Method for developing a financial analysis

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Method

The following chapter will explain and examine the different methods applied in this thesis. We will further discuss and motivate the applied methods and provide the reader with insight into the research, as well as a credibility assessment.

Research approach

The purpose of this thesis is to evaluate a new hedging model for currency risk management in the multinational corporation Sandvik AB. Theories and models developed solely for this purpose had to be investigated. A key factor was to discover a relationship between the reality, which is the possible outcome of the hedge, and the existing theories for valuing the undertaken hedging methods. The reason to hedge currency risk is to protect the exposure in the foreign currency from negative fluctuations. By collecting daily historical data from several currencies, the hedging results may be investigated from different hedging models and theories. Personal contact with personnel at Sandvik Financial services was necessary to conduct reliable and relevant research within this specific subject.
Patel and Davidson (2003) states that the most central problem for a scientific research is to identify and connect theory to reality. Three key concepts when dealing with this problem are deduction, induction and abduction. The deductive approach allows a researcher to draw conclusions and obtain results through already proven existing theories. An inductive approach implies that the researcher formulates a theory based on the data collected and the analysis received from it. A combination of these two approaches would be the abduction approach. Two other expressions within business research are quantitative and qualitative and are used to distinguish different data analysis processes and data collection techniques. A qualitative approach implies that the techniques and analysis of the data and procedure is based on, or generates non-numerical data. A quantitative approach could be used as a synonym for data collection techniques and analysis that generates and is based on numerical data. The quantitative data analysis procedure predominately involve analyzing statistics and graphs, while in the qualitative manner the collection is mostly focused on interviews and the analysis of the data are achieved through conceptualization (Saunders, Lewis, & Thornhill, 2009).
According to Saunders, et al. (2009) the deductive approach involves testing theories rather than building them. A hypothesis about the relationship between several variables must be deduced. The hypothesis would due to the large amount of different currencies, involve the relationship between the correlation and the volatility of currencies. Furthermore, because of the framing of the investigated model, the relationship between the volatility and the hedging tenor should be of significant importance.
A collection of quantitative data, which is a characteristic of a deductive approach, was necessary to test this hypothesis between hedge tenors, correlation and volatility. The quantitative data collection contains daily historical exchange rates from 18 different currencies with the SEK as the base currency, nearly ten years back in time. The large numerical sample size would be sufficient to fulfill the deductive characteristic, called generalization. That is, a sufficient numerical sample size is necessary for generalizing a statistical behavior (Saunders, et al., 2009).
The procedure of evaluating the Layered Basket Option hedging clearly shows that we have undertaken mainly a deductive and quantitative approach. Statistical data has been central to conduct the subsequent calculations based on the theories of currency risk management. The personal contact with representatives from the Sandvik group has been a crucial part for us to succeed with our research and a qualitative approach should not be totally detracted.

Research design

There are three main research perspectives; exploratory, descriptive and explanatory (Saunders et al, 2009). We have applied a combination of an exploratory and an explanatory approach. Exploratory research means to achieve improved knowledge within a subject and to clarify dimensions that have not been fully explained. An exploratory study refers to exploring the unknown, and concludes if a study is worth pursuing. In an exploratory study the researcher must be willing to change direction if new findings are found (Saunders et al, 2009). Layered Basket Option hedging is a recently developed model. Due to this novelty, there is a lack of information within the subject. Information was gathered through articles provided from Citibank and interactions with our contact person at Sandvik Financial services, Thomas Hamberg. Explanatory research explains the relationships between variables to clarify a situation or a problem (Saunders et al., 2009). Layered Basket Option hedging differs from other strategies in the sense of the number of variables taken into consideration. For example, the imperfect correlation between assets helps explain why the basket volatility is being reduced in comparison to the average volatility between individually hedged currencies. In order to conduct this research we were dependent on secondary data, where daily exchange rates were the foundation of our collection.

