Browsing by Author "Luo, Shaowen"
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- The Economics of CryptocurrenciesYang, Zichao (Virginia Tech, 2021-04-26)This paper has four chapters. The first chapter serves as an introduction. The second chapter studies the transaction fees in the bitcoin system. The transaction fees and transaction volume in the bitcoin system increase whenever the network is congested and results from a simple VAR show that it is indeed the case. To account for the empirical findings, we build a model where users and miners together determine the transaction fee and transaction volume endogenously. Even though the fluctuating transaction fee mechanism in bitcoin introduces the extra cost of uncertainty to users, a back-of-envelope calculation shows that the cost of using the bitcoin network for transactions is still smaller than the cost of using the current conventional payment system with a fix transaction fee rate. The second chapter studies the time-varying price dispersion among different bitcoin exchanges. We identify the sources of price dispersion using a standard time-varying vector autoregression model with stochastic volatility. The results show that shocks to transaction fees and bitcoin price growth explain on average 20%, and sometimes more than 60%, of the variation of price dispersion. The third chapter studies the relationship between connections and returns in the bitcoin investor network. Using transaction data from the bitcoin blockchain, we reach three conclusions. First, on average, the annualized returns of connected addresses in the network are 20.75% above those of their unconnected peers. Second, returns also differ among those connected addresses. By dividing the connected ad- dresses into ten deciles based on their centrality, we find that addresses in the two most-connected deciles earn higher returns than the other connected addresses. Third, eigenvector centrality is more related than degree centrality to higher returns, implying that quality of connections matters.
- Essays in Game Theory and Forest EconomicsWang, Haoyu (Virginia Tech, 2022-08-18)This dissertation consists of three essays in theoretical and applied microeconomics: the first essay is in cooperative game theory, and the second and third essays relate to forest economics. The first chapter studies a class of cooperative games dubbed ``r-essential games''. Cooperative game theory has proposed different notions of powerful players. For example, big-boss games (Muto et al., 1988) and clan games (Potters et al., 1989) are particular cases of veto games (Bahel, 2016). The first chapter extends these veto games by assuming that there is a given subset of powerful (or essential) players, but only a few (as opposed to all) essential players are required for a coalition to have a positive value. The resulting games, which are called r-essential games, encompass convex games (Shapley, 1971) and veto games. We show that r-essential games have a nonempty core. We give a recursive description of the core. Moreover, it is shown that the core and the bargaining set are equivalent for every r-essential game. An application to networks is provided. The second chapter employs a two-principal, one-agent model to estimate the social cost of fiscal federalism in China's northeast native forests. China's key forested region is located in the northeast and consists of state forest enterprises which manage forest harvesting and reforestation. Deforestation is a major problem there and has resulted in several central government reforms. We develop a framework for assessing the social cost of state forest enterprise deforestation. We first develop a two-principal, one-agent model that fits the federalistic organization of state forests, in that state forest managers make (potentially hidden) decisions under influence of provincial and central government policies. This model is used to quantify the social cost of these hidden actions. We then use panel data from a survey conducted by Peking University to compute social welfare losses and to formally identify the main factors in these costs. A sensitivity analysis shows that, interestingly, command and control through lower harvesting limits and a more accurate monitoring system are more important to lowering social welfare losses than conventional incentives targeting the wages of forest managers. Through regression analysis we also find that the more remote areas with a higher percentage of mature natural forests are the ones that will always have the highest social welfare losses. The third chapter studies the problem of choosing a rotation under uncertain future ecosystem values and timber prices. This problem is nearly as old as the field of forest economics itself. A forest owner faces various uncertainties caused by climate change and market shocks, due to its long-term nature of production and the joint production of interrelated timber and amenity (non-harvesting) benefit streams. The vast literature in stochastic rotation problems simply assumes a known probability distribution for whatever parameter is uncertain, but this type of assumption may lead to misspecification of a rotation decision model if a forest owner has no such information. We study a more relevant question of how to choose rotation ages when there is pure (or Knightian) uncertainty, in that the forest owner does not know distributional features of parameters and further can be averse to this type of information deficit. This chapter is the first to investigate pure uncertainty in amenity benefit streams and is also the first to analytically solve a stochastic rotation problem under pure uncertainty in either amenity streams or market prices. We use robust methods developed in macroeconomics that are particularly suited to forest capital investment problem, but with important differences owing to the nature of forest goods production. The results show that newer models suggesting rotation ages could be longer under volatile parameter distributions do not hold generally when pure uncertainty and forest owner uncertainty aversion is considered. Rather, the earlier literature showing faster or greater harvesting with increases in risk under risk neutrality may actually be a more general result than current literature supposes. In particular, we find that a landowner tends to harvest more when his degree of uncertainty aversion is higher and the model is misspecified by assumption, or when the volatility of an uncertain process is higher. These situations tend to magnify model misspecification costs, especially because the forest manager always assumes the worst case will happen when there is uncertainty. This implies the decision maker is pessimistic in the sense that he or she is always trying to maximize the utility under the worst possible state of nature (the lowest amenity benefit or the lowest timber price). Whether landowners are in fact uncertainty averse and assume the worst case in their decisions remains to be empirically investigated, but our work suggests it is an important question that must be answered.
- Market Sentiments and the Housing MarketsHuang, Yao (Virginia Tech, 2020-04-03)This paper has three chapters. In the first chapter, we develop a measure of housing sentiment for 24 cities in China by parsing through newspaper articles from 2006 to 2017.We find that the sentiment index has strong predictive power for future house prices even after controlling for past price changes and macroeconomic fundamentals. The index leads price movements by nearly 9 months, and it is highly correlated with other survey expectations measures that come with a significant time lag. In the second chapter, we show that short term house price movement is predictable by solely using newspaper and historical price change. In the last chapter, using the sentiment index constructed from newspaper, we got empirical results to show that some people are forward-looking when deciding default and a positive sentiment (anticipated house price appreciation) will lower the Z score of probability of default by 0.028.
