Source: Dissertation Abstracts International, Volume: 30-12, Section: A, page: 5146. / Thesis (D.B.A.)--The Florida State University, 1969.

Source: Dissertation Abstracts International, Volume: 32-01, Section: A, page: 0064. / Thesis (Ph.D.)--The Florida State University, 1970.

Source: Dissertation Abstracts International, Volume: 32-09, Section: A, page: 4808. / Thesis (D.B.A.)--The Florida State University, 1970.

Source: Dissertation Abstracts International, Volume: 30-10, Section: A, page: 4119. / Thesis (Ph.D.)--The Florida State University, 1969.

This dissertation is a collection of three essays that use real-time data, which reflects information available to market participants at the time forecasts were made, to calculate output gaps and forecast exchange rates. The first study investigates the differences between real-time and ex-post output gap estimates using a newly-constructed international real-time data set over the period from 1973:Q1 to 2007:Q2. We extend the findings in Orphanides and van Norden (2002) for the United States that the use of ex-post information in calculating potential output, not the data revisions themselves, is the major cause of the difference between real-time and ex-post output gap estimates to nine additional OECD countries. The results are robust to the use various detrending methods. By using quasi real-time methods, reliable real-time output gap estimates can be constructed with revised data. / The second study evaluates out-of-sample exchange rate forecasting with Purchasing Power Parity (PPP) and Taylor rule fundamentals for 9 OECD countries vis-a-vis the U.S. dollar over the period from 1973:Q1 to 2009:Q1 at short and long horizons. In contrast with previous work, which reports "forecasts" using revised data, I construct a quarterly real-time dataset that incorporates only the information available to market participants when the forecasts are made. Using bootstrapped out-of-sample test statistics, the exchange rate model with Taylor rule fundamentals performs better at the one-quarter horizon and panel specifications are not able to improve its performance. The PPP model, however, forecasts better at the 16-quarter horizon and its performance increases with the panel framework. The results are in accord with previous research on long-run PPP and estimation of Taylor rule models. / The third paper evaluates out-of-sample exchange rate predictability with Taylor rule fundamentals for 10 OECD countries vis-a-vis the U.S. dollar at short horizons. In contrast with previous research on out-of-sample exchange rate forecasting with Taylor rules using panel data, this study finds evidence of exchange rate predictability. Using real-time quarterly data vintages for OECD countries from 2000:Q1 to 2010:Q2, the exchange rate model with Taylor rule fundamentals significantly outperforms the random walk benchmark at the one-quarter horizon within a panel specification.

This dissertation consists of two manuscripts. In the first manuscript, we investigate the extent to which a life insurer's characteristics and the characteristics of bonds it owns affect the probability of selling downgraded bonds. We find a negative relation between the probability of selling a downgraded bond and a life insurer's risk-based capital (RBC) ratio and a positive relation between the probability of selling a downgraded bond and the indicator of a financially-stressed insurer. We also find life insurers are less likely to sell downgraded bonds remaining in the same rating category as before the downgrade. Apparently, RBC regulation has desired impacts on investment risk taking behavior for life insurers. We also find evidence that mutual insurers and widely-held stock insurers are more likely to sell downgraded bonds than are closely-held stock insurers and the magnitude is stronger when insurers have low levels of capital. Finally, life insurers sold fewer downgraded bonds during the recent financial crisis. Both the relation between the probability of selling downgraded bonds and the capital level, and the relation between the probability of selling downgraded bonds and post-downgrade rating status, weakened during that period. / In the second manuscript, we investigate the extent to which a broad set of a life insurer's firm characteristics, including corporate governance, affects its investment risk taking. Unlike prior research, we use a market-value-based, disaggregated approach to calculate the respective portfolio value-at-risk for 28 mutual insurers and 135 stock insurers over the period from 2007 to 2009. We find that investment risk taking is related to firm size, underwriting risk, leverage, product composition, and board size. Larger insurers take higher investment risk; life insurers that have greater underwriting uncertainty take lower investment risk; insurers with higher leverage take lower investment risk; life insurers that issue more long-term insurance contracts match their obligations with more long-term investments that may have higher volatility in the short run; and insurers that have a larger board take lower investment risk. Finally, we find more than half of the life insurers in our sample reduced their portfolio risk through various techniques during the recent financial crisis.

