Problem With Lower And Upper No-Arbitrage Barriers And Inequalities-Financial Mathematics
up vote
2
down vote
favorite
EXERCISE
We consider a financial market of one period with two stocks $(S_0,S_1)$.
We further consider a purchase option with repayment $C = (S_1^{1} - K)^+$
with a value of $K> 0$ and maturing time $T = 1$. Let $underline{π}(C)$,$overline{π}(C)$ the lower and upper no-arbitrage barriers for the value of $C$.
a)Show that the following inequality applies
$$underline{π}(C)geq bigg(S_1^{1} - dfrac{k}{1+r}bigg)^+$$
Please also show that the following equality applies
$$underline{π}(C)= bigg(S_1^{1} - dfrac {k}{1+r}bigg)^+$$
if it is further assumed that:
$$P[S_1^{1}=S_0^{1} cdot(1+r)]> 0$$
Hint : Use the probability measure $$Q[·]: = P [·|S_1^{1} =S_0^{1}cdot (1+r)]$$
b) Also show that the following inequality applies
$$underline {π}(C)leq S_0^{1}$$
and that the following equality applies
$$overline{π}(C)=S_0^{1}$$
assuming that $rm ess sup S_1^{1}=infty $ and that $rm ess inf S_1^{1}=0$.
ATTEMPT
a)We have a financial market with no-arbitrage so we have the form:
$$π(C)=E_Qbigg[dfrac{c}{1+r}bigg]<infty$$ for $Qsubset P$
So,$$underline{π}(C)geq inf_{Q in P}E_Qbigg[dfrac{c}{1+r}bigg]=inf_{Q in P}E_Qbigg[dfrac{S_1^{1}-k}{1+r}bigg]=inf_{Q in P}dfrac{1} {1+r}E_Q[S_1^{1}-k]$$
UPDATE
Ι think that i have to use put-call-parity equation:
$$C_t-P_t=S_t-kB(t,T)$$
with $B(t,T)=dfrac{S_0^{0}} {S_0^{1}}=dfrac{1}{1+r}$
Then we rearrange the equation so that C is on the left hand side and use P_t>0 (using 1/(1+r) for the discount factor).So,we have $$C_t=P_t+S_t-k dfrac{1}{1+r}$$
Can anyone help me from here with a thorough solution so to find the question a)?
I don't know if this is right but i want to know if I am in the right way to solve the problem.
Also,I would like to ask if $min=inf$ in this problem
For question b) i know that i have to take this theorem
and this remark but i don't know even how to start!
I would really appreciate any hints/thorough solution because it's the first problem that I have to solve and I don't have any experience in this type of exercise and generally it's my first try in the field of financial mathematics
Thanks, in advance!
probability inequality stochastic-processes martingales finance
add a comment |
up vote
2
down vote
favorite
EXERCISE
We consider a financial market of one period with two stocks $(S_0,S_1)$.
We further consider a purchase option with repayment $C = (S_1^{1} - K)^+$
with a value of $K> 0$ and maturing time $T = 1$. Let $underline{π}(C)$,$overline{π}(C)$ the lower and upper no-arbitrage barriers for the value of $C$.
a)Show that the following inequality applies
$$underline{π}(C)geq bigg(S_1^{1} - dfrac{k}{1+r}bigg)^+$$
Please also show that the following equality applies
$$underline{π}(C)= bigg(S_1^{1} - dfrac {k}{1+r}bigg)^+$$
if it is further assumed that:
$$P[S_1^{1}=S_0^{1} cdot(1+r)]> 0$$
Hint : Use the probability measure $$Q[·]: = P [·|S_1^{1} =S_0^{1}cdot (1+r)]$$
b) Also show that the following inequality applies
$$underline {π}(C)leq S_0^{1}$$
and that the following equality applies
$$overline{π}(C)=S_0^{1}$$
assuming that $rm ess sup S_1^{1}=infty $ and that $rm ess inf S_1^{1}=0$.
