Fundamental Theorem of Asset Pricing under fixed and proportional transaction costs

TL;DR

This paper extends the Fundamental Theorem of Asset Pricing to models with both fixed and proportional transaction costs, establishing conditions for no arbitrage involving a family of probability measures and a martingale process within bid-ask spreads.

Contribution

It generalizes the classical theorem to include combined fixed and proportional transaction costs, providing a new theoretical framework.

Findings

Equivalence between no arbitrage and existence of suitable probability measures and martingale process.

Extension of Harrison and Pliska's classical theorem to more complex transaction cost models.

Theoretical foundation for pricing and hedging in markets with mixed transaction costs.

Abstract

We show that the lack of arbitrage in a model with both fixed and proportional transaction costs is equivalent to the existence of a family of absolutely continuous single-step probability measures, together with an adapted process with values between the bid-ask spreads that satisfies the martingale property with respect to each of the measures. This extends Harrison and Pliska's classical Fundamental Theorem of Asset Pricing to the case of combined fixed and proportional transaction costs.

Equations

$$Q:=\{Q_t^{\lambda }|t=0,\dots ,T-1,\lambda \in {\Lambda }_t\}$$

$$\lambda \cap {\mathcal{F}}_{t+1}:=\left\{\lambda \cap A|A\in {\mathcal{F}}_{t+1}\right\}$$

$$\mathbb{Q}(\lambda ):=\prod _{t=0}^{T-1}{Q}_t^{{\lambda }_t}({\lambda }_{t+1})$$

$$S_t^{\lambda }=\sum _{\mu \in \mathrm{succ}(\lambda )}{Q}_t^{\lambda }(\mu )S_{t+1}^{\mu }$$

$$x+B_t^{\lambda }{y}^{+}-A_t^{\lambda }{y}^{-}-C_t^{\lambda }\ge 0$$

$$x,y\ge 0.$$

$$(X_t^{\lambda }-X_{t+1}^{\lambda },Y_t^{\lambda }-Y_{t+1}^{\lambda })\in {\mathcal{G}}_t^{\lambda }$$

$$L_t^{\lambda }(x,y):=x+(B_t^{\lambda }{y}^{+}-A_t^{\lambda }{y}^{-}-C_t^{\lambda })1_{y\notin [0,C_t^{\lambda }/B_t^{\lambda }]}$$

$$U_T^{\lambda }:=A_T^{\lambda },V_T^{\lambda }:=B_T^{\lambda }$$

$$U_{t-1}^{\lambda }:=\underset{\mu \in \mathrm{succ}(\lambda )}{\max }(U_t^{\mu }\wedge A_t^{\mu }),V_{t-1}^{\lambda }:=\underset{\mu \in \mathrm{succ}(\lambda )}{\min }(V_t^{\mu }\vee B_t^{\mu })$$

$$U_t\ge A_{\sigma },V_t\le B_{\tau }.$$

$$\sigma :=\eta 1_{\left\{A_t>U_t\right\}}+t{1}_{\left\{A_t\le U_t\right\}}.$$

$$U_{t-1}\ge U_t\wedge A_t=U_t{1}_{\left\{A_t>U_t\right\}}+A_t{1}_{\left\{A_t\le U_t\right\}}\ge A_{\eta }{1}_{\left\{A_t>U_t\right\}}+A_t{1}_{\left\{A_t\le U_t\right\}}=A_{\sigma },$$

$$V_t\vee B_t\le U_t\wedge A_t$$

$$(X_{s},Y_{s}):=\left\{\begin{array}[c]{ll}(0,0)&\text{for }s=0,\ldots,t,\\ \mathbf{1}_{\lambda}(-A_{t}z-C_{t},z)&\text{for }s=t+1,\ldots,\tau,\\ \mathbf{1}_{\lambda}(-A_{t}z+B_{\tau}z-C_{t}-C_{\tau},0)&\text{for }s=\tau+1,\ldots,T+1,\end{array}\right.$$