This dissertation advances the theoretical and empirical understanding of unspanned macro risks in the term structure of interest rates. The first chapter shows that a significant portion of bond risk premia is unspanned by the first three principal components of the bond yield covariance matrix, which together explain over 99.5 percent of the cross-sectional variation in bond yields. Fluctuations in unspanned factors induce movements in expected excess bond returns and expected future short-term interest rates in opposite directions, and are linked to both business cycle and higher-frequency variation in the level of macroeconomic activity. The second chapter examines the plausibility of unspanned macro risks in the term structure of interest rates within a simple New-Keynesian macroeconomic model augmented to allow for time-varying risk prices. In a calibrated version of the model, shocks to the natural rate of output less autonomous spending (so-called 'demand shocks') drive a large percentage of time-variation in bond risk premia, yet the importance of these shocks in explaining contemporaneous yield variation is significantly more subdued. The model demonstrates that within a plausibly calibrated macro-finance model of the term structure there may exist a significant wedge between factors driving cross-sectional variation in the term structure and those driving yield dynamics. The third chapter analyzes the ability of two leading nonlinear consumption-based term structure models to match salient features of bond risk premia. Both an external habit formation model (Campbell and Cochrane 1999) and a long run risk model (Bansal and Yaron 2004) are able to replicate the central finding of Cochrane and Piazzesi (2005): a single return forecasting factor---a tent-shaped linear combination of forward rates---captures all information in the term structure relevant in predicting excess bond returns. Both models fail to replicate an important feature of bond risk premia observed in the data, however. Neither model is able to account for the existence of unspanned macroeconomic factors in the term structure. In both models, yields summarize virtually all information in state variables useful in forecasting excess bond returns.

<p>The idea that integrates parts of this dissertation is that high-frequency data allow for more precise and robust methods for forecasting financial volatility and elucidating the role of volatility in forming asset prices. Thus, the first two chapters compare the performance of model-free forecasts specifically designed to employ high-frequency data with the performance of "classical" forecasts developed for daily data. The final chapter of the dissertation incorporates high-frequency data to verify the predictions of asset pricing models about the risk-return relationships at the very shortest horizons. The results are arranged in the following order.</p><p>Chapter 1 presents the analytical comparison of feasible reduced-form forecasts designed to employ high-frequency data and model-based forecasts updated to use high-frequency data. The prediction errors of both forecast groups are calculated using the ESV-representation of Meddahi (2003), which allows one to generalize the statements from this analysis to a wider class of volatility processes. The results show that reduced-form forecasts outperform model-based forecasts at longer horizons and perform just as well for day-ahead forecasts.</p><p>Chapter 2 expands the conclusions from Chapter 1 to economic measures of forecast performance. These performance measures are constructed within a microeconomic framework that mimics the decision making process of a variance trader who uses volatility forecasts to predict the future profitability of a trade. The results support the theoretical predictions of Chapter 1.</p><p>Chapter 3 is co-authored with Professor Tim Bollerslev and Professor George Tauchen. It extends the "long-run risk" model of Bansal and Yaron(2004) to consistently price volatility risks and to be applicable to high-frequency data. The hypothesis at the outset is that while financial volatility is a long-memory process (it exhibits long-range dependence), its own variance (volatility-of-volatility) is a short memory one. Then the presented model implies that the volatility premium (the measure of the difference between option-implied and expected variances) should be short-memory as well. This insight is confirmed by studying cross correlations of returns and volatility measures. Horizons at which cross correlations are considered are unique for the literature; they start at intra-day values, as short as five minutes.</p> / Dissertation

<p>The first essay of my dissertation investigates how lender-specific shocks impact the strictness of the loan contract that a borrower receives. Exploiting between-bank variation in recent portfolio performance, I find evidence that banks write tighter contracts than their peers after suffering defaults to their own loan portfolios, even when</p><p>defaulting borrowers are in different industries and geographic regions than the current borrower. The effects of recent defaults persist after controlling for bank capitalization, although negative bank equity shocks are also strongly associated with tighter contracts. The evidence is consistent with lenders learning about their own screening technology</p><p>via defaults and adjusting contracts accordingly. Finally, contract tightening is most pronounced for borrowers who are dependent on a relatively small circle of lenders, with each incremental default implying covenant tightening equivalent to that of a ratings downgrade.</p><p>The second essay examines the use of soft information in primary and secondary mortgage markets. Using a large sample of mortgage loan applications, I develop a proxy for soft information collection based on the probability that a mortgage applicant had a face-to-face meeting with a loan officer. I find that the use of soft information increases the probability of loan approval, and conditional on loan approval, reduces the interest rate charged. These loans, however, are less likely to be sold, consistent with the difficulty in credibly communicating soft information. This provides evidence of one mechanism through which securitization may affect screening. Meanwhile, preliminary evidence suggests that screening based on soft information may be valuable to lenders,</p><p>with face-to-face meetings substantially reducing the growth in loan delinquencies during the recent period.</p> / Dissertation

Thesis (Ph.D.)--Indiana University, Kelley School of Business, 2009. / Title from PDF t.p. (viewed on Feb. 8, 2010). Source: Dissertation Abstracts International, Volume: 70-05, Section: A, page: 1738. Adviser: Gregory F. Udell.