ATTEMPT
a)We have a financial market with no-arbitrage so we have the form:
$$π(C)=E_Qbigg[dfrac{c}{1+r}bigg]<infty$$ for $Qsubset P$
So,$$underline{π}(C)geq inf_{Q in P}E_Qbigg[dfrac{c}{1+r}bigg]=inf_{Q in P}E_Qbigg[dfrac{S_1^{1}-k}{1+r}bigg]=inf_{Q in P}dfrac{1} {1+r}E_Q[S_1^{1}-k]$$
UPDATE
Ι think that i have to use put-call-parity equation:
$$C_t-P_t=S_t-kB(t,T)$$
with $B(t,T)=dfrac{S_0^{0}} {S_0^{1}}=dfrac{1}{1+r}$
Then we rearrange the equation so that C is on the left hand side and use P_t>0 (using 1/(1+r) for the discount factor).So,we have $$C_t=P_t+S_t-k dfrac{1}{1+r}$$
Can anyone help me from here with a thorough solution so to find the question a)?
I don't know if this is right but i want to know if I am in the right way to solve the problem.
Also,I would like to ask if $min=inf$ in this problem
For question b) i know that i have to take this theorem
and this remark but i don't know even how to start!
I would really appreciate any hints/thorough solution because it's the first problem that I have to solve and I don't have any experience in this type of exercise and generally it's my first try in the field of financial mathematics
Thanks, in advance!
probability inequality stochastic-processes martingales finance
add a comment |
up vote
2
down vote
favorite
up vote
2
down vote
favorite
EXERCISE
We consider a financial market of one period with two stocks $(S_0,S_1)$.
We further consider a purchase option with repayment $C = (S_1^{1} - K)^+$
with a value of $K> 0$ and maturing time $T = 1$. Let $underline{π}(C)$,$overline{π}(C)$ the lower and upper no-arbitrage barriers for the value of $C$.
a)Show that the following inequality applies
$$underline{π}(C)geq bigg(S_1^{1} - dfrac{k}{1+r}bigg)^+$$
Please also show that the following equality applies
$$underline{π}(C)= bigg(S_1^{1} - dfrac {k}{1+r}bigg)^+$$
if it is further assumed that:
$$P[S_1^{1}=S_0^{1} cdot(1+r)]> 0$$
Hint : Use the probability measure $$Q[·]: = P [·|S_1^{1} =S_0^{1}cdot (1+r)]$$
b) Also show that the following inequality applies
$$underline {π}(C)leq S_0^{1}$$
and that the following equality applies
$$overline{π}(C)=S_0^{1}$$
assuming that $rm ess sup S_1^{1}=infty $ and that $rm ess inf S_1^{1}=0$.
ATTEMPT
a)We have a financial market with no-arbitrage so we have the form:
$$π(C)=E_Qbigg[dfrac{c}{1+r}bigg]<infty$$ for $Qsubset P$
So,$$underline{π}(C)geq inf_{Q in P}E_Qbigg[dfrac{c}{1+r}bigg]=inf_{Q in P}E_Qbigg[dfrac{S_1^{1}-k}{1+r}bigg]=inf_{Q in P}dfrac{1} {1+r}E_Q[S_1^{1}-k]$$
UPDATE
Ι think that i have to use put-call-parity equation:
$$C_t-P_t=S_t-kB(t,T)$$
with $B(t,T)=dfrac{S_0^{0}} {S_0^{1}}=dfrac{1}{1+r}$
Then we rearrange the equation so that C is on the left hand side and use P_t>0 (using 1/(1+r) for the discount factor).So,we have $$C_t=P_t+S_t-k dfrac{1}{1+r}$$
Can anyone help me from here with a thorough solution so to find the question a)?
I don't know if this is right but i want to know if I am in the right way to solve the problem.
Also,I would like to ask if $min=inf$ in this problem
For question b) i know that i have to take this theorem
and this remark but i don't know even how to start!
I would really appreciate any hints/thorough solution because it's the first problem that I have to solve and I don't have any experience in this type of exercise and generally it's my first try in the field of financial mathematics
Thanks, in advance!
probability inequality stochastic-processes martingales finance
EXERCISE
We consider a financial market of one period with two stocks $(S_0,S_1)$.
