An Econometric Analysis of 3G Auction Spectrum Valuations

Scarce radio spectrum is assigned to mobile network operators (MNOs) by national regulatory authorities (NRAs). Spectrum is usually assigned by beauty contest or an auction. The process requires that winners make a payment to the government. MNOs seek scarce spectrum to enable the provision of wireless services for profit. While MNOs are imperfectly aware of their costs, NRAs rely solely on MNOs for this information. As such, NRAs set spectrum assignment conditions (including minimum bid price) largely ignorant of MNO operating conditions. This study examines the performance of 3G auction outcomes in terms of the prices paid by winners via an econometric analysis of a unique sample of national 3G spectrum auctions. These winning bids depend on national and mobile market conditions, spectrum package attributes, license process, and post-award operator requirements. Finally, model estimation accounts for the censored nature of these data.Mobile telephone markets, spectrum allocation, spectrum bid price

Technology and Management for Sustainable Buildings and Infrastructures

A total of 30 articles have been published in this special issue, and it consists of 27 research papers, 2 technical notes, and 1 review paper. A total of 104 authors from 9 countries including Korea, Spain, Taiwan, USA, Finland, China, Slovenia, the Netherlands, and Germany participated in writing and submitting very excellent papers that were finally published after the review process had been conducted according to very strict standards. Among the published papers, 13 papers directly addressed words such as sustainable, life cycle assessment (LCA) and CO2, and 17 papers indirectly dealt with energy and CO2 reduction effects. Among the published papers, there are 6 papers dealing with construction technology, but a majority, 24 papers deal with management techniques. The authors of the published papers used various analysis techniques to obtain the suggested solutions for each topic. Listed by key techniques, various techniques such as Analytic Hierarchy Process (AHP), the Taguchi method, machine learning including Artificial Neural Networks (ANNs), Life Cycle Assessment (LCA), regression analysis, Strength–Weakness–Opportunity–Threat (SWOT), system dynamics, simulation and modeling, Building Information Model (BIM) with schedule, and graph and data analysis after experiments and observations are identified

Order Aggressiveness and Quantity: How Are They Determined in a Limit Order Market?

Dealers trading in a limit order market must choose both the order aggressiveness and the quantity for their orders. We empirically investigate how dealers jointly make these decisions in the foreign exchange market using a unique simultaneous equations model. The model uses an ordered probit model to account for the discrete nature of order aggressiveness and a censored regression model to capture the clustering of orders placed at the smallest available quantity, $1 million. We find evidence of a clear trade-off between order aggressiveness and quantity: more aggressive orders tend to be smaller in size. The increased competition (demand) suggested by increased depth on the same (opposite) side of the market leads to less (more) aggressive orders in smaller (larger) size. This holds for the depths at both the best and off-best prices, even though off-best depths are not observable to dealers.Exchange rates; Financial markets

Approximately optimal trade execution strategies under fast mean-reversion

In a fixed time horizon, appropriately executing a large amount of a particular asset -- meaning a considerable portion of the volume traded within this frame -- is challenging. Especially for illiquid or even highly liquid but also highly volatile ones, the role of "market quality" is quite relevant in properly designing execution strategies. Here, we model it by considering uncertain volatility and liquidity; hence, moments of high or low price impact and risk vary randomly throughout the trading period. We work under the central assumption: although there are these uncertain variations, we assume they occur in a fast mean-reverting fashion. We thus employ singular perturbation arguments to study approximations to the optimal strategies in this framework. By using high-frequency data, we provide estimation methods for our model in face of microstructure noise, as well as numerically assess all of our results

Updating the Bridge Construction Cost Database

Adopting a comprehensive suite of methods to track, analyze, and maintain data on bridge construction costs can help state transportation agencies identify and implement strategies to mitigate the influence of factors which escalate project costs. This report discusses how the Kentucky Transportation Cabinet (KYTC) should approach updating, maintaining, and analyzing its bridge construction cost data. Based on a review of practices introduced at other agencies and interviews with public and private industry stakeholders, the report catalogues practical strategies for improving estimating procedures and tracking cost data as well as the most important cost drivers of bridge construction. Analysis of KYTC data on average unit bid prices for eight key bid items on bridge projects found that prices went up for every item between 2015 and 2021. Steel reinforcement and epoxy coated steel reinforcement displayed the most consistent linear upward trend, while greater variability was noticeable in prices for Class A and AA concrete and foundation preparation. This analysis substantiated observations by interviewees that contractors submit higher bid prices when they perceive greater risk associated with a work item. Recommendations for process improvements at the Cabinet focus on agencywide rollout of AASHTOWare Estimation, conducting post-construction reviews, establishing contract durations that reasonably accommodate the completion of all work, and performing more in-depth geotechnical investigations

Two Essays on Liquidity Suppliers\u27 Gross Profits.

