. The function

is increasing, star-shaped and such that. Yet, it is clear that and, so that. Note that the subdifferential of f at 0 is the empty set (no straight line fits below the graph of f).

The point that can be proven is that, if a star-shaped price functional is the result of a “best price” over several markets, “cross-market” arbitrages are possible if and only if there are assets whose price around 0 behaves like the function of Example 9. Therefore, the same condition seen for convex functionals applies: a star-shaped price functional does not allow for arbitrages, not even cross-market ones, if and only if the subdifferential of at 0 contains at least a positive functional. If, of course such a condition is not satisfied, which immediately signals the possibility of arbitrages.

Note that cross-market convenient super-hedgings may still be possible, as Example 8 itself makes clear¹²: as a consequence of Proposition 3, this is of course bound to happen every time that the price function is (star- shaped but) not convex.

We can summarise the results of this section in the following:

Theorem 2. Let be a linear space of financial assets, and be a star-shaped pricing functional such that. Define and. Call and. Finally, call F the inf-convolution of G (see Definition 1) and define, as in Theorem 1,. Then:

1. G and are non-empty, closed and convex;

2. for every,;

3. is increasing if and only if for every.

4. is positive if and only if there exists a positive.

Remark 3. In this section, we have seen two ways to build the overall supply and demand function of a given asset in the case when several “markets” are available.

1. In the first case, seen at the beginning of the section, we supposed that the agent simply chooses the “best market”: implicitly, we imposed that every single trade can only happen with a single agent. In such a case, the market is chosen where the total price, or (which is the same) the average price, is the minimum one.

2. In the second case, by using the “inf-convolution” technique, we supposed that the agent is free to split the desired position into “chunks” and buy the various “chunks” separately on the various markets. This way, for every single additional unit, the agent chooses the market where the marginal price is the minimum one.

Depending on the type of price functionals on the “original” market, we saw that:

1. In the first case, when all of the markets feature either a convex or a star-shaped price functional, the overall price functional turns out to be star-shaped: we pointed indeed out that an increasing average price is obtained;

2. In the second case, the overall price functionals inherit the convexity or the star-shape of the original functions (namely, it is convex if all of the original pricing functionals are, and star-shaped likewise): the minimum marginal price is chosen, and yet it need not be increasing unless it is in the original markets already.

In some sense, we have found that convexity and star-shape are quite “stable” properties in financial markets.

Of course, several other “rules” can be imagined: for instance, some markets may be only available for purchases, or for sales, or only some particular amounts can be bought (not just with a minimum or a maximum amount, as seen in the beginning of this section, but for instance for multiples of some “size” only), in such a way that the offer price function turns out not even to be star-shaped. Anyway, examining such cases goes beyond the scopes of the present paper.

Four type of price systems, each a generalisation of the previous one, have been examined in this paper. From the last to the first, they are:

1. star-shaped prices:

2. convex (or granular) prices, obtained when G is a singleton:

3. sublinear prices, obtained when (G is a singleton and) (and therefore) for every:

4. linear prices, obtained when (G is a singleton, for every, and furthermore) L is a singleton:

The mentioned fact that both the pointwise minimum and the inf-convolution of a family of star-shaped functionals still are star-shaped seems to suggest that no further generalisation of this type should be fruitful.

For each of the above types, the conditions for absence of arbitrages and of convenient super-hedgings can be examined by taking into consideration the behaviour of the price functionals on the indicator functions with (where, as usual, if and =0 otherwise), which are all positive.

1. Linear prices:. The price of is the value: it is then immediate that such a price is positive for every A (i.e., that no arbitrages and no convenient super-hedgings are possible) if and only if. Intuitively, indeed, if is not positive, then there exists an ¹³ such that, so that makes an arbitrage.