$$(X_{s},Y_{s}):=\left\{\begin{array}[c]{ll}(0,0)&\text{for }s=0,\ldots,t,\\ \mathbf{1}_{\lambda}(B_{t}z-C_{t},-z)&\text{for }s=t+1,\ldots,\sigma,\\ \mathbf{1}_{\lambda}(B_{t}z-A_{\sigma}z-C_{t}-C_{\sigma},0)&\text{for }s=\sigma+1,\ldots,T+1,\end{array}\right.$$

$$S_0\in [V_0\vee B_0,U_0\wedge A_0].$$

$$V_{t+1}^{\nu }\vee B_{t+1}^{\nu }=V_t^{\lambda }\le S_t^{\lambda }\le U_t^{\lambda }=U_{t+1}^{\mu }\wedge A_{t+1}^{\mu }$$

$$S_{t+1}^{\mu }:=U_{t+1}^{\mu }\wedge A_{t+1}^{\mu },S_{t+1}^{\nu }:=V_{t+1}^{\nu }\vee B_{t+1}^{\nu }$$

$$S_{t+1}^{\eta }\in [V_{t+1}^{\eta }\vee B_{t+1}^{\eta },U_{t+1}^{\eta }\wedge A_{t+1}^{\eta }].$$

$$\underset{\mu \in \mathrm{succ}(\lambda )}{\min }{S}_{t+1}^{\mu }\le S_t^{\lambda }\le \underset{\mu \in \mathrm{succ}(\lambda )}{\max }{S}_{t+1}^{\mu },$$

$$S_{t+1}^{\mu }:=S_0$$

$$S_{t+1}^{\eta }\in [V_{t+1}^{\eta }\vee B_{t+1}^{\eta },U_{t+1}^{\eta }\wedge A_{t+1}^{\eta }].$$

$$X_t^{\lambda }+S_{t-1}^{\lambda }{Y}_t^{\lambda }\ge \underset{\mu \in \mathrm{succ}(\lambda )}{\min }(X_t^{\lambda }+S_t^{\mu }{Y}_t^{\lambda })$$

$$S_t^{\lambda }<\underset{\mu \in \mathrm{succ}(\lambda )}{\min }{S}_{t+1}^{\mu }\text{or}{S}_t^{\lambda }>\underset{\mu \in \mathrm{succ}(\lambda )}{\max }{S}_{t+1}^{\mu },$$

$$X_t^{\lambda }-X_{t+1}^{\lambda }+S_t^{\lambda }(Y_t^{\lambda }-Y_{t+1}^{\lambda })$$

$$\ge X_t^{\lambda }-X_{t+1}^{\lambda }+B_t^{\lambda }(Y_t^{\lambda }-Y_{t+1}^{\lambda }{)}^{+}-A_t^{\lambda }(Y_t^{\lambda }-Y_{t+1}^{\lambda }{)}^{-}$$

$$\ge C_t^{\lambda }>0$$

$$X_t^{\lambda }\ge X_{t+1}^{\lambda },Y_t^{\lambda }\ge Y_{t+1}^{\lambda }.$$

$$X_t+S_t{Y}_t\ge X_{t+1}+S_t{Y}_{t+1}$$

$$X_{t+1}+S_t{Y}_{t+1}\ge 0$$

Full text

1 Introduction

The Fundamental Theorem of Asset Pricing, linking the lack of arbitrage with the existence of a risk neutral probability measure, has been studied for a diverse range of models of the financial market. The first to establish the result for discrete time models with finite state space were Harrison and Pliska [HarPli1981]. Dalang, Morton and Willinger [DalMorWil1990] extended the theorem to the case of infinite state space, and Delbaen and Schachermayer [DelSch1994], [DelSch1998] to continuous-time models.

The above classical results apply to frictionless models. Harrison and Pliska’s result was extended to models with friction in the form of proportional transaction costs (represented as bid-ask spreads) by Jouini and Kallal [JouKal1995], Kabanov and Stricker [KabStr2001] and Ortu [Ort2001]. Furthermore, Roux [Rou2011] included interest rate spreads in addition to proportional transaction costs. Similarly, the result by Dalang et al. involving an infinite state space was extended to models with proportional transaction costs by Zhang and Deng [ZanDen2002], Kabanov, Rásonyi and Stricker [KabRasStr2002], and Schachermayer [Sch2004].