Secondary data

Secondary data is a form of data that has been gathered through former studies (Saunders et al., 2009). The purpose of the previous studies is not necessarily the same as ours. It is important to realize the procedure of data gathering, to comprehend if the data was conducted in a biased or unbiased way (Befring & Andersson, 1994). The majority of the information was gathered through secondary data. Information in how multinational organizations manage their hedging strategies and how the model, Layered Basket Option hedging, stands in relation to other strategies were collected. There was a necessity to understand other hedging strategies to realize the unique aspects of the model. Deeper studies were derived from secondary data in the form of textbooks, academic articles, relevant newspaper articles and organizational articles.
In order to complete our numerical section, other types of secondary data needed to be collected. Firstly, information regarding theories and models were gathered through textbooks and academic articles to get knowledge about theories such as Black Scholes, Value at Risk and portfolio theories. After we determined the best-suited approach, exchange rates on daily basis needed to be gathered. The data was collected through Bank of Canada and from Reuters’ database.

Primary data

When the secondary data could no longer fully answer our research questions, we had to collect further relevant data for our research. This kind of data is equivalent to primary data and should be collected accordingly to our research questions (Ghauri & Grønhaug, 2005).
According to Churchill (1999, p. 215), “Begin with secondary data, and only when the secondary data are exhausted or show diminishing returns, proceed to primary data.”
The procedure stated above has been conducted through our research. Our primary data involved communication with personnel within the Sandvik group. The communication consisted of personal contact and mail conversations. We have been in contact with the Head of Corporate Finance and the CEO of Sandvik Treasury AB. The personal communication have been somewhat informal since we all strove for the same goal, which was to improve and evaluate the hedging strategy for Sandvik AB. Communication in form of mail conversations with personnel within the CitiFX Corporate Solutions Group, the developer of the model, was added to the primary data collected.

Materials used in the financial analysis

Sandvik’s current situation regarding exposed currencies is reflected in their basket. We have chosen to work with a fixed basket to enable a comparison between periods. The exposed sums will be fixed in foreign currencies, while the SEK will vary since the spot rates fluctuate on a day-to-day basis. For example, a USD may be worth 7,00 SEK one day and 7,10 the day after. This implies, if our foreign currencies stand as fixed, the SEK will still vary because of currency fluctuations
The table above (see table 2) illustrates how a basket is structured according to Sandvik’s exposure. The “spot” column refers to the spot prices between a foreign currency and the SEK. The “Exposure” column represents the fixed amounts in foreign currencies that Sandvik is exposed in and the SEK column is the exposed sum represented in SEK. Some of the exposures are negative since Sandvik has a larger proportion of production than sales in some countries and need to make payments in foreign currencies. The last column demonstrates the basket’s distribution of currencies. The weights will vary between periods because of exchange rates fluctuations. The weights are calculated by dividing the exposed sum of a foreign currency represented in SEK, by the total exposed sum in SEK.

Method for developing a financial analysis

The instruments chosen in our investigation were a combination of the Minimum Variance Portfolio Theory and the Black-Scholes formula. The models will be used to calculate the volatility of the baskets and eventually the value of the basket options. The period investigated will be set to 10 years to achieve sufficient and realistic outcomes. The results will be compared to the outcomes of other hedging instruments to conclude if the Layered Basket Option hedging strategy is the appropriate alternative according to Sandviks strategies.

Correlation

Correlation refers to the relationship between two individual assets. Correlation is measured between +1 and -1, where a positive relationship indicates that assets move in the same direction given external circumstances, while a negative refers to the opposite (Aczel & Sounderpandian, 2009). We needed to calculate the pairwise correlation between all currencies in the baskets, which was a requirement to manage the latter calculations. The correlation has been based on historical spot rates and was calculated using an add-in function in Excel. Another alternative is to base the correlation on daily returns. After further research we concluded that an estimation based on spot rates would be preferred. The function created a table that illustrated the correlation between the basket’s currencies (see appendix 11-20). The procedure was repeated every third month, to continuously generate correlations based on historical data for the last year. In total, 37 different correlation tables were compiled.