We further consider a purchase option with repayment $C = (S_1^{1} - K)^+$
with a value of $K> 0$ and maturing time $T = 1$. Let $underline{π}(C)$,$overline{π}(C)$ the lower and upper no-arbitrage barriers for the value of $C$.
a)Show that the following inequality applies
$$underline{π}(C)geq bigg(S_1^{1} - dfrac{k}{1+r}bigg)^+$$
Please also show that the following equality applies
$$underline{π}(C)= bigg(S_1^{1} - dfrac {k}{1+r}bigg)^+$$
if it is further assumed that:
$$P[S_1^{1}=S_0^{1} cdot(1+r)]> 0$$
Hint : Use the probability measure $$Q[·]: = P [·|S_1^{1} =S_0^{1}cdot (1+r)]$$
b) Also show that the following inequality applies
$$underline {π}(C)leq S_0^{1}$$
and that the following equality applies
$$overline{π}(C)=S_0^{1}$$
assuming that $rm ess sup S_1^{1}=infty $ and that $rm ess inf S_1^{1}=0$.
ATTEMPT
a)We have a financial market with no-arbitrage so we have the form:
$$π(C)=E_Qbigg[dfrac{c}{1+r}bigg]<infty$$ for $Qsubset P$
So,$$underline{π}(C)geq inf_{Q in P}E_Qbigg[dfrac{c}{1+r}bigg]=inf_{Q in P}E_Qbigg[dfrac{S_1^{1}-k}{1+r}bigg]=inf_{Q in P}dfrac{1} {1+r}E_Q[S_1^{1}-k]$$
UPDATE
Ι think that i have to use put-call-parity equation:
$$C_t-P_t=S_t-kB(t,T)$$
with $B(t,T)=dfrac{S_0^{0}} {S_0^{1}}=dfrac{1}{1+r}$
Then we rearrange the equation so that C is on the left hand side and use P_t>0 (using 1/(1+r) for the discount factor).So,we have $$C_t=P_t+S_t-k dfrac{1}{1+r}$$
Can anyone help me from here with a thorough solution so to find the question a)?
I don't know if this is right but i want to know if I am in the right way to solve the problem.
Also,I would like to ask if $min=inf$ in this problem
For question b) i know that i have to take this theorem
and this remark but i don't know even how to start!
I would really appreciate any hints/thorough solution because it's the first problem that I have to solve and I don't have any experience in this type of exercise and generally it's my first try in the field of financial mathematics
Thanks, in advance!
probability inequality stochastic-processes martingales finance
probability inequality stochastic-processes martingales finance
edited Nov 15 at 11:10
asked Nov 14 at 17:31
Magic K. Mamba
305113
305113
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
up vote
1
down vote
accepted
$mathbf{(a)}$ From the extension of the Theorem hinted, the lower no-arbitrage bound is :
$$underline{pi}(C) = inf_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{C}{1+r}bigg]=inf_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg]$$
The Jenses inequality in context of probability theory, says thati f $X$ is a random variable and $varphi$ is a convex function, then it is : $varphibig(mathbb{E}[X]big) leq mathbb{E}[varphi(X)]$. The function $f(x) = (x-K)^+, ; K>0$ is convex, thus :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] geqfrac{1}{1+r}big[mathbb{E}_mathbb{Q}(S_1^1 - K)big]^+ $$
$$= frac{1}{1+r}big[mathbb{E}_mathbb{Q}(S_1^1) - mathbb{E}_mathbb{Q}(K)big]^+= frac{1}{1+r}bigg[S_0^1(1+r) - Kbigg]^+$$
$$implies$$
$$underline{pi}(C) geq bigg(S_0^1 - frac{K}{1+r}bigg)^+$$
where the probability measure $mathbb{Q}[cdot] := mathbb{P}[cdot | S_1^1 = S_0^1(1+r)]$ was used.
Now if $mathbb{P}[S_1^1 = S_0^1(1+r)] >0$, this means that at some point it will be $S_1^1 = S_0^1(1+r)$ and thus :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] = mathbb{E}_mathbb{Q}bigg[frac{(S_0^1(1+r) - K)^+}{1+r}bigg] = mathbb{E}_mathbb{Q}bigg[bigg(S_0^1 - frac{K}{1+r}bigg)^+bigg]$$
which means that the equality will hold, since $S_0^1$ is just a constant $>0$ :
$$underline{pi}(C) = bigg(S_0^1 - frac{K}{1+r}bigg)^+$$
For part $mathbf{(b)}$, again from the extension of the Theorem hinted, we have :
$$overline{pi}(C) = sup_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{C}{1+r}bigg]=sup_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg]$$
But, note that simply it is :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] leq mathbb{E}_mathbb{Q}bigg[frac{S_1^1}{1+r}bigg] = frac{mathbb{E}_mathbb{Q}(S_1^1)}{1+r} = S_0^1 $$
$$implies$$
$$overline{pi}(C) leq S_0^1 $$
where the probability measure $mathbb{Q}[cdot] := mathbb{P}[cdot | S_1^1 = S_0^1(1+r)]$ was used.
