Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Social sciences (3)
- Applied sciences (2)
- Systemic risk (2)
- Agricultural Resource Management Survey (ARMS) (1)
- Capital investment (1)
-
- Cash injection (1)
- Classification (1)
- Clearing payments (1)
- Farm capital (1)
- Farm investment (1)
- Farm machinery (1)
- Financial networks (1)
- Hig-dimensional data (1)
- Interbank liabilities (1)
- Lending networks (1)
- Machine learning (1)
- Majorization (1)
- Mitigation strategies (1)
- Monitoring (1)
- Neural networks (1)
- Pattern recognition (1)
- Pseudo panels (1)
- United States (1)
Articles 1 - 7 of 7
Full-Text Articles in Entire DC Network
Systemic Risk In Financial Networks, Peng-Chu Chen
Systemic Risk In Financial Networks, Peng-Chu Chen
Open Access Dissertations
This thesis extends the literature of systemic risk in financial networks in two directions.
First, we develop a majorization-based tool to compare financial networks in terms of systemic losses with a focus on the implications of liability concentration. Specifically, we quantify liability concentration by applying the majorization order to the liability matrix that captures the interconnectedness of banks in a financial network. We develop notions of balancing and unbalancing networks to bring out the qualitatively different implications of liability concentration on the system's loss profile. An empirical analysis of the network formed by the banking sectors of eight representative European …
Optimal Monitoring And Mitigation Of Systemic Risk In Lending Networks, Zhang Li
Optimal Monitoring And Mitigation Of Systemic Risk In Lending Networks, Zhang Li
Open Access Dissertations
This thesis proposes optimal policies to manage systemic risk in financial networks. Given a one-period borrower-lender network in which all debts are due at the same time and have the same seniority, we address the problem of allocating a fixed amount of cash among the nodes to minimize the weighted sum of unpaid liabilities. Assuming all the loan amounts and cash flows are fixed and that there are no bankruptcy costs, we show that this problem is equivalent to a linear program. We develop a duality-based distributed algorithm to solve it which is useful for applications where it is desirable …
U.S. Farm Capital Investment 1996--2013: Differences By Farm Size And Operator Primary Occupation, Sarah Stutzman
U.S. Farm Capital Investment 1996--2013: Differences By Farm Size And Operator Primary Occupation, Sarah Stutzman
Open Access Dissertations
This study analyzes U.S. farm level investment in machinery, equipment and structures between 1996-2013. A synthetic panel is constructed using annual cross-sectional farm level observations from the Agricultural Resource Management Survey (ARMS). Cohorts are formed by grouping farms into similar categories based upon farm production type, region and farm typology. This methodology allows the use of fixed effects to control for cohort specific and time-invariant similarities in investment levels, addresses non-investment in a single period by using cohort average investment rates, and allows links between investment levels and other key determinants across cohorts over time.
Within farm typologies, farms are …
A Pure-Jump Market-Making Model For High-Frequency Trading, Chi Wai Law
A Pure-Jump Market-Making Model For High-Frequency Trading, Chi Wai Law
Open Access Dissertations
We propose a new market-making model which incorporates a number of realistic features relevant for high-frequency trading. In particular, we model the dependency structure of prices and order arrivals with novel self- and cross-exciting point processes. Furthermore, instead of assuming the bid and ask prices can be adjusted continuously by the market maker, we formulate the market maker's decisions as an optimal switching problem. Moreover, the risk of overtrading has been taken into consideration by allowing each order to have different size, and the market maker can make use of market orders, which are treated as impulse control, to get …
Factors That Affect The Outcome Of A General Fund Referendum In Indiana, Andrew Charles Sargent
Factors That Affect The Outcome Of A General Fund Referendum In Indiana, Andrew Charles Sargent
Open Access Dissertations
School finance elections in Indiana were a relatively rare occurrence prior to a series of new legislation enacted in 2008 that, through the imposition of property tax caps statewide, resulted in sweeping reforms to education funding. These new laws coupled with a national recession resulted in many school districts not having the necessary financial resources to maintain programming and personnel consistent with their needs. With this in mind, many of these districts turned to the General Fund referendum as a mechanism to raise more revenue for their districts through an increase in local property taxes as decided by the voters …
Essays On Corporate Bank Loan Contracting, Leann G. Pashnyak
Essays On Corporate Bank Loan Contracting, Leann G. Pashnyak
Open Access Dissertations
This dissertation is comprised of two essays on corporate bank loan contracting. The purpose of the first essay is to investigate the effect of loan's designated purpose on loan agreement contracting terms, as well as to examine whether lenders apply different standards to assess the value of borrower's corporate governance for each type of loan purpose. Using a large sample of private bank loans, the results indicate that both price and non-price loan terms vary significantly by loan purpose. Specifically, the spread yield varies by about 182 basis points (bps) for loans made for different purposes. Further, borrowers of operations …
New Covariance-Based Feature Extraction Methods For Classification And Prediction Of High-Dimensional Data, Mopelola Adediwura Sofolahan
New Covariance-Based Feature Extraction Methods For Classification And Prediction Of High-Dimensional Data, Mopelola Adediwura Sofolahan
Open Access Dissertations
When analyzing high dimensional data sets, it is often necessary to implement feature extraction methods in order to capture relevant discriminating information useful for the purposes of classification and prediction. The relevant information can typically be represented in lower-dimensional feature spaces, and a widely used approach for this is the principal component analysis (PCA) method. PCA efficiently compresses information into lower dimensions; however, studies indicate that it is not optimal for feature extraction especially when dealing with classification problems. Furthermore, for high-dimensional data having limited observations, as is typically the case with remote sensing data and nonstationary data such as …