Under fixed transaction costs, to our best knowledge, the equivalence between the absence of arbitrage and the existence of risk neutral measures has so far been studied in just one paper, by Jouini, Kallal and Napp [JouKalNap2001].

The long-standing question of extending the Fundamental Theorem of Asset Pricing to cover the situation when both fixed and proportional transaction costs apply simultaneously is addressed in the present paper. We use the term ‘combined costs’ as shorthand when referring to this case. Such costs are ubiquitous in the markets, hence it is important to be able to characterise the lack of arbitrage in their presence. In Theorem 4.3 we show that the absence of arbitrage in a market with combined costs is equivalent to the existence of a family of single-step probability measures absolutely continuous with respect to (but not necessarily equivalent to) the physical probability, along with a martingale with respect to such a family of measures (as defined in Section 2) and taking values between the bid and ask prices. In doing so, we extend the classical result of Harrison and Pliska [HarPli1981] for a finite state space to the case of combined costs. Later on, in Corollary 5.2 we provide another equivalent condition for the lack of combined-cost arbitrage, namely the existence of an embedded arbitrage-free model with fixed costs.

The technical difficulties inherent in the problem solved here are due to a combination of two factors. On the one hand, proportional costs mean that the absence of arbitrage in the full multi-step model is not equivalent to the condition that every single-step submodel should be arbitrage free (even though such an equivalence holds in frictionless models as well as under fixed costs), preventing an argument by reduction to a single step. On the other hand, fixed costs imply that the set of solvent portfolios lacks convexity. While these difficulties have been tackled separately in the context of proportional costs and, respectively, fixed costs only, they require fresh ideas to handle their compounded effect. This is achieved in the proof of Theorem 4.3.

Finally, we mention the recent paper by Lepinette and Tran [LepTra2017], in which arbitrage under market friction involving lack of convexity (and including the case of simultaneous fixed and proportional costs) has been considered. In that paper the absence of asymptotic arbitrage is characterised by the existence of a so-called equivalent separating probability measure. However, no link is made with risk neutral probabilities, by contrast to the present paper. In Example 6.1 we show that the non-existence of an equivalent separating probability measure does not, in fact, mean that a combined-cost arbitrage opportunity must be present.

2 Notation and preliminaries

Let $T$ be a positive integer and let $\left(\Omega,\Sigma,\mathbb{P}\right)$ be a finite probability space equipped with a filtration $\mathcal{F}=\left(\mathcal{F}_{t}\right)_{t=0}^{T}$. We assume (without loss of generality) that the physical measure $\mathbb{P}$ satisfies the condition $\mathbb{P}(A)>0$ for each non-empty $A\in\mathcal{F}_{T}$, and the sigma-field $\mathcal{F}_{0}$ has a single atom, that is, $\mathcal{F}_{0}=\{\emptyset,\Omega\}$. We refer to the atoms of $\mathcal{F}_{t}$ as the nodes at time $t=0,\ldots,T$, and write $\Lambda_{t}$ for the set of nodes at time $t=0,\ldots,T$. For any non-terminal node $\lambda\in\Lambda_{t}$, where $t=0,\ldots,T-1$, we denote by $\mathrm{succ}(\lambda)$ the set of successor nodes of $\lambda$, that is, nodes $\mu\in\Lambda_{t+1}$ such that $\mu\subset\lambda$.

For each $t=0,\ldots,T$, we can identify any $\mathcal{F}_{t}$-measurable random variable $X$ with a function on $\Lambda_{t}$, and will write $X^{\lambda}$ for the value of $X$ at a node $\lambda\in\Lambda_{t}$.

We shall say that

[TABLE]

is a family of absolutely continuous single-step probability measures whenever $Q_{t}^{\lambda}$ is a probability measure defined on the sigma-field

[TABLE]

for each $t=0,\ldots,T-1$ and $\lambda\in\Lambda_{t}$. Note that absolute continuity of these measures with respect to $\mathbb{P}$ is automatically ensured by the assumption that $\mathbb{P}(A)>0$ for each non-empty $A\in\mathcal{F}_{T}$. Such a family of measures gives rise to a unique probability measure$\leavevmode\nobreak\ \mathbb{Q}$ defined on the sigma-field$\leavevmode\nobreak\ \mathcal{F}_{T}$ by

[TABLE]

for any $\lambda\in\Lambda_{T}$, where $\lambda_{t}\in\Lambda_{t}$ for $t=0,\ldots,T$ is the unique sequence of nodes such that $\lambda_{0}\supset\cdots\supset\lambda_{T}=\lambda$. In general, the family $Q$ is a richer object than the corresponding measure $\mathbb{Q}$ in that it carries more information at those nodes $\lambda$ where $\mathbb{Q}(\lambda)=0$.

