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May 19

Question 1 from UoL exam 2016, Zone B, Post 1




Question 1 from UoL exam 2016, Zone B, Post 1

There is a hidden mine in this question, and it is caused by discreteness of the distribution function. We had a lively discussion of this oddity in my class. The answer will be given in two posts.

Question. Two corporations each have a 4% chance of going bankrupt and the event that one of the two companies will go bankrupt is independent of the event that the other company will go bankrupt. Each company has outstanding bonds. A bond from any of the two companies will return R=0% if the corporation does not go bankrupt, and if it goes bankrupt you lose the face value of the investment, i.e., R=-100%. Suppose an investor buys $1000 worth of bonds of the first corporation, which is then called portfolio P_1, and similarly, an investor buys $1000 worth of bonds of the second corporation, which is then called portfolio P_2.

(a) [40 marks] Calculate the VaR at \alpha=5\% critical level for each portfolio and for the joint portfolio P_1+P_2.

(b) [30 marks] Is VaR sub-additive in this example? Explain why the absence of sub-additivity may be a concern for risk managers.

(c) [30 marks] The expected shortfall ES^\alpha at the \alpha =5\% critical level can be defined as ES^\alpha=-E_t[R|R<-VaR_{t+1}^\alpha]. Calculate the expected shortfall for the portfolios P_1, P_2 and P_1+P_2. Is this risk measure sub-additive?

Solution. a) The return on each portfolio is a binary variable described by Table 1:

Table 1. Return on separate portfolios

R_i Prob
0 0.96
-100 0.04

Therefore the distribution function F_{R_i}(x) of the return is a piece-wise constant function equal to 0 for x<-100, to 0.04 for -100\leq x<0 and to 1 for x\geq 0, see Example 3. For instance, if x\geq 0 we can write

F_{R_i}(x)=P(R_i\leq x)=P(R_i<-100)+P(R_i=-100)+P(-100<R_i<0)+P(R_i=0)+P(0<R_i\leq  x)=0.04+0.96=1.

Return distribution function

Diagram 1. Return distribution function

Since this function is not one-to-one, it's usual inverse does not exist and we have to use the quasi-inverse, see Answer 15. As with the distribution function, we need to look at different cases.

If y>1, drawing a horizontal line at y we see that the set \{x:F_{R_i}(x)\geq y\} is empty and the infimum of an empty set is by definition +\infty (can you guess why?)

For any 1\geq y>0.04, we have \{x:F_{R_i}(x)\geq y\}=[1,+\infty ), so F_{R_i}^{-1}(y)=1.

Next, for 0<y\leq 0.04 we have \{x:F_{R_i}(x)\geq y\}=[-100,+\infty ) and F_{R_i}^{-1}(y)=-100.

Finally, if y\leq 0, we get \{x:F_{R_i}(x)\geq  y\}=(-\infty ,+\infty ) and F_{R_i}^{-1}(y)=-\infty .

Quase-inverse function

Diagram 2. Quasi-inverse function

 

The resulting function is bad in two ways. Firstly, it takes infinite values. In applications to VaR this should not concern us because the bad values occur in the ranges y>1 and y\leq 0.

Secondly, for practically interesting values of y\in (0,1), the graph of F_{R_i}^{-1} has flat pieces, which may be problematic. By definition, VaR^\alpha is the solution to the equation P(R_i\leq VaR^\alpha)=\alpha . This means that we should have VaR^\alpha=F_{R_i}^{-1}(\alpha). When we plug this value in F_{R_i}(x), we are supposed to get \alpha . However, here we don't, in general. For example, VaR^{0.02}=F_{R_i}^{-1}(0.02)=-100, while F_{R_{i}}(-100)=0.04\neq 0.02.

This happens because the usual inverse does not exist. When the usual inverse exists, we have two identities F^{-1}(F(x))=x and F(F^{-1}(y))=y. Here both are violated.

Now the definition of VaR gives VaR_{R_i}^\alpha=F_{R_i}^{-1}(0.05)=1 for each portfolio.

To be continued.

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