# Using Financial Instruments to Transfer the Information Security Risks
## 7 Demonstration: An Example Application
### Information Security Futures
#### 7.4.4 Risk Hedging
\[\alpha_{m}=(\rho_{s_{1}}*\mu_{u_{1}})+(\rho_{s_{2}}*\mu_{u_{2}})+(\rho_{s_{3}}* \mu_{u_{3}}) \tag{63}\]  
where, \(\alpha_{\rm m}\) is the market's expected future value (unhedged), \(\rho_{s_{1}}\) is the probability of s\({}_{1}\), \(\mu_{u_{1}}\) is the impact value (unhedged) for state 1, \(\rho_{s_{2}}\) is the probability of s\({}_{2}\), \(\mu_{u_{2}}\) is the impact value (unhedged) for state 2, \(\rho_{s_{3}}\) is the probability of s\({}_{3}\), and \(\mu_{u_{3}}\) is the impact value (unhedged) for state 3. The expected future value from the market's perspective in an unhedged scenario is shown in Figure 19, and calculated as in Equation (64). \[\alpha_{m}=(30\%*0)+(55\%*(-\$100,000))+(15\%*(+\$150,000))=-\$32,500 \tag{64}\]  
Based on the company's risk estimate the expected future value in an unhedged scenario can be calculated as shown in Equation (65). \[\alpha_{c}=(\rho_{s_{1}}*\mu_{u_{1}})+(\rho_{s_{2}}*\mu_{u_{2}})+(\rho_{s_{3}}* \mu_{u_{3}}) \tag{65}\]  
where, \(\alpha_{c}\) is the company's expected future value (Unhedged), \(\rho_{s_{1}}\) is the probability of s\({}_{1}\), \(\mu_{u_{1}}\) is the impact value (unhedged) for state 1, \(\rho_{s_{2}}\) is the probability of s\({}_{2}\), \(\mu_{u_{2}}\) is the impact value (unhedged) for state 2, \(\rho_{s_{3}}\) is the probability of s\({}_{3}\), and \(\mu_{u_{3}}\) is the impact value (unhedged) for state 3. The expected future value from the company's perspective in an unhedged scenario is shown in Figure 20, and calculated as shown in Equation (66). \[\alpha_{c}=(50\%*0)+(35\%*(-\$100,000))+(15\%*(+\$150,000))=-\$12,500 \tag{66}\]  
Figure 19: Unhedged Expected Future Value for Market’s Probability Estimate. Thus, in an unhedged scenario the company may suffer significant financial loss due to cyber-attack. (2.2) Hedged Scenario  
If the company decides to hedge its risk exposure to the state S\({}_{2}\), _i.e._, a loss of $100,000. The first step is to determine the number of ISF contracts that are required to perfectly hedge the risk exposure. We use the Equation (28) to calculate the required number of ISF contracts. Thus, to perfectly hedge the risk exposure of $100,000, number of ISF contracts required are as follows:  
\[\text{(For a perfect hedge, hedge ratio is 1)}=>1=\frac{(\omega*1000)-0}{100,000} \tag{67}\]  
For the sake of clarity and simple demonstration, we ignore the cost of transaction in the above calculation. \[\omega=100 \tag{68}\]  
If the current price of the futures contract is $1000 to the buyer (car manufacturer) for every 1000 cars which fall short of the strike value and the investor pays $05500. We use the Equation (31) to do an ex-ante assessment of hedge effectiveness, as computed in Equation (69). $1000 to the buyer (car manufacturer) for every 1000 cars which fall short of the strike value and the investor pays $1  
Therefore, the above hedge strategy with ISF is expected to be 94.79% effective in mitigating the underlying information security risk. In a perfectly hedged scenario the expected future value from the market's perspective can be calculated as shown in Equation (70). $1000 to the buyer (car manufacturer) for every 1000 cars which fall short of the strike value and the investor pays $2  
where, \(\beta_{m}\) is the market's expected future value (hedged), \(\rho_{s_{1}}\) is the probability of \(s_{1}\), \(\mu_{h_{1}}\) is the impact value (hedged) for state 1, \(\rho_{s_{2}}\) is the probability of \(s_{2}\), \(\mu_{h_{2}}\) is the impact value (hedged) for state 2, \(\rho_{s_{3}}\) is the probability of \(s_{3}\), and \(\mu_{h_{3}}\) is the impact value (hedged) for state 3. $1000 to the buyer (car manufacturer) for every 1000 cars which fall short of the strike value and the investor pays $3 Unhedged Expected Future Value for Company’s Probability Estimate. The expected future value from the market's perspective in a hedged scenario is shown in Figure 21, and calculated as shown in Equation (71).