Furthermore, we shall say that an adapted process $S$ is a martingale with respect to the family of measures $Q$ if

[TABLE]

for each $t=0,\ldots,T-1$ and $\lambda\in\Lambda_{t}$. This condition implies that, in particular, $S$ is a martingale (in the usual sense) under the probability measure $\mathbb{Q}$ related to the family $Q$ by (2.1).

Families of absolutely continuous single-step probability measures and martingales with respect to such families of measures will be used to characterise the absence of arbitrage in a market model with combined (fixed and proportional) transaction costs; see Theorem 4.3.

3 Model with fixed and proportional costs

Let $A$, $B$ and $C$ be $\mathbb{R}$-valued processes adapted to the filtration $\mathcal{F}$ such that $0<B\leq A<\infty$ and $0<C<\infty$. We refer to this collection of processes together with the filtration as a combined-cost model, in which $A,B$ play the respective roles of ask and bid stock prices, with $C$ representing fixed transaction costs.

The notions of solvency and self-financing can be formalised as follows in the combined-cost model.

Definition 3.1

1. $1)$

We say that a portfolio $(x,y)\in\mathbb{R}^{2}$ of cash and stock is combined-cost solvent at time $t=0,\ldots,T$ and node $\lambda\in\Lambda_{t}$ when liquidating the stock position leaves a non-negative cash amount

[TABLE]

after the fixed transaction cost $C_{t}^{\lambda}$ is met, or when both the cash and stock positions are non-negative to begin with, that is,

[TABLE]

We denote by $\mathcal{G}_{t}^{\lambda}$ the set of such portfolios $(x,y)$. 2. $2)$

We define a combined-cost self-financing strategy as an $\mathbb{R}^{2}$-valued $\mathcal{F}$-predictable process $(X,Y)=\{(X_{t},Y_{t})\}_{t=0}^{T+1}$ such that

[TABLE]

for each $t=0,\ldots,T$ and $\lambda\in\Lambda_{t}$.

Remark 3.2

We can also consider the combined-cost liquidation value

[TABLE]

of a portfolio $(x,y)\in\mathbb{R}^{2}$ at time $t=0,\ldots,T$ and node $\lambda\in\Lambda_{t}$. Observe that $(x,y)\in\mathcal{G}_{t}^{\lambda}$ is equivalent to $L_{t}^{\lambda}(x,y)\geq 0$. Figure 1 shows a typical set $\mathcal{G}_{t}^{\lambda}$ of combined-cost solvent portfolios.

4 Fundamental Theorem of Asset Pricing under fixed and proportional

costs

Definition 4.1

We say that a combined-cost self-financing strategy $(X,Y)$ is a combined-cost arbitrage opportunity whenever the following conditions hold:

1. $1)$

$(X_{0},Y_{0})=(0,0)$, 2. $2)$

$X_{T+1}\geq 0$ and $Y_{T+1}\geq 0$, 3. $3)$

$X_{T+1}^{\lambda}>0$ for some $\lambda\in\Lambda_{T}$.

Remark 4.2

The absence of combined-cost arbitrage can be described in terms of the liquidation value introduced in Remark 3.2. Namely, there is no combined-cost arbitrage opportunity if and only if $L_{T}(X_{T},Y_{T})=0$ for every combined-cost self-financing strategy $(X,Y)$ such that $(X_{0},Y_{0})=(0,0)$ and $L_{T}(X_{T},Y_{T})\geq 0$. This is a direct extension of the classical no-arbitrage (NA) condition as in [KabStr2001], [Sch2004] and others.

The Fundamental Theorem of Asset Pricing extends to the case of a combined-cost model as follows.