Can you figure out a similar approach for the last part now ?
add a comment |
up vote
2
down vote
(a) Use put-call parity and the fact that the put price must be +ve. (b) Call price cannot exceed the value of the stock, as otherwise buying the stock and selling the call would generate an arbitrage profit.
Could you elaborate please?I understand that you tell to use the put-call parity equation which is $C_t-P_t=S_t-k cdot B(t,T)$.What does "the put price must be +ve" means?
– Magic K. Mamba
Nov 14 at 17:50
It means that the put price must be positive.
– Richard Martin
Nov 14 at 17:53
I can't see how to proceed with this information. Could you provide a thorough solution?
– Magic K. Mamba
Nov 14 at 17:57
First, learn what the symbols mean. Then rearrange the equation so that $C$ is on the left hand side and use $P>0$ (using $1/(1+r)$ for the discount factor).
– Richard Martin
Nov 14 at 18:02
And after this?
– Magic K. Mamba
Nov 14 at 18:15
|
show 4 more comments
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
accepted
$mathbf{(a)}$ From the extension of the Theorem hinted, the lower no-arbitrage bound is :
$$underline{pi}(C) = inf_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{C}{1+r}bigg]=inf_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg]$$
The Jenses inequality in context of probability theory, says thati f $X$ is a random variable and $varphi$ is a convex function, then it is : $varphibig(mathbb{E}[X]big) leq mathbb{E}[varphi(X)]$. The function $f(x) = (x-K)^+, ; K>0$ is convex, thus :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] geqfrac{1}{1+r}big[mathbb{E}_mathbb{Q}(S_1^1 - K)big]^+ $$
$$= frac{1}{1+r}big[mathbb{E}_mathbb{Q}(S_1^1) - mathbb{E}_mathbb{Q}(K)big]^+= frac{1}{1+r}bigg[S_0^1(1+r) - Kbigg]^+$$
$$implies$$
$$underline{pi}(C) geq bigg(S_0^1 - frac{K}{1+r}bigg)^+$$
where the probability measure $mathbb{Q}[cdot] := mathbb{P}[cdot | S_1^1 = S_0^1(1+r)]$ was used.
Now if $mathbb{P}[S_1^1 = S_0^1(1+r)] >0$, this means that at some point it will be $S_1^1 = S_0^1(1+r)$ and thus :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] = mathbb{E}_mathbb{Q}bigg[frac{(S_0^1(1+r) - K)^+}{1+r}bigg] = mathbb{E}_mathbb{Q}bigg[bigg(S_0^1 - frac{K}{1+r}bigg)^+bigg]$$
which means that the equality will hold, since $S_0^1$ is just a constant $>0$ :
$$underline{pi}(C) = bigg(S_0^1 - frac{K}{1+r}bigg)^+$$
For part $mathbf{(b)}$, again from the extension of the Theorem hinted, we have :
$$overline{pi}(C) = sup_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{C}{1+r}bigg]=sup_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg]$$
But, note that simply it is :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] leq mathbb{E}_mathbb{Q}bigg[frac{S_1^1}{1+r}bigg] = frac{mathbb{E}_mathbb{Q}(S_1^1)}{1+r} = S_0^1 $$
$$implies$$
$$overline{pi}(C) leq S_0^1 $$
where the probability measure $mathbb{Q}[cdot] := mathbb{P}[cdot | S_1^1 = S_0^1(1+r)]$ was used.