The purpose of this dissertation is to examine the strategic behavior of the specialist proposed by Glosten (1989) and its implications for price volatility and market liquidity. The extant literature suggests that the bid-ask spread is responsible, at least in part, for the greater volatility and more negative autocorrelation at the open than at the close. We find that these phenomena are not related to the bid-ask spread, but related to pricing errors quoted by the specialist or by limit order traders around the open. We use George, Kaul, and Nimalendran\u27s (1991) model, which is less biased than Roll\u27s (1984) model, to estimate the implied spread. The results show that, on average, the implied spread earned by liquidity suppliers is less at the open than at the close. These results refute Stoll and Whaley\u27s (1990) contention that the specialist exploits his monopoly position and earns a higher profit at the opening call. Glosten (1989) posits that when information asymmetry is high, the specialist may reduce profits or even realize losses to induce informed traders to trade and to release their information. This reduces the adverse selection problem and makes subsequent trades more profitable. This hypothesis of averaging profits through time implies that the pattern in the specialist\u27s gross profits is inversely related to the pattern in information asymmetry. Since information asymmetry has been found to be higher at the beginning of the trading day, we predict that gross profits earned by the specialist will be lower at the beginning than during the rest of the trading day. Empirical results are consistent with this hypothesis

System Support for Managing Risk in Cloud Computing Platforms

Cloud platforms sell computing to applications for a price. However, by precisely defining and controlling the service-level characteristics of cloud servers, they expose applications to a number of implicit risks throughout the application’s lifecycle. For example, user’s request for a server may be denied, leading to rejection risk; an allocated resource may be withdrawn, resulting in revocation risk; an acquired cloud server’s price may rise relative to others, causing price risk; a cloud server’s performance may vary due to external factors, triggering valuation risk. Though these risks are implicit, the costs they bear on the applications are not. While some risks exist in all Infrastructure-as-a-Service offerings, they are most pronounced in an emerging category called transient cloud servers. Since transient servers are carved out of instantaneous idle cloud capacity, they exhibit two distinct features: (i) revocations that are intentional, frequent and come with advanced warning, and (ii) prices that are low in average but vary across time and location. Thus, despite enabling inexpensive access to at-scale computing, transient cloud servers expose applications to risks, the scale of which were unseen in the past platforms. Unfortunately, the current generation system software are not designed to handle these risks, which in turn results in inconsistent performances, unexpected failures, missed savings, and slower adoption. In this dissertation, we elevate risk management to a first-class system design principle. Our goal is to identify the risks, quantify their costs, and explicitly manage them for applications deployed on cloud platforms. Towards that goal, we adapt and extend concepts from finance and economics to propose a new system design approach called financializing cloud computing. By treating cloud resources as investments, and by quantifying the cost of their risks, financialization enables system software to manage the risk-reward trade-offs, explicitly and autonomously. We demonstrate the utility of our approach via four contributions: (i) mitigating revocation risk with insurance policy, (ii) reducing price risk through active trading, (iii) eliminating uncertainty risk by index tracking, and (iv) minimizing server’s valuation risk via asset pricing. We conclude by observing that diversity and asymmetry in the creation and consumption of cloud compute resources is on the rise, and that financialization can be effectively employed to manage its complexity and risks

Classifying the Level of Bid Price Volatility Based on Machine Learning with Parameters from Bid Documents as Risk Factors

The purpose of this study is to classify the bid price volatility level with machine learning and parameters from bid documents as risk factors. To this end, we studied project-oriented risk factors affecting the bid price and pre-bid clarification document as the uncertainty of bid documents through preliminary research. The authors collected Caltrans’s bid summary and pre-bid clarification document from 2011–2018 as data samples. To train the classification model, the data were preprocessed to create a final dataset of 269 projects consisting of input and output parameters. The projects in which the bid inquiries were not resolved in the pre-bid clarification had higher bid averages and bid ranges than the risk-resolved projects. Besides this, regarding the two classification models with neural network (NN) algorithms, Model 2, which included the uncertainty in the bid documents as a parameter, predicted the bid average risk and bid range risk more accurately (52.5% and 72.5%, respectively) than Model 1 (26.4% and 23.3%, respectively). The accuracy of Model 2 was verified with 40 verification test datasets