Theorem 4.3

The following conditions are equivalent:

1. $1)$

There is no combined-cost arbitrage opportunity in the model with ask and bid prices $A,B$ and fixed costs $C$; 2. $2)$

There exist an adapted process $S$ and a family of absolutely continuous single-step probability measures $Q$ such that $S$ is a martingale with respect to the family $Q$ and $B\leq S\leq A$.

Proof. To prove the implication $1)\Rightarrow 2)$, assume that there is no combined-cost arbitrage opportunity. We begin by constructing two adapted processes $U$ and $V$ by backward induction:

[TABLE]

for each $\lambda\in\Lambda_{T}$, and

[TABLE]

for each $t=1,\ldots,T$ and $\lambda\in\Lambda_{t-1}$.

Having constructed the processes $U$ and $V$, we claim that for each $t=0,\ldots,T-1$ there exist stopping times $\sigma,\tau>t$ such that

[TABLE]

We prove the existence of $\sigma$ by backward induction. For $\tau$ the argument is similar and will be omitted for brevity. For $t=T-1$ we get $U_{T-1}\geq A_{\sigma}$ by putting $\sigma:=T$. Now suppose that for some $t=1,\ldots,T-1$ we have already established that there is a stopping time $\eta>t$ such that $U_{t}\geq A_{\eta}$. Let us put

[TABLE]

It follows that

[TABLE]

completing the proof of the claim.

Next we show that

[TABLE]

for each $t=0,...,T$. Suppose that this were not so, and take the largest $t=0,...,T$ such that (4.1) is violated. Since $U_{T}=A_{T}$ and $V_{T}=B_{T}$, it follows that $t<T$. It also follows that $V_{t+1}\vee B_{t+1}\leq U_{t+1}\wedge A_{t+1}$, which implies that $V_{t}\leq U_{t}$. Moreover, we know that $B_{t}\leq A_{t}$. Hence, for (4.1) to be violated, at least one of the following two inequalities would have to hold at some node $\lambda\in\Lambda_{t}$:

• •

$V_{t}^{\lambda}>A_{t}^{\lambda}$. We know that there is a stopping time $\tau>t$ such that $B_{\tau}\geq V_{t}$, so $B_{\tau}>A_{t}$ on $\lambda$. In this case the strategy to buy a large enough position in stock for $A_{t}^{\lambda}$ at time $t$ and node $\lambda$, and to sell it for $B_{\tau}$ at time $\tau$ for any scenario belonging to $\lambda$ (and otherwise to do nothing) would be a combined-cost arbitrage opportunity. To be precise, such a strategy $(X,Y)$ could be defined as

[TABLE]

for a large enough $z>0$ so that $\left(-A_{t}+B_{\tau}\right)z>C_{t}+C_{\tau}$ on $\lambda$.

• •

$U_{t}^{\lambda}<B_{t}^{\lambda}$. We know that there is a stopping time $\sigma>t$ such that $A_{\sigma}\leq U_{t}$, so $A_{\sigma}<B_{t}$ on $\lambda$. The strategy $(X,Y)$ defined as

[TABLE]

would be a combined-cost arbitrage opportunity when $z>0$ is large enough so that $\left(B_{t}-A_{\sigma}\right)z>C_{t}+C_{\sigma}$ on $\lambda$.

This contradicts the assumption that there is no combined-cost arbitrage opportunity. Claim (4.1) has therefore been proved.

We are ready to construct a process $S$ and a family of single-step probability measures $Q$ by induction. At time $t=0$ we take any value

[TABLE]

Now suppose that an $\mathcal{F}_{t}$-measurable random variable $S_{t}\in[V_{t}\vee B_{t},U_{t}\wedge A_{t}]$ has already been constructed for some $t=0,\ldots,T-1$. For each $\lambda\in\Lambda_{t}$ we have

[TABLE]

for some $\mu,\nu\in\mathrm{succ}(\lambda)$. If $\mu\neq\nu$, we put

[TABLE]

and, for any $\eta\in\mathrm{succ}(\lambda)$ other than $\mu$ or $\nu$, we take as $S_{t+1}^{\eta}$ any value

[TABLE]