- The price adjustment hazard function: Evidence from high inflation periodsLuo, Shaowen; Villar, Daniel (Elsevier, 2021-09-01)The price adjustment hazard function - the probability of a good's price changing as a function of its price misalignment - enables the examination of the relationship between price stickiness and monetary non-neutrality without specifying a micro-founded model, as discussed by Caballero and Engel (1993a, 2007). Using the micro data underlying the U.S. Consumer Price Index going back to the 1970s, we estimate the hazard function relying on empirical patterns from high and low inflation periods. We find that the relation between inflation and higher moments of the price change distribution is particularly informative for the shape of the hazard function. Our estimated hazard function is relatively flat with positive values at zero. It implies weak price selection and a high degree of monetary non-neutrality: about 60% of the degree implied by the Calvo model, and much higher than what menu cost models imply. In addition, our estimated function is asymmetric: price increases are considerably more likely to occur than price decreases of the same magnitude.
- Propagation of shocks in an input-output economy: Evidence from disaggregated pricesLuo, Shaowen; Villar, David (Elsevier, 2023-07-01)Using disaggregated industry-level data, this paper empirically evaluates predictions for the cross-sectional price change distribution made by input-output models with sticky prices. The response of prices to shocks is found to be consistent with the price sensitivities predicted by the input-output model. Moreover, moments of the sectoral price change distribution vary over time in response to the evolution of the network structure. Finally, through a quantitative analysis, demand and supply shocks are disentangled during the pandemic period. Counterfactual analyses show that sectoral supply shocks, aggregate demand shocks and the production network structure contributed significantly to the inflation surge in 2021–2022.
- A Retrospective View of the Phillips Curve and Its Empirical Validity since the 1950sDo, Hoang-Phuong (Virginia Tech, 2021-05-07)Since the 1960s, the Phillips curve has survived various significant changes (Kuhnian paradigm shifts) in macroeconomic theory and generated endless controversies. This dissertation revisits several important, representative papers throughout the curve's four historical, formative periods: Phillips' foundational paper in 1958, the wage determination literature in the 1960s, the expectations-augmented Phillips curve in the 1970s, and the latest New Keynesian iteration. The purpose is to provide a retrospective evaluation of the curve's empirical evidence. In each period, the preeminent role of the theoretical considerations over statistical learning from the data is first explored. To further appraise the trustworthiness of empirical evidence, a few key empirical models are then selected and evaluated for their statistical adequacy, which refers to the validity of the probabilistic assumptions comprising the statistical models. The evaluation results, using the historical (vintage) data in the first three periods and the modern data in the final one, show that nearly all of the models in the appraisal are misspecified - at least one probabilistic assumption is not valid. The statistically adequate models produced from the respecification with the same data suggest new understandings of the main variables' behaviors. The dissertations' findings from the representative papers cast doubt on the traditional narrative of the Phillips curve, which the representative papers play a crucial role in establishing.
- The Skewness of the Price Change Distribution: A New Touchstone for Sticky Price ModelsLuo, Shaowen; Villar, Daniel (Wiley, 2021-02-01)We present a new way of empirically evaluating various sticky price models that are used to assess the degree of monetary nonneutrality. While menu cost models uniformly predict that price change skewness and dispersion fall with inflation, in the Calvo model, both rise. However, the U.S. Consumer Price Index (CPI) data from the late 1970s onward show that skewness does not fall with inflation, while dispersion does. We present a random menu cost model that, with a menu cost distribution that has a strong Calvo flavor, can match the empirical patterns. The model exhibits much more monetary nonneutrality than existing menu cost models.
- Volatility Modeling and Risk Measurement using Statistical Models based on the Multivariate Student's t DistributionBanasaz, Mohammad Mahdi (Virginia Tech, 2022-04-01)An effective risk management program requires reliable risk measurement. Failure to assess inherited risks in mortgage-backed securities in the U.S. market contributed to the financial crisis of 2007–2008, which has prompted government regulators to pay greater attention to controlling risk in banks, investment funds, credit unions, and other financial institutions to prevent bankruptcy and financial crisis in the future. In order to calculate risk in a reliable manner, this thesis has focused on the statistical modeling of expected return and volatility. The primary aim of this study is to propose a framework, based on the probabilistic reduction approach, to reliably quantify market risk using statistical models and historical data. Particular emphasis is placed on the importance of the validity of the probabilistic assumptions in risk measurement by demonstrating how a statistically misspecified model will lead the evaluation of risk astray. The concept of market risk is explained by discussing the narrow definition of risk in a financial context and its evaluation and implications for financial management. After highlighting empirical evidence and discussing the limitations of the ARCH-GARCH-type volatility models using exchange rate and stock market data, we proposed Student's t Autoregressive models to estimate expected return and volatility to measure risk, using Value at Risk (VaR) and Expected Shortfall (ES). The misspecification testing analysis shows that our proposed models can adequately capture the chance regularities in exchange rates and stock indexes data and give a reliable estimation of regression and skedastic functions used in risk measurement. According to empirical findings, the COVID-19 pandemic in the first quarter of 2020 posed an enormous risk to global financial markets. The risk in financial markets returned to levels prior to the COVID-19 pandemic in 2021, after COVID-19 vaccine distribution started in developed countries.