A growing need for clean and sustainable energy is causing a significant shift in the electricity generation paradigm. In the electricity system of the future, integration of renewable energy sources with smart grid technologies can lead to potentially huge economical and environmental benefits ranging from lesser dependency on fossil fuels and improved efficiency to greater reliability and eventually reduced cost of electricity. In this context, microgrids serve as one of the main components of smart grids with high penetration of renewable resources and modern control strategies. This dissertation is concerned with developing optimal control strategies to manage an energy storage unit in a grid-connected microgrid under uncertainty of electricity demand and prices. Two methods are proposed based on the concept of rolling horizon control, where charge/discharge activities of the storage unit are determined by repeatedly solving an optimization problem over a moving control window. The predicted values of the microgrid net electricity demand and electricity prices over the control horizon are assumed uncertain. The first formulation of the control is based on the scenario-based stochastic conditional value at risk (CVaR) optimization, where the cost function includes electricity usage cost, battery operation costs, and grid signal smoothing objectives. Gaussian uncertainty is assumed in both net demand and electricity prices. The second formulation reduces the computations by taking a worst-case CVaR stochastic optimization approach. In this case, the uncertainty in demand is still stochastic but the problem constraints are made robust with respect to price changes in a given range. The optimization problems are initially formulated as mixed integer linear programs (MILP), which are non-convex. Later, reformulations of the optimization problems into convex linear programs are presented, which are easier and faster to solve. Simulation results under different operation scenarios are presented to demonstrate the effectiveness of the proposed methods. Finally, the energy management problem in network of grid-connected microgrids is investigated and a strategy is devised to allocate the resulting net savings/costs of operation of the microgrids to the individual microgrids. In the proposed approach, the energy management problem is formulated in a deterministic co-operative game theoretic framework for a group of connected microgrids as a single entity and the individual savings are distributed based on the Shapley value theory. Simulation results demonstrate that this co-operation leads to higher economical return for individual microgrids compared to the case where each of them is operating independently. Furthermore, this reduces the dependency of the microgrids on the utility grid by exchanging power locally. / Thesis / Master of Applied Science (MASc)

This study presents a rigorous exploration of the effects of single versus multiple M&A deals on the performance of acquiring firms. A pooled ordinary least squares (OLS) regression analysis was employed to examine our key performance indicators: Value at Risk (VaR), Value of Return (VoR), and Returns across year, month, and week time frames. Findings reveal that multiple M&A deals can enhance stock performance and VoR, while also increasing VaR, indicating higher potential for downside risk. This research enriches the M&A literature by focusing on the differential impacts of single and multiple acquisitions on these crucial financial metrics. Its insights guide firms and investors in understanding the delicate interplay between the number of acquisitions, prospective gains, and related risks.

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An asset manager's goal is to provide a high return relative the risk taken, and thus faces the challenge of how to choose an optimal portfolio. Many mathematical methods have been developed to achieve a good balance between these attributes and using di erent risk measures. In thisthesis, we test the use of a relatively simple and common approach: the Markowitz mean-variance method, and a more quantitatively demanding approach: the tail optimization method. Using active portfolio based on data provided by the Swedish fund management company Enter Fonderwe implement these approaches and compare the results. We analyze how each method weighs theunderlying assets in order to get an optimal portfolio.

The green bond market has gained significant traction over the past few years. In 2019, issuers released $320 billion worth of green bonds, compared to $490 billion in 2023 (Yamaguchi & Ramos, 2024). This study applies the widely used metric Value-at-Risk (VaR) to better understand the risk profile of this relatively new financial instrument. The study forecasts volatility using an ARMA(1,1)-GARCH(1,1) model to simulate VaR. The thesis employs regression analysis to explore higher downside risk and a green premium in the primary Nordic bond market for green bonds. The findings reveal that green bonds carry higher downside risk, as evidenced by significantly larger VaR values for green-labeled bonds. Additionally, the study identifies a lower yield to maturity associated with green bonds compared to conventional bonds, indicating a green premium in the primary Nordic bond markets. This research contributes to the growing body of literature on green finance, providing valuable insights for investors regarding the risks and yields associated with green bonds. The findings of this study are essential since ESG is a crucial topic for today’s investors, and a green premium encourages firms to invest in more sustainable projects. If proof of investors willingness to pay a premium for sustainability was confirmed, firms might undertake more climate-friendly projects. Further research should explore alternative risk metrics and apply these metrics across different regions.