2. Sublinear prices:, with. Recall that the positivity of is equivalent to the positivity of its ask prices, and its (increasing) monotonicity is equivalent to the positivity of its bid prices. The ask price of is and its bid price is: therefore, the ask price is positive as soon as one of the prices is, i.e., if there exists a positive, and the bid price is positive only if all of the prices are, i.e., if all are positive. Intuitively, if there is a non-positive, then as above there exists an such that: this means that, so that makes a convenient super-hedging of the null pay-off. Less intuitively, and indeed harder to prove, if all are not positive, then there exists an such that for every: in this case, , and makes an arbitrage.

3. Convex prices:, with. Call the set of all such that for some. The ask price of is positive as soon as is: since, this implies that is increasing, i.e., positive, that is to say, that there exists a positive in.

As for arbitrages, since is not positively homogeneous, we need to take into consideration every for: in order to make an arbitrage, has to be negative for all and for some. This happens if for every: since this equality can be easily met when (it is

enough to take “small enough”), indeed only the such that can be taken into consideration, and these fs (which amount to the set of the linear functionals of L) are precisely the ones that concur in determining the value of close to 0. Therefore, the above arguments for sublinear functionals can be repeated in a “local” sense (i.e., for a with “small enough”), to conclude that does not allow for arbitrages if there exists a positive.

Intuitively, if there is a non-positive, then as above there exists an such that: since there is some such that, it is possible to get also if k is “big enough”, and this means that (convenient super-hedging). On the other hand, if all are not positive, then there exists an such that for every: in this case, it is possible to choose k “small enough” so as to obtain, and makes an arbitrage.

4. Star-shaped prices:. The ask price of is positive only if all of the are positive for every convex: in other words, does not allow for arbitrages only if

all do not allow for them. As for convenient super-hedgings, it is now quite complex to summarise the properties that have to be imposed on the convex functionals in order to get an increasing functional

(for instance, it is possible to prove that, for every,: an increasing

functional, i.e., the identity, is obtained as the pointwise minimum of a family of functions, none of which is increasing). We have nevertheless realized that, if is increasing, then only the increasing functionals may be taken into consideration and, on the other hand, that the pointwise minimum of increasing functions is increasing: therefore, does not allow for arbitrages if it can be written as the minimum of a family of increasing convex functionals¹⁴. Intuitively, it is clear that an arbitrage is possible as soon as even a single market allows for it, but now, for convenient super-hedgings, things become quite different. Indeed, recall that a convenient super-hedging is a pair such that but: in order to “deactivate” such a situation, i.e., in order to obtain, it may be enough that on a single market Y is sold at a cheaper price than.

It is clear that moving from linear to star-shaped price systems yields price systems whose properties are closer and closer to what happens in “real” financial markets. Yet, because of the fact that such systems are each the generalisation of the previous one, the “efficiency” conditions for each family of functionals propagates on the further generalisations in a reasonable way. All in all, therefore, these four price systems altogether make quite a versatile toolbox for building financial models, in the sense that, when a financial model is needed, it is easy to “fine tune” the level of precision to suit the needs of the considered problem.

which are all convex and increasing.

A classical problem in Mathematical Finance is to study the prices of a suitable set of risky financial assets, modeled as random variables on some state sets, by means of suitable functionals defined on this set of random variables. The properties of the price functional reflect the assumptions on the market. In this paper, we analysed four types of price functionals. We first summarized the properties of the widely known linear functionals, obtained when the market was supposed to be perfect, and of sublinear functionals, which took into account the proportional frictions. Then, we introduced two more classes of functionals which accommodate a wider set of market frictions: granular (convex) functionals, obtained when the unit prices of traded assets were increasing w.r.t. the traded amount, and star-shaped functionals, obtained when the average unit prices of traded assets were increasing w.r.t. the traded amount. We explored some characterizations of such functionals, together with their relationships with arbitrages and market inefficiencies, and performed a final analysis on their effectiveness in allowing for versatile modelling.

ErioCastagnoli,Marzia DeDonno,GinoFavero,PaolaModesti, (2015) Granular and Star-Shaped Price Systems. Journal of Financial Risk Management,04,227-249. doi: 10.4236/jfrm.2015.43018