Can you figure out a similar approach for the last part now ?
add a comment |
up vote
1
down vote
accepted
$mathbf{(a)}$ From the extension of the Theorem hinted, the lower no-arbitrage bound is :
$$underline{pi}(C) = inf_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{C}{1+r}bigg]=inf_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg]$$
The Jenses inequality in context of probability theory, says thati f $X$ is a random variable and $varphi$ is a convex function, then it is : $varphibig(mathbb{E}[X]big) leq mathbb{E}[varphi(X)]$. The function $f(x) = (x-K)^+, ; K>0$ is convex, thus :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] geqfrac{1}{1+r}big[mathbb{E}_mathbb{Q}(S_1^1 - K)big]^+ $$
$$= frac{1}{1+r}big[mathbb{E}_mathbb{Q}(S_1^1) - mathbb{E}_mathbb{Q}(K)big]^+= frac{1}{1+r}bigg[S_0^1(1+r) - Kbigg]^+$$
$$implies$$
$$underline{pi}(C) geq bigg(S_0^1 - frac{K}{1+r}bigg)^+$$
where the probability measure $mathbb{Q}[cdot] := mathbb{P}[cdot | S_1^1 = S_0^1(1+r)]$ was used.
Now if $mathbb{P}[S_1^1 = S_0^1(1+r)] >0$, this means that at some point it will be $S_1^1 = S_0^1(1+r)$ and thus :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] = mathbb{E}_mathbb{Q}bigg[frac{(S_0^1(1+r) - K)^+}{1+r}bigg] = mathbb{E}_mathbb{Q}bigg[bigg(S_0^1 - frac{K}{1+r}bigg)^+bigg]$$
which means that the equality will hold, since $S_0^1$ is just a constant $>0$ :
$$underline{pi}(C) = bigg(S_0^1 - frac{K}{1+r}bigg)^+$$
For part $mathbf{(b)}$, again from the extension of the Theorem hinted, we have :
$$overline{pi}(C) = sup_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{C}{1+r}bigg]=sup_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg]$$
But, note that simply it is :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] leq mathbb{E}_mathbb{Q}bigg[frac{S_1^1}{1+r}bigg] = frac{mathbb{E}_mathbb{Q}(S_1^1)}{1+r} = S_0^1 $$
$$implies$$
$$overline{pi}(C) leq S_0^1 $$
where the probability measure $mathbb{Q}[cdot] := mathbb{P}[cdot | S_1^1 = S_0^1(1+r)]$ was used.
Can you figure out a similar approach for the last part now ?
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
$mathbf{(a)}$ From the extension of the Theorem hinted, the lower no-arbitrage bound is :
$$underline{pi}(C) = inf_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{C}{1+r}bigg]=inf_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg]$$
The Jenses inequality in context of probability theory, says thati f $X$ is a random variable and $varphi$ is a convex function, then it is : $varphibig(mathbb{E}[X]big) leq mathbb{E}[varphi(X)]$. The function $f(x) = (x-K)^+, ; K>0$ is convex, thus :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] geqfrac{1}{1+r}big[mathbb{E}_mathbb{Q}(S_1^1 - K)big]^+ $$
$$= frac{1}{1+r}big[mathbb{E}_mathbb{Q}(S_1^1) - mathbb{E}_mathbb{Q}(K)big]^+= frac{1}{1+r}bigg[S_0^1(1+r) - Kbigg]^+$$
$$implies$$
$$underline{pi}(C) geq bigg(S_0^1 - frac{K}{1+r}bigg)^+$$
where the probability measure $mathbb{Q}[cdot] := mathbb{P}[cdot | S_1^1 = S_0^1(1+r)]$ was used.
Now if $mathbb{P}[S_1^1 = S_0^1(1+r)] >0$, this means that at some point it will be $S_1^1 = S_0^1(1+r)$ and thus :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] = mathbb{E}_mathbb{Q}bigg[frac{(S_0^1(1+r) - K)^+}{1+r}bigg] = mathbb{E}_mathbb{Q}bigg[bigg(S_0^1 - frac{K}{1+r}bigg)^+bigg]$$
which means that the equality will hold, since $S_0^1$ is just a constant $>0$ :
$$underline{pi}(C) = bigg(S_0^1 - frac{K}{1+r}bigg)^+$$
For part $mathbf{(b)}$, again from the extension of the Theorem hinted, we have :
$$overline{pi}(C) = sup_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{C}{1+r}bigg]=sup_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg]$$
But, note that simply it is :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] leq mathbb{E}_mathbb{Q}bigg[frac{S_1^1}{1+r}bigg] = frac{mathbb{E}_mathbb{Q}(S_1^1)}{1+r} = S_0^1 $$
$$implies$$
$$overline{pi}(C) leq S_0^1 $$
where the probability measure $mathbb{Q}[cdot] := mathbb{P}[cdot | S_1^1 = S_0^1(1+r)]$ was used.
