Essays on Risk Measurement and Modeling in Macroeconomics and Finance

This dissertation consists of four essays that focus on the measurement and economic analysis of key risk factors behind macroeconomic and financial variables using state-space models. Chapters 2, 3, 4, and 5 develop and implement estimation approaches that can handle nonlinear linkages of economic forces and tackle issues when data are missing or contaminated by errors. Chapter 2 estimates an equilibrium term structure model that includes real and nominal uncertainty in particular that allows for changes in the responsiveness of the Federal Reserve to inflation fluctuations. These uncertainty, particularly those concerning monetary policy action are considered potential sources of risk variations that can explain several features in the U.S. government bond market including the upward sloping yield curve. Chapter 3, co-authored with Frank Schorfheide and Amir Yaron, develops a nonlinear state-space model to estimate predictable mean and volatility components in monthly consumption growth using a mixed-frequency data and accounting for serially-correlated measurement errors. We provide a methodological contribution that allow to maximize the span of the estimation sample to recover the predictable component and at the same time use high-frequency data to efficiently identify the volatility processes. The estimation provides strong evidence for predictable mean and volatility components in consumption growth. We show that the model can go a long way in explaining several well known asset pricing facts of the data. Chapter 4, co-authored with Boragan Aruoba, Francis Diebold, Jeremy Nalewaik, and Frank Schorfheide, considers the fundamental question of GDP estimation, focusing on the U.S., and provides estimates superior to the ubiquitous expenditure-side series by applying optimal signal-extraction techniques to the noisy expenditure-side and income-side GDP estimates. The quarter-by-quarter values of the new measure often differ noticeably from those of the traditional measures, and dynamic properties differ as well, indicating that the persistence of aggregate output dynamics is stronger than previously thought. Chapter 5, co-authored with Frank Schorfheide, develops the idea of using mixed-frequency data in state-space form. We show that adding monthly observations to a quarterly VAR, which then is estimated with Bayesian methods under a Minnesota-style prior, substantially improves its forecasting performance

Improving GDP Measurement: A Forecast Combination Perspective

Two often-divergent U.S. GDP estimates are available, a widely-used expenditure side version, GDPE, and a much less widely-used income-side version, GDPI . We propose and explore a "forecast combination" approach to combining them. We then put the theory to work, producing a superior combined estimate of GDP growth for the U.S., GDPC. We compare GDPC to GDPE and GDPI, with particular attention to behavior over the business cycle. We discuss several variations and extensions.National Income and Product Accounts, Output, Expenditure, Economic Activity, Business Cycle, Recession

A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, we investigate various deep learning models for the detection and localization of the tumor in MRI. A novel two-tier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second tire. Furthermore, in this paper, we introduce a well-annotated dataset comprised of tumor and normal images. The experimental results demonstrate the effectiveness of the proposed framework by achieving 97% accuracy using GoogLeNet on the proposed dataset for classification and 83% for localization tasks after fine-tuning the pre-trained you only look once (YOLO) v3 model

Gypsum-Dependent Effect of NaCl on Strength Enhancement of CaO-Activated Slag Binders

This study explores the combined effect of NaCl and gypsum on the strength of the CaO-activated ground-granulated blast furnace slag (GGBFS) binder system. In the CaO-activated GGBFS system, the incorporation of NaCl without gypsum did not improve the strength of the system. However, with the presence of gypsum, the use of NaCl yielded significantly greater strength than the use of either gypsum or NaCl alone. The presence of NaCl largely increases the solubility of gypsum in a solution, leading to a higher concentration of sulfate ions, which is essential for generating more and faster formations of ettringite in a fresh mixture of paste. The significant strength enhancement of gypsum was likely due to the accelerated and increased formation of ettringite, accompanied by more efficient filling of pores in the system

Use of Coal Bottom Ash and CaO-CaCl2-Activated GGBFS Binder in the Manufacturing of Artificial Fine Aggregates through Cold-Bonded Pelletization