Standard deviation

Standard deviation is a statistical measurement and explains how much a data set varies from its mean. A high standard deviation refers to a dataset widely spread around the mean. It is a measure of risk, where a high standard deviation is an indication of a risky asset and a low standard deviation the opposite (Ramos, Staking, Calle, Beato, O’Shea, & Carrasco, 2000).
The minimum variance portfolio calculations required a standard deviation for each currency involved. The standard deviations of the currencies were calculated and based on daily returns on our collected dataset. The minimum variance portfolio requires annual standard deviation, which was solved by multiplying the daily standard deviation by √ . The square root sign is necessary because standard deviation is the square root of the variance. As mentioned in the correlation section, the same procedure needed to be done for standard deviation, where each standard deviation was calculated 37 times.

Risk free rate

The Black-Scholes formula requires a risk free rate, which in our case was based on historical data. The data was based on the period 2002-2012 and was collected from The Central Bureau of Statistics. We approximated the risk free rate to 4,5%, which was used throughout our calculations (The Central Bureau of Statistics, 2012). The risk free rate was also approximated based on several countries risk free rates, since the basket consists of eighteen different currencies. The risk free rate was set as constant to increase the transparency for Sandvik AB. This indicates that changes in the basket variance can be located in a more effective way.

Weights

The currencies are represented in both negative and positive weights, and it is important to ensure that the currencies in total correspond to 100% of the basket. A consistently on-going problem throughout this thesis has been to manage the negative weights. If one currency equals -20%, the remaining currencies should represent 120%. The reason why there are negatively weighted currencies is because of Sandvik’s currency exposure. To clarify this, if each currency were to be hedged individually, there would be both put- and call options. Therefore, the basket will contain both positive and negative weights, but still be classified as a basket put option.

Minimum variance Portfolio

The Minimum Variance Portfolio is an approach to calculate the basket variance based on several variables.
The formula above illustrates how to calculate the variance of a basket. The X’s represents the weights of the different assets, denotes the standard deviation of an individual asset and is the correlation between two currencies (Elton, Gruber, Brown and Goetzmann, 2009).

1 Introduction
1.1 Background
1.2 Sandvik AB
1.3 Problem discussion
1.4 Purpose
1.5 Research questions
1.6 Delimitations
2 Theoretical Framework
2.1 Hedging currency risk
2.2 Options
2.3 Black-Scholes
2.4 Basket options
2.5 Minimum variance portfolio (MVP)
2.6 Systematic & non-systematic risk
2.7 Layered hedging
2.8 Layered Basket Option Hedging
2.9 Forward contracts
2.10 Alternative methods
3 Method 
3.1 Research approach
3.2 Research design
3.3 Secondary data
3.4 Primary data
3.5 Materials used in the financial analysis
3.6 Method for developing a financial analysis
3.7 Assumptions
3.8 The layered hedging approach
3.9 The effects of applying a basket
3.10 Alternative hedging strategies
3.11 Improved baskets
3.12 Manipulation of variables to realize the actual effect of changes
3.13 Criticism of theories and models
3.14 Reliability and validity
4 Empirical Findings 
4.1 Sandvik’s hedging purposes
4.2 Standard deviation
4.3 The MVP approach
4.4 The premium using Black-Scholes
4.5 The total result of the hedging strategies
4.6 The results of the Layered Basket Option
4.7 The results of the Layered Forward
4.8 Manipulated baskets
4.9 The effects of applying a basket
4.10 Improved baskets – Divided Layered Basket
5 Analysis 
5.1 Incentives to hedge
5.2 The Layered approach
5.3 The Basket approach
5.4 The Layered Forward approach
5.5 The Layered Basket Option approach
5.6 Improvements
6 Concluding Remarks 
6.1 Conclusions
6.2 Discussion and further studies
7 List of References 
8 Appendices
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Layered Basket Option Hedging Currency risk management for multinational corporations

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