Each semester, the performance of the three assets are measured. Each performance is calculated between the beginning and end of an observation period of 6 months. Then, the weighted half-year performance of the basket is computed taking into account the weights defined at the end of the previous period. The compounded return<\/em> of the strategy is defined as the overall performance of the strategy between the start date of the product and the end of the semester. The process is repeated for each half-period and new weights are assigned to each asset based on their performance. The last coupon is paid using the average of compounded returns<\/em>.<\/p>\n Because of the mechanism of compounded returns<\/em>, the performance of the basket at the beginning of the contract has a major impact on the final coupon. Moreover, the investor is protected against sharp falls through a floor to 0%.<\/p>\n In this simple example, we show the cash flow generated by the dynamic strategy for a given scenario. For simplicity, we’ve performed this analysis on four periods while the actual product has twenty periods.<\/p>\n In this scenario, the last coupon is equal to the average return of indices, or 8.73%. Assuming that a period lasts six months, the annual return is equal to 4.27%.<\/p>\n This product is non-linearly dependent on the future performance of 3 assets. Consequently, forward<\/em> volatility and correlation are the main risks. The mix of different asset classes requires a flexible pricing model. For all assets, stochastic volatility models were selected and calibrated with 6M, 1Y and 2Y volatilities. The Quanto<\/em> effect must be taken into account since DJAIG<\/em> index is listed in U.S dollars is and the product is valued in euros. To this end, we introduced a Black-Scholes<\/em> model on the EUR \/ USD. The correlation between the three indices was calculated according to lambda<\/em> methodology: after computing the historical correlation using fixings<\/em>, we estimate the implied correlation as a weighted average of the historical correlation and a parametric correlation matrix.<\/p>\n Through the mechanism of compounded returns<\/em>, this strategy can potentially produce very high returns, even in the current context of low interest rates. The floor<\/em> adds an interesting protection: The performance of the product lies mainly in the range 0% -5%.<\/p>\n The Greeks<\/em> – sensitivities – show that the product is very sensitive to changes in volatility. The correlation risk, measured through the Cega<\/em>, is significant as most basket products: in input, the correlation must be carefully estimated from the market. Since the product depends on the relative performance of assets, interest rate risk is very low: two assets are indeed driven by the same drift.<\/p>\nProduct features<\/h2>\n
![\"image1.png\"](\"http:\/\/beta.next-finance.net\/wp-content\/uploads\/2009\/10\/image1.png\")
<\/p>\nCash flow example<\/h2>\n
![\"image2.png\"](\"http:\/\/beta.next-finance.net\/wp-content\/uploads\/2009\/10\/image2.png\")
<\/p>\nValuation and risks<\/h2>\n
Returns table of the strategy<\/h2>\n
![\"image_3_.png\"](\"http:\/\/beta.next-finance.net\/wp-content\/uploads\/2009\/10\/image_3_.png\")
<\/p>\nPerformance analysis<\/h2>\n
![\"image4.png\"](\"http:\/\/beta.next-finance.net\/wp-content\/uploads\/2009\/10\/image4.png\")
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