Can you figure out a similar approach for the last part now ?
$mathbf{(a)}$ From the extension of the Theorem hinted, the lower no-arbitrage bound is :
$$underline{pi}(C) = inf_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{C}{1+r}bigg]=inf_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg]$$
The Jenses inequality in context of probability theory, says thati f $X$ is a random variable and $varphi$ is a convex function, then it is : $varphibig(mathbb{E}[X]big) leq mathbb{E}[varphi(X)]$. The function $f(x) = (x-K)^+, ; K>0$ is convex, thus :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] geqfrac{1}{1+r}big[mathbb{E}_mathbb{Q}(S_1^1 - K)big]^+ $$
$$= frac{1}{1+r}big[mathbb{E}_mathbb{Q}(S_1^1) - mathbb{E}_mathbb{Q}(K)big]^+= frac{1}{1+r}bigg[S_0^1(1+r) - Kbigg]^+$$
$$implies$$
$$underline{pi}(C) geq bigg(S_0^1 - frac{K}{1+r}bigg)^+$$
where the probability measure $mathbb{Q}[cdot] := mathbb{P}[cdot | S_1^1 = S_0^1(1+r)]$ was used.
Now if $mathbb{P}[S_1^1 = S_0^1(1+r)] >0$, this means that at some point it will be $S_1^1 = S_0^1(1+r)$ and thus :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] = mathbb{E}_mathbb{Q}bigg[frac{(S_0^1(1+r) - K)^+}{1+r}bigg] = mathbb{E}_mathbb{Q}bigg[bigg(S_0^1 - frac{K}{1+r}bigg)^+bigg]$$
which means that the equality will hold, since $S_0^1$ is just a constant $>0$ :
$$underline{pi}(C) = bigg(S_0^1 - frac{K}{1+r}bigg)^+$$
For part $mathbf{(b)}$, again from the extension of the Theorem hinted, we have :
$$overline{pi}(C) = sup_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{C}{1+r}bigg]=sup_{mathbb{Q} in mathcal{P}}mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg]$$
But, note that simply it is :
$$mathbb{E}_mathbb{Q}bigg[frac{(S_1^1 - K)^+}{1+r}bigg] leq mathbb{E}_mathbb{Q}bigg[frac{S_1^1}{1+r}bigg] = frac{mathbb{E}_mathbb{Q}(S_1^1)}{1+r} = S_0^1 $$
$$implies$$
$$overline{pi}(C) leq S_0^1 $$
where the probability measure $mathbb{Q}[cdot] := mathbb{P}[cdot | S_1^1 = S_0^1(1+r)]$ was used.
Can you figure out a similar approach for the last part now ?
answered Nov 15 at 17:49
Rebellos
11.6k21040
11.6k21040
add a comment |
add a comment |
up vote
2
down vote
(a) Use put-call parity and the fact that the put price must be +ve. (b) Call price cannot exceed the value of the stock, as otherwise buying the stock and selling the call would generate an arbitrage profit.
Could you elaborate please?I understand that you tell to use the put-call parity equation which is $C_t-P_t=S_t-k cdot B(t,T)$.What does "the put price must be +ve" means?
– Magic K. Mamba
Nov 14 at 17:50
It means that the put price must be positive.
– Richard Martin
Nov 14 at 17:53
I can't see how to proceed with this information. Could you provide a thorough solution?
– Magic K. Mamba
Nov 14 at 17:57
First, learn what the symbols mean. Then rearrange the equation so that $C$ is on the left hand side and use $P>0$ (using $1/(1+r)$ for the discount factor).
– Richard Martin
Nov 14 at 18:02
And after this?
– Magic K. Mamba
Nov 14 at 18:15
|
show 4 more comments
up vote
2
down vote
(a) Use put-call parity and the fact that the put price must be +ve. (b) Call price cannot exceed the value of the stock, as otherwise buying the stock and selling the call would generate an arbitrage profit.