This means that

[TABLE]

so there is a probability measure $Q_{t}^{\lambda}$ on the sigma-field $\lambda\cap\mathcal{F}_{t+1}$ such that (2.2) holds. But if $\mu=\nu$, then we put

[TABLE]

and, for any $\eta\in\mathrm{succ}(\lambda)$ other than $\mu$, we take as $S_{t+1}^{\eta}$ any value

[TABLE]

Moreover, we put $Q_{t}^{\lambda}(\mu):=1$ and $Q_{t}^{\lambda}(\eta):=0$ for any $\eta\in\mathrm{succ}(\lambda)$ other than $\mu$, which defines a probability measure $Q_{t}^{\lambda}$ on the sigma-field $\lambda\cap\mathcal{F}_{t+1}$ such that (2.2) holds. This construction produces an adapted process $S$ such that $B\leq S\leq A$, and a family of absolutely continuous single-step probability measures $Q$ such that $S$ is a martingale with respect to the family $Q$. The implication $1)\Rightarrow 2)$ has been proved.

Conversely, to verify that $2)\Rightarrow 1)$, we assume that condition $2)$ holds and, to argue by reductio ad absurdum, suppose that there is a combined-cost arbitrage opportunity $(X,Y)$. Condition $2)$ implies that

[TABLE]

for each $t=1,\ldots,T$ and $\lambda\in\Lambda_{t-1}$. Indeed, if this inequality failed for some $t=1,\ldots,T$ and $\lambda\in\Lambda_{t-1}$, then we would have

[TABLE]

depending on whether $Y_{t}^{\lambda}>0$ or $Y_{t}^{\lambda}<0$. In either case it would mean that there is no probability measure $Q_{t}^{\lambda}$ on the sigma-field $\lambda\cap\mathcal{F}_{t+1}$ such that (2.2) holds, violating condition $2)$ of the theorem. Next, since $(X,Y)$ is a combined-cost self-financing strategy, it follows that, for each $t=0,\ldots,T$ and $\lambda\in\Lambda_{t}$,

[TABLE]

or

[TABLE]

Hence

[TABLE]

for each $t=0,\ldots,T$. We can show by backward induction that

[TABLE]

for each $t=0,\ldots,T$. Clearly, (4.6) holds for $t=T$, given that $X_{T+1}\geq 0$ and $Y_{T+1}\geq 0$. Now suppose that (4.6) holds for some $t=1,\ldots,T$. Take any $\lambda\in\Lambda_{t-1}$. Then

[TABLE]

where the first inequality holds by (4.2), the second by (4.5) and the last one by the induction hypothesis, completing the backward induction argument.

To proceed further, let us put

[TABLE]

Since $(X,Y)$ is a combined-cost arbitrage opportunity, we know that $X_{0}=Y_{0}=0$ and $X_{T+1}^{\lambda}>0$ for some $\lambda\in\Lambda_{T}$. If $t=T+1$, it would mean that $0=X_{0}\geq X_{T+1}^{\lambda}>0$, a contradiction. On the other hand, if $t\leq T$, then there would be a $\lambda\in\Lambda_{t}$ such that (4.4) fails, so (4.3) would have to hold, implying that

[TABLE]

where the last inequality follows from (4.6). However, that too is impossible as $X_{t}\leq X_{0}=0$ and $Y_{t}\leq Y_{0}=0$. This contradiction completes the proof.

5 Fixed costs

A fixed-cost model involves two processes $S$ and $C$ adapted to the filtration $\mathcal{F}$, where $0<S<\infty$ represents the stock prices and $0<C<\infty$ the fixed transaction costs. This is a special case of the combined-cost model when the ask and bid prices coincide. Hence, fixed-cost solvent portfolios, fixed-cost self-financing strategies and fixed-cost arbitrage opportunities are covered by Definitions 3.1 and 4.1 with $A:=B:=S$. In this case Theorem 4.3 reduces to the following result.

Corollary 5.1

The following conditions are equivalent:

1. $1)$

There is no fixed-cost arbitrage opportunity in the model with stock prices $S$ and fixed costs $C$; 2. $2)$

There exists a family of absolutely continuous single-step probability measures $Q$ such that $S$ is a martingale with respect to $Q$.

