Value at Risk in measuring market risk: Historical simulation

In this study the theoretical part was created to make comparison between different Value at Risk models. Based on that comparison one model was chosen to the empirical part which concentrated to find out whether the model is accurate to measure market risk. The purpose of this study was to test if Volatility-weighted Historical Simulation is accurate in measuring market risk and what improvements does it bring to market risk measurement compared to traditional Historical Simulation. Volatility-weighted method by Hull and White (1998) was chosen In order to improve the traditional methods capability to measure market risk. In this study we found out that result based on Historical Simulation are dependent on chosen time period, confidence level and how samples are weighted. The findings of this study are that we cannot say that the chosen method is fully reliable in measuring market risk because back testing results are changing during the time period of this study.

Impact of global warming on potato late blight : risk, yield loss and control

Selostus: Ilmaston lämpenemisen vaikutus perunaruttoon

Improving Transportation and Warehousing Efficiency with Simulation-Based Decision Support Systems

Transportation and warehousing are large and growing sectors in the society, and their efficiency is of high importance. Transportation also has a large share of global carbondioxide emissions, which are one the leading causes of anthropogenic climate warming. Various countries have agreed to decrease their carbon emissions according to the Kyoto protocol. Transportation is the only sector where emissions have steadily increased since the 1990s, which highlights the importance of transportation efficiency. The efficiency of transportation and warehousing can be improved with the help of simulations, but models alone are not sufficient. This research concentrates on the use of simulations in decision support systems. Three main simulation approaches are used in logistics: discrete-event simulation, systems dynamics, and agent-based modeling. However, individual simulation approaches have weaknesses of their own. Hybridization (combining two or more approaches) can improve the quality of the models, as it allows using a different method to overcome the weakness of one method. It is important to choose the correct approach (or a combination of approaches) when modeling transportation and warehousing issues. If an inappropriate method is chosen (this can occur if the modeler is proficient in only one approach or the model specification is not conducted thoroughly), the simulation model will have an inaccurate structure, which in turn will lead to misleading results. This issue can further escalate, as the decision-maker may assume that the presented simulation model gives the most useful results available, even though the whole model can be based on a poorly chosen structure. In this research it is argued that simulation- based decision support systems need to take various issues into account to make a functioning decision support system. The actual simulation model can be constructed using any (or multiple) approach, it can be combined with different optimization modules, and there needs to be a proper interface between the model and the user. These issues are presented in a framework, which simulation modelers can use when creating decision support systems. In order for decision-makers to fully benefit from the simulations, the user interface needs to clearly separate the model and the user, but at the same time, the user needs to be able to run the appropriate runs in order to analyze the problems correctly. This study recommends that simulation modelers should start to transfer their tacit knowledge to explicit knowledge. This would greatly benefit the whole simulation community and improve the quality of simulation-based decision support systems as well. More studies should also be conducted by using hybrid models and integrating simulations with Graphical Information Systems.

Improving strategic decision-making with simulation based decision support systems

Combating climate change is one of the key tasks of humanity in the 21st century. One of the leading causes is carbon dioxide emissions due to usage of fossil fuels. Renewable energy sources should be used instead of relying on oil, gas, and coal. In Finland a significant amount of energy is produced using wood. The usage of wood chips is expected to increase in the future significantly, over 60 %. The aim of this research is to improve understanding over the costs of wood chip supply chains. This is conducted by utilizing simulation as the main research method. The simulation model utilizes both agent-based modelling and discrete event simulation to imitate the wood chip supply chain. This thesis concentrates on the usage of simulation based decision support systems in strategic decision-making. The simulation model is part of a decision support system, which connects the simulation model to databases but also provides a graphical user interface for the decisionmaker. The main analysis conducted with the decision support system concentrates on comparing a traditional supply chain to a supply chain utilizing specialized containers. According to the analysis, the container supply chain is able to have smaller costs than the traditional supply chain. Also, a container supply chain can be more easily scaled up due to faster emptying operations. Initially the container operations would only supply part of the fuel needs of a power plant and it would complement the current supply chain. The model can be expanded to include intermodal supply chains as due to increased demand in the future there is not enough wood chips located close to current and future power plants.