Could you elaborate please?I understand that you tell to use the put-call parity equation which is $C_t-P_t=S_t-k cdot B(t,T)$.What does "the put price must be +ve" means?
– Magic K. Mamba
Nov 14 at 17:50
It means that the put price must be positive.
– Richard Martin
Nov 14 at 17:53
I can't see how to proceed with this information. Could you provide a thorough solution?
– Magic K. Mamba
Nov 14 at 17:57
First, learn what the symbols mean. Then rearrange the equation so that $C$ is on the left hand side and use $P>0$ (using $1/(1+r)$ for the discount factor).
– Richard Martin
Nov 14 at 18:02
And after this?
– Magic K. Mamba
Nov 14 at 18:15
|
show 4 more comments
up vote
2
down vote
up vote
2
down vote
(a) Use put-call parity and the fact that the put price must be +ve. (b) Call price cannot exceed the value of the stock, as otherwise buying the stock and selling the call would generate an arbitrage profit.
(a) Use put-call parity and the fact that the put price must be +ve. (b) Call price cannot exceed the value of the stock, as otherwise buying the stock and selling the call would generate an arbitrage profit.
answered Nov 14 at 17:38
Richard Martin
1,3938
1,3938
Could you elaborate please?I understand that you tell to use the put-call parity equation which is $C_t-P_t=S_t-k cdot B(t,T)$.What does "the put price must be +ve" means?
– Magic K. Mamba
Nov 14 at 17:50
It means that the put price must be positive.
– Richard Martin
Nov 14 at 17:53
I can't see how to proceed with this information. Could you provide a thorough solution?
– Magic K. Mamba
Nov 14 at 17:57
First, learn what the symbols mean. Then rearrange the equation so that $C$ is on the left hand side and use $P>0$ (using $1/(1+r)$ for the discount factor).
– Richard Martin
Nov 14 at 18:02
And after this?
– Magic K. Mamba
Nov 14 at 18:15
|
show 4 more comments
Could you elaborate please?I understand that you tell to use the put-call parity equation which is $C_t-P_t=S_t-k cdot B(t,T)$.What does "the put price must be +ve" means?
– Magic K. Mamba
Nov 14 at 17:50
It means that the put price must be positive.
– Richard Martin
Nov 14 at 17:53
I can't see how to proceed with this information. Could you provide a thorough solution?
– Magic K. Mamba
Nov 14 at 17:57
First, learn what the symbols mean. Then rearrange the equation so that $C$ is on the left hand side and use $P>0$ (using $1/(1+r)$ for the discount factor).
– Richard Martin
Nov 14 at 18:02
And after this?
– Magic K. Mamba
Nov 14 at 18:15
Could you elaborate please?I understand that you tell to use the put-call parity equation which is $C_t-P_t=S_t-k cdot B(t,T)$.What does "the put price must be +ve" means?
– Magic K. Mamba
Nov 14 at 17:50
Could you elaborate please?I understand that you tell to use the put-call parity equation which is $C_t-P_t=S_t-k cdot B(t,T)$.What does "the put price must be +ve" means?
– Magic K. Mamba
Nov 14 at 17:50
It means that the put price must be positive.
– Richard Martin
Nov 14 at 17:53
It means that the put price must be positive.
– Richard Martin
Nov 14 at 17:53
I can't see how to proceed with this information. Could you provide a thorough solution?
– Magic K. Mamba
Nov 14 at 17:57
I can't see how to proceed with this information. Could you provide a thorough solution?
– Magic K. Mamba
Nov 14 at 17:57
First, learn what the symbols mean. Then rearrange the equation so that $C$ is on the left hand side and use $P>0$ (using $1/(1+r)$ for the discount factor).
– Richard Martin
Nov 14 at 18:02
First, learn what the symbols mean. Then rearrange the equation so that $C$ is on the left hand side and use $P>0$ (using $1/(1+r)$ for the discount factor).
– Richard Martin
Nov 14 at 18:02
And after this?
– Magic K. Mamba
Nov 14 at 18:15
And after this?
– Magic K. Mamba
Nov 14 at 18:15
|
show 4 more comments
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fmath.stackexchange.com%2fquestions%2f2998567%2fproblem-with-lower-and-upper-no-arbitrage-barriers-and-inequalities-financial-ma%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown