This paper explores the relationship between Chinese rare earth metals (REMs) and the industries in the U.S and Europe that heavily rely on them. The study uses the EGARCH(1,1)-ARMA(1,0) process for conditional volatility and incorporates it into VAR(8) framework for forecast error variance decomposition to evaluate the static and dynamic volatility spillovers using daily data from the 2nd of January 2018 to the 3rd of March 2023. The liaison of risk management is also consolidated through the incorporation of Value at Risk and Event Study. Our findings indicate that the volatility interconnectedness between the Chinese REMs market and computer and electronics, electric vehicle, and wind energy industries exhibits relatively low volatility spillover to and from each other. Value at Risk measures suggests complexity in assessing the potential short-term losses for REM equity, leading to difficulties in risk management. Establishing and utilizing a derivatives market could be beneficial for future notice. However, the study also highlights that severe geopolitical risk or conflict could enable extreme levels of financial risk due to the global supply dominance of the Chinese quasi-monopolistic construct and the elements' overall criticality in the sustainable energy transition. The study also highlights the infeasibility of Western nations decoupling themselves from the Chinese REM supply. Various factors such as the pace of advancement in sourcing alternatives, technological advancements, and recycling technology are the main drivers of ineligibility. The forecasted global demand for REMs is also expected to increase significantly, primarily driven by the renewable and sustainable energy transition worldwide, further straining the possibility of independence. Therefore, the pace of advancement of these factors must collectively supersede that of the forecasted demand to mitigate the risk. Keywords: Rare Earth Metals, Interconnectedness, Conditional Volatility, Risk Management, Value at Risk, Event Study.

In this work, we look into the practical applicability of three joint quantile and expected shortfall regression backtests. The strict, auxiliary, and intercept ESR backtests are applied to the historical log returns of the OMX Stockholm 30 market-weight price index. We estimate the conditional variance using GARCH models for various rolling window lengths and refitting frequencies. We are particularly interested in the rejection rates of the one-sided intercept ESR backtest as it is comparable to the current standard of backtests. The one-sided test is found to perform well when the conditional variance is estimated by either the GARCH(1,1), GJR-GARCH(1,1), or EGARCH(1,1) coupled with student’s t-innovation residuals and a rolling window size of 1000 days.

This research presents a bi-level stochastic network interdiction model on an attack graph to enable a risk-averse resource constrained cyber network defender to optimally deploy security countermeasures to protect against attackers having an uncertain budget. This risk-averse conditional-value-at-risk model minimizes a weighted sum of the expected maximum loss over all scenarios and the expected maximum loss from the most damaging attack scenarios. We develop an exact algorithm to solve our model as well as several acceleration techniques to improve the computational efficiency. Computational experiments demonstrate that the application of all the acceleration techniques reduces the average computation time of the basic algorithm by 71% for 100-node graphs. Using metrics called mean-risk value of stochastic solution and value of risk-aversion, numerical results suggest that our stochastic risk-averse model significantly outperforms deterministic and risk-neutral models when 1) the distribution of attacker budget is heavy-right-tailed and 2) the defender is highly risk-averse.

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