The COVID-19 pandemic and the subsequent lockdown brought about a massive slowdown of the economy and an unparalleled stock market crash. Using U.S. data, this paper explores how firms with high Environmental and Social (ES) ratings fare during the first quarter of 2020 compared to other firms. We show that stocks with high ES ratings have significantly higher returns, lower return volatilities, and higher trading volumes than other stocks. Firms with high ES ratings and high advertising expenditures perform especially well during the crash. This paper highlights the importance of ES policies in making firms more resilient during a time of crisis.

This thesis consists of three research topics, which together study the related topics of volatility jumps, modeling volatility and forecasting Value-at-Risk (VaR). The first topic focuses on volatility jumps based on two recently developed jumps detection methods and empirically studied six markets and the distributional features, size and intensity of jumps and cojumps. The results indicate that foreign exchange markets have higher jump intensities, while equity markets have a larger jump size. I find that index and stock markets have more interdependent cojumps across markets. I also find two recently proposed jump detection methods deliver contradictory results of jump and cojump properties. The jump detection technique based on realized outlyingness weighted variation (ROWV) delivers higher jump intensities in foreign exchange markets, whereas the bi-power variation (BV) method produces higher jump intensities in equity markets. Moreover, jumps under the ROWV method display more serial correlations than the BV method. The ROWV method detects more cojumps and higher cojumps intensities than the BV method does, particularly in foreign exchange markets. In the second topic, the Model Confidence Set test (MCS) is used. MCS selects superior models by power in forecasting ability. The candidate models set included 9 GARCH type models and 8 realized volatility models. The dataset is based on six markets sparming more than 10 years, avoiding the so- called data snooping problem. The dataset is extended by including recent financial crisis periods. The dc.description.abstract advantage of the MCS test is that it can compare models in a group, not only in a pair. Two loss functions that are robust to noise in volatility proxy were also implemented and the empirical results indicated that the traditional GARCH models were outperformed by realized volatility models when using intraday data. The MCS test based on MSE selected asymmetric ARFlMA models and the HAR mode as the most predictive, while the asymmetric QLike loss function revealed the leveraged HAR and leveraged HAR-CJ model based on bi-power variation as the highest performers. Moreover, results from the subsamples indicate that the asymmetric ARFIMA model performs best over turbulent periods. The third topic focuses on evaluating a broad band ofVaR forecasts. Different VaR models were compared across six markets, five volatility models, four distributions and 8 quantiles, resulting in 960 specifications. The MCS test based on regulatory favored asymmetric loss function was applied and the empirical results indicate that the proposed asymmetric ARFIMA and leveraged HAR models, coupled with generalized extreme value distribution (GEV) or generalized Pareto distribution (GPD), have the superior predictive ability on both long and short positions. The filtered extreme value methods were found to handle not only extreme quantiles but also regular ones. The analysis conducted in this thesis is intended to aid risk management, and subsequently reduce the probability of financial distress in the sector.

Variable annuities (VAs) are equity-linked annuities with embedded investment guarantees. Their long-term security and tax deferred features have made them one of the major insurance products in the world. Nowadays, many insurance companies are managing large VA portfolios that contain hundreds of thousands policies. Since most of the VA contributions are invested in the equity market, the insurance companies are exposed to significant market risks and risk managing the VA liabilities has become the central task. In practice, the stochastic-on-stochastic nested simulation is commonly used for VA portfolio valuation and risk management. The path-dependency of the embedded guarantees and the non-homogeneity of the VA policies make the nested simulation algorithm extremely complex and time-consuming to run. As a result, timely managing the portfolio risks becomes a major challenge to the insurance companies. The complexity of the nested simulation algorithm depends directly on three components: the number of policies, the number of outer-loop simulation, and the number of inner-loop simulation. In this thesis, we incorporate the idea of surrogate modelling to the nested simulation algorithm such that all of the input dimensions are reduced. The surrogate models act as proxies to approximate the input/output relationships. Since only a few input points are needed in order to identify the surrogate models, the simulation time could be shortened significantly. The key feature of the proposed algorithm is that the methodologies for selecting the inputs are theoretically justifiable from the statistical properties of the surrogate models. As a result, a robust performance can be ensured for the proposed algorithm in different context. Specifically, we introduce a model-assisted estimation framework with balanced sampling to reduce the number of policies, and a spline regression framework with scenario clustering to reduce the number of outer-/inner-loops. The proposed algorithm is applied to perform various valuations for large synthetic VA portfolios such as calculating the predictive liability distribution, the portfolio Greeks, and the regulatory capital requirement with dynamic hedging. The efficiency and the robustness of the algorithm are demonstrated through numerical studies on a number of uniform/non-uniform large synthetic VA portfolios and economics models.