Value at Risk in Foreign Exchange Risk Management

This thesis examines the suitability of VaR in foreign exchange rate risk management from the perspective of a European investor. The suitability of four different VaR models is evaluated in respect to have insight if VaR is a valuable tool in managing foreign exchange rate risk. The models evaluated are historical method, historical bootstrap method, variance-covariance method and Monte Carlo simulation. The data evaluated are divided into emerging and developed market currencies to have more intriguing analysis. The foreign exchange rate data in this thesis is from 31st January 2000 to 30th April 2014. The results show that the previously mentioned VaR models performance in foreign exchange risk management is not to be considered as a single tool in foreign exchange rate risk management. The variance-covariance method and Monte Carlo simulation performs poorest in both currency portfolios. Both historical methods performed better but should also be considered as an additional tool along with other more sophisticated analysis tools. A comparative study of VaR estimates and forward prices is also included in the thesis. The study reveals that regardless of the expensive hedging cost of emerging market currencies the risk captured by VaR is more expensive and thus FX forward hedging is recommended

Bumblebees in a Changing Climate: Evaluating the Effects of Temperature on Queen Performance

Bumblebees are a very essential group of pollinating insects, but their populations have declined drastically during the past decades. We need to understand why their numbers are decreasing and what can be done to reverse this trend. Climate change-related phenomena, such as changes in the overwintering temperatures and spring conditions, are among the most prominent threats to bumblebees. Queens have a special role in the lifecycle of bumblebees because they overwinter and start new colonies the next year. Their successful performance: survival, overwintering ability, longevity, immune competence, and nest establishing capability in spring, is highly important for bumblebee populations. However, the effects of climate change on bumblebee queen performance remain unknown. The main objective of this thesis was to assess how temperature affects the performance of bumblebee queens during and after overwintering. The effects of warm temperature predicted by climate change scenarios on queen survival and stress-tolerance were studied by a four-month artificial diapause of bumblebee queens at two temperatures (9°C and 1.8°C). Bumblebee colonies were also reared in a laboratory and factors affecting colony characteristics were examined. In addition, queen performance during spring was studied in a starvation experiment using two temperatures (15°C as normal; 24°C as warmer than average) and queens collected from nature right after their emergence. My research revealed how temperature affects queen performance, and queen size was found to be an important factor determining the direction of some of these effects. We found a 0.4g weight threshold for bumblebee queens to be able to survive overwintering. In addition, during mild winters, larger queens have a higher chance than smaller ones to survive through winter and also to cope with immunological stresses after overwintering. During cold conditions, which are normal in the current climatic situation, this advantage disappears. In the spring starvation experiment, the starved queens survived approximately eight days longer in 15°C than in 24°C, which means that starvation risk rises significantly with increasing spring temperature, in a situation where food is scarce due to for example frost damage or asynchrony between bumblebees and their important food plants. These results could mean that in the future climate, larger queens are better able to survive the winter, initiate their nests and start rearing their offspring. This may be problematic, because I also detected two alternative strategies of colony development that differ between large and small queens; larger queens start to lay eggs earlier at nest initiation, their colonies mature later, they produce more workers, and they have a more strongly male biased sex allocation compared with smaller queens. If larger queens have a greater change of producing offspring after a mild winter, this could lead to a significant decline in the total production of new queens at a population level. Thus, it seems that queen size could act as one mechanism regulating the population level outcomes in different temperatures. The new information presented in my thesis reinforces that basic research, monitoring, and local species conservation of bumblebees both in Finland and globally must be increased to ensure that this highly important pollinator group survives in the face of climate change.