We product test the structured financial products offered in Mangold Fondkommission AB’s issue nr. 7, according to guidelines set up by the European Securities and Market Authority. The constructed model is a real world economic scenario generator (ESG) that forecasts future performance of the assets that underlies each product. In the model, we assume that stock paths follow a geometric Brownian motion where volatility is time dependent, heteroscedastic and auto correlated. Given a forecast, we test a product’s performance to receive understanding of the risks that a holder might face. The results have led to the conclusion that the potential return corresponds to the level of risk taken by the investor. Thus, product testing would serve as good practice for all distributors in order to find the most suitable structured product for a given customer due to their risk- and reward profile.

<p>Structured products are investment vehicles that combine basic financial instruments to provide private investors with packaged solutions to more advanced investment strategies in financial markets.</p><p>This paper investigates the pricing of 22 index-linked notes examined during their full life cycles between January 12, 2005 and January 17, 2007. The selected products constitute some 40% of the structured products issued by Swedbank in 2005, which at the time of the study is the second largest issuer of structured products to private investors in Sweden. Quoted prices on secondary markets are compared with duplication strategies using exchange traded options in order to calculate price differences.</p><p>The pricing results show that quoted prices deviate very little from their theoretical values in secondary markets. The price deviations are surprisingly low in an international comparison. Some indications have been found that the market maker is able to influence prices on secondary market by orienting the pricing towards the relative life cycle and moneyness of the structured products.</p><p>The importance of patterns in price deviations can, however, be questioned since the absolute level of pricing errors is low. There is little evidence to suspect that the issuer, Swedbank, systematically quotes prices that are not in line with their theoretical values. Sophisticated investors are thus likely to be able to judge the attractiveness of the structured product issue by comparing the transaction costs of the instruments in a duplication strategy with the transaction costs of the structured product. The author welcomes further research that includes multiple issuers to determine whether these findings apply for the Swedish market as a whole.</p>

Structured products are investment vehicles that combine basic financial instruments to provide private investors with packaged solutions to more advanced investment strategies in financial markets. This paper investigates the pricing of 22 index-linked notes examined during their full life cycles between January 12, 2005 and January 17, 2007. The selected products constitute some 40% of the structured products issued by Swedbank in 2005, which at the time of the study is the second largest issuer of structured products to private investors in Sweden. Quoted prices on secondary markets are compared with duplication strategies using exchange traded options in order to calculate price differences. The pricing results show that quoted prices deviate very little from their theoretical values in secondary markets. The price deviations are surprisingly low in an international comparison. Some indications have been found that the market maker is able to influence prices on secondary market by orienting the pricing towards the relative life cycle and moneyness of the structured products. The importance of patterns in price deviations can, however, be questioned since the absolute level of pricing errors is low. There is little evidence to suspect that the issuer, Swedbank, systematically quotes prices that are not in line with their theoretical values. Sophisticated investors are thus likely to be able to judge the attractiveness of the structured product issue by comparing the transaction costs of the instruments in a duplication strategy with the transaction costs of the structured product. The author welcomes further research that includes multiple issuers to determine whether these findings apply for the Swedish market as a whole.

Currently, the companies are exposed to many kinds of risk during the business, especially in case of activities which are overlapping the frontiers of inland market. Risk-aversive businessmen tend to minimize or even eliminate those risks so that they use different types of hedging instruments. The derivatives are a good choice but they are not able to meet all clients' needs sufficiently in their classic simple form at present. That is the reason for rise of their new combinations and modifications which are so-called "tailor-made" for each company separately. The purpose of this paper is to create the compact overview about those "second generation" products and show the interest and currency risk hedging options.

In the process of building maintainable and customizable software used and displayed in different mediums, a user interface (UI) that is auto-generated from meta-data can be built. That way minimal effort can be made when customizing the software. This thesis took place at the financial software company SunGard. Traders have requested a web based solution to one of SunGard's financial softwares, and will be used for managing structured products. The solution had to work with some already defined web and server components that SunGard is using. The web tool used for making a prototype and evaluating the suitability was AngularJS. As a result it was found that it was possible to create a meta-data driven UI. Some programmatic design issues when generating the UI are discussed.

Autocallable structured products represent an investment opportunity which has been growing in both the European and American market since they were first launched. The value of these structured products is dependent on how their underlying assets perform, which can consist of stocks, indexes or other assets. With a sample size of 30 structured products we provide research on the relation between the products return and the return of the underlying assets. Specifically, the purpose of the study is to analyse how increases in the returns of the underlying assets affect the returns in the products. Using an ordinary least squares regression model, we find that the return in the underlying assets, the issuers credit rating and the interest rate at issuance have a statistically significant effect on the returns in the products. We conclude that in our sample, an increase in the underlying assets returns results in a less than equal increase in the returns of the autocalls.

Abstract We propose an algorithm to price and analyze the performance of auto-callable structured _nancial products. The algorithm contains Monte-Carlo simulations in order to reproduce, as probable as possible, a future product. This model is then compared to other, previously presented models. The di_erent in-data parameters together with a time dependency study is then performed to evaluate what one might expect when investing in these products. Numerical results conclude that, the risks taken by the investor closely reect the potential return for each product. When constructing these products for the near future, one must closely evaluate the demand from the investors i.e. evaluate the level of risk that the investors are willing to take.

Structured products are complex non-linear financial instruments that make it difficult to calculate their future risk and return. Two categories of structured products are Capital Protected and Participation notes, which are built by bonds and options. Since the structured products are non-linear, it is difficult to asses their long-term risk today. This study, conducted at Nordea Markets, focuses on the risk of structured products and how the risk and return in a portfolio changes when we include structured products into it. Nordea can only calculate the one-year risk with their current risk advisory tool, which makes long time predictions difficult. To solve this problem, we have simulated portfolios and structured products over a five-year time horizon with the Monte Carlo method. To investigate how the structured product allocations behave in different conditions, we have developed three test methods and a ranking program. The first test method measures how different underlying assets changes the risk and return in the portfolio allocations. The second test method varies the drift, volatility, and correlation for both the underlying asset and the portfolio to see how these parameters changes the risk and return. The third test method simulates a crisis market with high correlations and low drift. All these tests go through the ranking program, the most important part, where the different allocations are compared against the original portfolio to decide when the allocations perform better. The ranking is based on multiple risk measures, but the focus in this study is at using Expected Shortfall for risk while the expected return is used for ranking the return. We used five different reference portfolios and six different structured products with specific parameters in an example run where the ranking program and all three test methods are used. We found that the properties of the reference portfolio and the structured product’s underlying are significant and affect the performance the most. In the example run it was possible to find preferable cases for all structured products but some performed better than others. The test methods revealed many aspects of portfolio allocation with structured products, such as the decrease in portfolio risk for Capital Protected notes and increase in portfolio return for Participation notes. Our ranking program proved to be useful in the sense that it simplifies the result interpretations.

The objective of the thesis is to acquaint readers with field of structured product valuation. It is a relatively complex issue which is, however, based on general valuation foundations. The opening chapter is dedicated to these general fundamentals of valuation. Emphasis is placed mainly on present value principle, a specific variant of comparison, and its related aspects. The second section describes key elements of structured product valuation. Greater part of this chapter is devoted to the Monte Carlo simulation, the most employed tool in valuation of these products in practice. An important part of Monte Carlo simulation is an option spread, which arises as by-product of the simulation and reflects value of an option contained in the evaluated instrument. Third chapter is focused on interest rate and prepayment models. Level of prepayment is dependent on interest rates development which both are the most critical factors that affect value of structured products. Description of models includes theoretical and mathematical formulation as well as mentioning their advantages and disadvantages. Valuation model is illustrated in the last part, which is demonstrated on valuation of hypothetical structured products example. Based on the model, the development of cash flows from underlying asset portfolio is forecasted which in turn determines the value of evaluated instruments. The final section deals with advantages of structured products and, hence, why banks and other institutions use them in practice.

Monte Carlo methods in derivative pricing are computationally expensive, in particular for evaluating models partial derivatives with regard to inputs. This research proposes the use of deep learning to approximate such valuation models for highly exotic derivatives, using automatic differentiation to evaluate input sensitivities. Deep learning models are trained to approximate Phoenix Autocall valuation using a proprietary model used by Svenska Handelsbanken AB. Models are trained on large datasets of low-accuracy (10^4 simulations) Monte Carlo data, successfully learning the true model with an average error of 0.1% on validation data generated by 10^8 simulations. A specific model parametrisation is proposed for 2-day valuation only, to be recalibrated interday using transfer learning. Automatic differentiation approximates sensitivity to (normalised) underlying asset prices with a mean relative error generally below 1.6%. Overall error when predicting sensitivity to implied volatililty is found to lie within 10%-40%. Near identical results are found by finite difference as automatic differentiation in both cases. Automatic differentiation is not successful at capturing sensitivity to interday contract change in value, though errors of 8%-25% are achieved by finite difference. Model recalibration by transfer learning proves to converge over 15 times faster and with up to 14% lower relative error than training using random initialisation. The results show that deep learning models can efficiently learn Monte Carlo valuation, and that these can be quickly recalibrated by transfer learning. The deep learning model gradient computed by automatic differentiation proves a good approximation of the true model sensitivities. Future research proposals include studying optimised recalibration schedules, using training data generated by single Monte Carlo price paths, and studying additional parameters and contracts.