Paid search advertisers strive to optimize search relevance (through ad design) and competitive advantage of their ads (through bidding strategies) so as to more effectively attract consumers' attention. Consumers' interest in search ads that appear with search results may extend to ads which do not necessarily have a literal or exact match to the search term, but which are related to the information they are searching for. Their interest in relevant ads may also spill over to competing ads which often appear in close visual proximity to focal ads. Hence, different from exact matching and positional competitive advantages that have been well examined in the extant literature, this study develops a systematic framework of metrics of 'inexact' (but relevant) matching and 'non-positional' competitive advantages from an informational matching perspective. We introduce the concept of 'semantic matching' to capture the semantic relevance of paid search ad content to the information searched for by consumers and also introduce two information-based metrics of competitive advantages. Empirical results using a unique dataset from Google China show that 'semantic matching' can effectively increase click-through as compared to 'exact matching'. Our results using competitive metrics provides empirical evidence for the positive externality effect of search ad competition predicted by the theoretical literature in economics and marketing, while showing that in the presence of directionality, the negative competitive effect dominates the positive externality effect. We study the variability of extant and proposed metrics with complexity of consumer search and also demonstrate the efficacy of the new matching and competition metrics in improving search ad design.

Powered by advanced artificial intelligence (AI) algorithms, frontline customer-facing robots can complete a variety of services reliably and independently. Despite accelerated adoption in practice, how service robots affect customer experience and demand remains unclear. Utilizing a unique data set from a large hotel group, this study is among the first to empirically investigate the impact of service robots on customer demand. We show that customer demand increases on average followed by the adoption of service robots and the benefit of service robots is stronger for premium hotels than for economy hotels. Our further analyses reveal that service robots have a positive effect on demand in the short term (i.e., less than two months) for both premium hotels and economy hotels, whereas a longer-term positive effect only exists for premium hotels. We uncover the underlying mechanisms by examining the mediation role of customer experience, mined from the textural content of customer reviews. Results of the mediation analyses suggest that service robots serve as a marketing gimmick to attract customers for both premium hotels and economy hotels. However, such a gimmick effect is only evident in the short term. Interestingly, service robots can improve employees' service quality for premium hotels, thereby benefiting customer experience and hotel demand in the longer term. Our findings provide important theoretical and practical implications.

To achieve passive vibration control, an adaptive flywheel design is proposed and fabricated from two different materials. The corresponding mathematical models for the adaptive flywheels are developed. A two-terminal hydraulic device and a two-terminal inverse screw device are introduced to analyze the two adaptive flywheels. Experiments are carried out to identify key parameters for both the two-terminal hydraulic system and the inverse screw system. The performance of three different suspension systems are evaluated; these are the traditional suspension system, the suspension system consisting of an ideal two-terminal device with constant flywheel and the suspension system consisting of an ideal two-terminal device with an adaptive flywheel (AFW suspension system). Results show that the AFW suspension system can outperform the other two suspension systems under certain conditions. The performance of a suspension system with the adaptive flywheel under different changing ratio is evaluated, and an optimal changing ratio is identified under certain circumstances. To obtain the steady-state response of the two-terminal device with adaptive flywheel, three different methods have been applied in this thesis. These methods are the single harmonic balance method, the multi-harmonic balance method and the scanning iterative multi-harmonic balance method, respectively. Compared to the single harmonic balance method, the multi-harmonic balance method provides a much more accurate system response. However, the proposed scanning iterative multi-harmonic balance method provides more accurate system response than the single harmonic balance method with much less computational effort.