Ex-post intraday market-risk extrema are compared with ex-ante standard RiskMetrics parametric Value-at-Risk (VaR) limits for three foreign currency futures markets (British Pound, Japanese Yen, Swiss Frank) to determine whether forecasted volatility of market returns based on settlement price data provides a valid proxy for short-term market risk independent of market leverage. Intraday violations of ex-ante one-day VaR limits at the 95% confidence level should occur for less than 5% of market days. Violation frequencies for each of the markets tested are shown to occur well in excess of this 5% tolerance level: 9.54% for the British Pound, 7.09% for the Japanese Yen, and 7.79% for the Swiss Franc futures markets. Thus, it is empirically demonstrated that VaR is a poor proxy for short-term market risk under conditions of market leverage. Implications for managing (measuring, monitoring, controlling), reporting, and regulating financial market risk are discussed. / Master of Arts

No / Risks exist in all aspects of our lives. Using data in both Scopus and ISI Web of Science, this review paper identifies pioneer work and pioneer scholars in enterprise risk management (ERM). Being ranked the first based on the review data, Desheng Wu has been active in this area by serving as a good academic network manager on the global research network, His global efforts with diverse networking have enabled him to publish outstanding papers in the field of ERM. Therefore, this paper also conducts a literature review of his papers and critical analysis of the work of Wu and Olson, from the perspective of the ERM, to glean implications and suggestions for the optimization and customization of the ERM. / NFSC grant (Grant # 71471055), the 100-Talents plan Program at Chinese Academy of Sciences and 1000-Talents plan Program for the Young Scientists.

The 2007 financial crisis highlighted the severe risks posed by counterparty defaults in financial markets. Assessing and addressing counterparty credit risk has consequently been a focal point of new regulations introduced in the wake of the crisis. The Central Clearing Counterparty (CCP) is at the heart of the solution, an entity dedicated to managing and mitigating counterparty risk in a market. CPPs manage risk by collecting collateral, referred to as margin, from the participants trading on the market. Appropriately sizing the margin is of utmost importance for the CCP to maintain the integrity of its operation and, by extension, protect the participants in the market. Most contemporary margin methodologies require significant resources which precludes frequent margin updates. In light of this issue, our work examines the capability of replicating the popular margin methodology Historical Simulation Value at Risk using machine-learning-based methods envisioning that an adequate such model could be used as a complement to the traditional model, providing real-time margin estimations. The experiment concerns portfolios containing stocks, bonds, and options and uses static market data and scenarios. We conclude that neither of the ensemble methods are sufficiently accurate, while both of the neural network-based models show moderate promise, warranting further development.

Within Central Counterparty Clearing, the Clearing House collects Initial Margin from its Clearing Members. The Initial Margin can be calculated in many ways, one of which is by applying the commonly used risk measure Value-at-Risk. However, Value-at-Risk has one major flaw, namely its inability to encapsulate Tail Risk. Due to this, there has for long been a desire to replace Value-at-Risk with Expected Shortfall, another risk measure that has shown to be much better suited to encapsulate Tail Risk. That said, Value-at-Risk is still used over Expected Shortfall, something which is mainly due to the fact that there is no consensus regarding how one should backtest Expected Shortfall. The goal of this thesis is to evaluate some of the most commonly proposed methods for backtesting Expected Shortfall. In doing this, several non-parametric backtests of Expected Shortfall are investigated using simulated data as well as market data from different types of securities. Moreover, this thesis aims to shed some light on the differences between Value-at-Risk and Expected Shortfall, highlighting why a change of risk measure is not as straightforward as one might believe. From the investigations of the thesis, several backtests are found to be sufficient for backtesting the Initial Margin with Expected Shortfall as the risk measure, the so called Minimally Biased Relative backtest showing the overall best performance of the looked at backtests. Further, the thesis visualizes how Value-at-Risk and Expected Shortfall are two risk measures that are inherently different in a real-world setting, emphasizing how one should be careful making conversions between the two based upon parametric assumptions.