This study investigated the use of coal bottom ash (bottom ash) and CaO-CaCl2-activated ground granulated blast furnace slag (GGBFS) binder in the manufacturing of artificial fine aggregates using cold-bonded pelletization. Mixture samples were prepared with varying added contents of bottom ash of varying added contents of bottom ash relative to the weight of the cementless binder (= GGBFS + quicklime (CaO) + calcium chloride (CaCl2)). In the system, the added bottom ash was not simply an inert filler but was dissolved at an early stage. As the ionic concentrations of Ca and Si increased due to dissolved bottom ash, calcium silicate hydrate (C-S-H) formed both earlier and at higher levels, which increased the strength of the earlier stages. However, the added bottom ash did not affect the total quantities of main reaction products, C-S-H and hydrocalumite, in later phases (e.g., 28 days), but simply accelerated the binder reaction until it had occurred for 14 days. After considering both the mechanical strength and the pelletizing formability of all the mixtures, the proportion with 40 relative weight of bottom ash was selected for the manufacturing of pilot samples of aggregates. The produced fine aggregates had a water absorption rate of 9.83% and demonstrated a much smaller amount of heavy metal leaching than the raw bottom ash

Simulation study of dose enhancement in a cell due to nearby carbon and oxygen in particle radiotherapy

The aim of this study is to investigate the dose-deposition enhancement by alpha-particle irradiation in a cellular model using carbon and oxygen chemical compositions.A simulation study was performed to study dose enhancement due to carbon and oxygen for a human cell where Geant4 code used for the alpha-particle irradiation to the cellular phantom. The characteristic of dose enhancement in the nucleus and cytoplasm by the alpha-particle radiation was investigated based on concentrations of the carbon and oxygen compositions and was compared with those by gold and gadolinium.The results show that both the carbon and oxygen-induced dose enhancement was found to be more effective than those of gold and gadolinium. We found that the dose-enhancement effect was more dominant in the nucleus than in the cytoplasm if carbon or oxygen is uniformly distributed in a whole cell. In the condition that the added chemical composition was inserted only into the cytoplasm, the effect of the dose enhancement in nucleus becomes weak.We showed that high-stopping-power materials offer a more effective dose-enhancement efficacy and suggest that the carbon nanotubes and oxygenation are promising candidates for dose utilization as dose enhancement tools in particle therapy.Comment: 19 pages, 6 figures, 4 tables. presented to 7th KOREA-JAPAN Joint Meeting on Medical Physics (2014.09.25) accepted to Journal of the Korean Physical Society (2015.03.10

Cytotoxic and atiangiogenetic xanthones inhibiting tumor proliferation and metastasis from Garcinia xipshuanbannaensis

Eight prenylated xanthones including four new analogues were extracted and purified from the leaves of Garcinia xipshuanbannaensis. Multiple techniques including UV, 1D and 2D NMR, and HRESIMS were used to determine the structures of the isolated xanthones. These xanthones were evaluated for their cytotoxicity toward human cancer cells, and compound 4 exhibited activity against HeLa cells. A cytotoxic mechanism examination revealed the active compound induced cell apoptosis by arresting the cell cycle, increasing the levels of ROS, and inhibiting the expression of p-STAT3 in HeLa cells. In in vivo zebrafish experiments, compound 4 was found to block tumor proliferation and migration and have antiangiogenetic activity, and thus seems worthy of further laboratory evaluation.The National Natural Science Foundation of China and the Natural Science Foundation of Tianjin, China.https://pubs.acs.org/journal/jnprdf2022-04-27hj2021Plant Production and Soil Scienc

Anti-Inflammatory ent-Kaurane Diterpenoids from Isodon serra

Ten new ent-kaurane diterpenoids, including two pairs of epimers 1/2 and 4/5 and a 6,7-seco-ent-kauranoid 10, were obtained from the aerial parts of Isodon serra. The structures of the new compounds were confirmed by extensive spectroscopic methods and electronic circular dichroism (ECD) data analysis. An anti-inflammatory assay was applied to evaluate their nitric oxide (NO) inhibitory activities by using LPS-stimulated BV-2 cells. Compounds 1 and 9 exhibited notable NO production inhibition with IC50 values of 15.6 and 7.3 μM, respectively. Moreover, the interactions of some bioactive diterpenoids with inducible nitric oxide synthase (iNOS) were explored by employing molecular docking studies.https://pubs.acs.org/journal/jnprdf2021-09-29hj2021Plant Production and Soil Scienc

Bond Market Exposures to Macroeconomic and Monetary Policy Risks

Parallel Sessions D: Monetary Polic