This version of the Fundamental Theorem of Asset Pricing under fixed costs is similar to that obtained by Jouini, Kallal and Napp [JouKalNap2001]. However, our method of proof (the proof of Theorem 4.3) is different. Moreover, the equivalent condition for the lack of fixed-cost arbitrage is expressed in terms of single-step measures only, whereas that in [JouKalNap2001] relies on a larger family of measures.

As a consequence of Theorem 4.3, together with Corollary 5.1, we also obtain an alternative characterisation of the lack of combined-cost arbitrage in terms of an embedded arbitrage-free fixed-cost model. It resembles earlier results for proportional transaction costs, which involve embedding an arbitrage-free frictionless model; for example, see Roux [Rou2011].

Corollary 5.2

The following conditions are equivalent:

1. $1)$

There is no combined-cost arbitrage opportunity in the model with ask and bid prices $A,B$ and fixed costs $C$; 2. $2)$

There exists a process $S$ adapted to the filtration $\mathcal{F}$ such that $B\leq S\leq A$ and the model with stock prices $S$ and fixed costs$\leavevmode\nobreak\ C$* admits no fixed-cost arbitrage opportunity.*

6 Equivalent separating probability measures

In the context of the present paper, we also refer to the recent article by Lepinette and Tran [LepTra2017], where the absence of asymptotic arbitrage in a class of non-convex models (which include models with fixed and proportional costs) is characterised by the existence of an equivalent separating probability measure (ESPM). By definition, an ESPM is a probability measure $\mathbb{Q}$ equivalent to $\mathbb{P}$ such that

[TABLE]

for all self-financing strategies $(X,Y)$ starting with initial endowment $(X_{0},Y_{0})=(0,0)$, where $L_{T}(X_{T+1},Y_{T+1})$ is the liquidation value (see Remark 3.2) of the terminal portfolio $(X_{T+1},Y_{T+1})$. The following example shows that it is possible for a model to be free of combined-cost (or fixed-cost) arbitrage opportunities though there is no ESPM.

We conclude that ESPM’s are unsuitable to characterise the lack of combined-cost arbitrage. Indeed, the absence of combined-cost arbitrage opportunities is consistent with the no-arbitrage (NA) condition in Definition 5.1 of [LepTra2017]. Until now, no suitable analogue of risk neutral probabilities has been put forward to characterise this kind of NA condition. This is resolved by our Theorem 4.3, according to which families of absolutely continuous single-step martingale measures can play this role.

Example 6.1

Consider the single-step model with stock prices $S=A=B$ and fixed costs $C$ such that

[TABLE]

By Theorem 4.3, this model does not admit a combined-costs (or indeed fixed-cost) arbitrage opportunity, as there is a family $Q$ of absolutely continuous single-step measures such that $S$ is a martingale with respect that family; the family consists of just one measure $Q_{0}$ such that $Q_{0}(\mathrm{u})=1$ and $Q_{0}(\mathrm{d})=0$. Observe that

[TABLE]

is a combined-cost self-financing strategy for any $y>1$. Suppose that $\mathbb{Q}$ is an ESPM, so we have $0<\mathbb{Q}(\mathrm{u})<1$. Hence

[TABLE]

when $y>0$. Taking $y>\frac{2}{\mathbb{Q}(\mathrm{u})}$, we get $\mathbb{E}_{\mathbb{Q}}[L_{1}(X_{2},Y_{2})]>0$, contradicting (6.1).

7 Concluding remarks

In this work the classical Fundamental Theorem of Asset Pricing due to Harrison and Pliska [HarPli1981] is extended to discrete market models with simultaneous fixed and proportional transaction costs and finite state space. This also extends later work on the Fundamental Theorem of Asset Pricing under proportional costs such as [JouKal1995], [KabStr2001], [Ort2001], [Rou2011], and under fixed costs [JouKalNap2001].

Developments for models with infinite state space and/or continuous time and/or several assets are likely to follow. Moreover, as the Fundamental Theorem of Asset Pricing has now been established for markets with simultaneous fixed and proportional costs, it will inform research on pricing and hedging derivative securities in this setting.