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This dissertation aims to investigate the asset pricing implications of the stock option's implied volatility term structure. We mainly focus on two directions: the volatility term structure of the market and the volatility term structure of individual stocks. The market volatility term structure, which is calculated from prices of index options with different expirations, reflects the market's expectation of future volatility of different horizons. So the market volatility term structure incorporates information that is not captured by the market volatility itself.
Supply chain management is one of the fundamental topics in the field of operations research, and a vast literature exists on the subject. Many recent developments in the field are rapidly narrowing the gap between the systems handled in the literature and the real-life problems companies need to solve on a day-to-day basis. However, there are certain features often observed in real-world systems that elude even these most recent developments. In this thesis, we consider a number of these features, and propose some new heuristics together with methodologies to evaluate their performance.
Many systems in services, manufacturing, and technology, feature users or customers sharing a limited number of resources, and which suffer some form of congestion when the number of users exceeds the number of resources. In such settings, queueing models are a common tool for describing the dynamics of the system and quantifying the congestion that results from the aggregated effects of individuals joining and leaving the system.
This dissertation focuses the corporate behaviors in a dynamic world with uncertainty. Especially, I am interested in how firms tradeoff their investment and cash savings when external financing is costly. The first two chapters fit into this theme. One considers optimal investment and financing policies when uncertainty itself is time-varying, the second investigates how firms prepare themselves against devaluation risks. Both chapters build dynamic corporate theories and test them empirically.
The rapid advance of information technologies largely facilitated firms' data-driven decision making. Particularly, in operations management practices, firms could continuously collect information to refine their demand knowledge, and integrate this process into their relevant operational decisions, e.g. pricing, inventory, and market entry, known as demand learning. Demand learning in complex business systems is often tangled with complex strategic interactions, thus requiring a deep understanding of how it affects the strategic relationship among players in various business setups.
This thesis is concerned with addressing operational issues in two types of dynamic markets where queueing plays an important role: limit order books (financial industry), and dynamic matching markets (residential real estate). We first study the smart order routing decisions of investors in fragmented limit order book markets and the implications on the market dynamics. In modern equity markets, participants have a choice of many exchanges at which to trade.
Sequential decision making problems are ubiquitous in a number of research areas such as operations research, finance, engineering and computer science. The main challenge with these problems comes from the fact that, firstly, there is uncertainty about the future. And secondly, decisions have to be made over a period of time, sequentially. These problems, in many cases, are modeled as Markov Decision Process (MDP). Most real-life MDPs are ‘high dimensional’ in nature making them challenging from a numerical point of view. We consider a number of such high dimensional MDPs.
Conventional wisdom and a wealth of research suggest that effective networks are an important key to career success. Yet, why do so many people struggle to build and maintain professional relationships? In this dissertation I argue that, rather than not knowing how to network, most people feel conflicted about the idea of networking. The present research applies a motivational framework to networking. Building on the idea of lay theories in motivational psychology, this dissertation investigates how lay theories of social intelligence influence networking engagement.
The entertainment industry is a highly competitive and risky business with only few successes. The ways in which we experience music, movies, games, books, and television in our lives have changed significantly in the past few decades, depending more on people's experiences. As these mainstream forms of entertainment are experience goods, it is hard to measure the value and fit of the product before trial. Thus, it is important for the entertainment industry to effectively engage and captivate the target audience by seizing their positions and by anticipating the consumer needs ahead of time.