Browsing by Subject "predictability"
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Item Credit Conditions and Stock Return Predictability(2012-10-19) Park, HeungjuThis dissertation examines stock return predictability with aggregate credit conditions. The aggregate credit conditions are empirically measured by credit standards (Standards) derived from the Federal Reserve Board's Senior Loan Officer Opinion Survey on Bank Lending Practices. Using Standards, this study investigates whether the aggregate credit conditions predict the expected returns and volatility of the stock market. The first essay, "Credit Conditions and Expected Stock Returns," analyzes the predictability of U.S. aggregate stock returns using a measure of credit conditions, Standards. The analysis reveals that Standards is a strong predictor of stock returns at a business cycle frequency, especially in the post-1990 data period. Empirically the essay demonstrates that a tightening of Standards predicts lower future stock returns. Standards performs well both in-sample and out-of-sample and is robust to a host of consistency checks including a small sample analysis. The second essay, "Credit Conditions and Stock Return Volatility," examines the role played by credit conditions in predicting aggregate stock market return volatility. The essay employs a measure of credit conditions, Standards in the stock return volatility prediction. Using the level and the log of realized volatility as the estimator of the stock return volatility, this study finds that Standards is a strong predictor of U.S. stock return volatility. Overall, the forecasting power of Standards is strongest during tightening credit periods.Item The Dynamics and Predictability of Tropical Cyclones(2010-01-15) Sippel, Jason A.Through methodology unique for tropical cyclones in peer-reviewed literature, this study explores how the dynamics of moist convection affects the predictability of tropical cyclogenesis. Mesoscale models are used to perform short-range ensemble forecasts of a non-developing disturbance in 2004 and Hurricane Humberto in 2007; both of these cases were highly unpredictable. Taking advantage of discrepancies between ensemble members in short-range ensemble forecasts, statistical correlation is used to pinpoint sources of error in forecasts of tropical cyclone formation and intensification. Despite significant differences in methodology, storm environment and development, it is found in both situations that high convective instability (CAPE) and mid-level moisture are two of the most important factors for genesis. In the gulf low, differences in CAPE are related to variance in quasi-geostrophic lift, and in Humberto the differences are related to the degree of interaction between the cyclone and a nearby front. Regardless of the source of CAPE variance, higher CAPE and mid-level moisture combine to yield more active initial convection and more numerous and strong vortical hot towers (VHTs), which incrementally contribute to a stronger vortex. In both cases, strength differences between ensemble members are further amplified by differences in convection that are related to oceanic heat fluxes. Eventually the WISHE mechanism results in even larger ensemble spread, and in the case of Humberto, uncertainty related to the time of landfall drives spread even higher. It is also shown that initial condition differences much smaller than current analysis error can ultimately control whether or not a tropical cyclone forms. Furthermore, even smaller differences govern how the initial vortex is built. Differences in maximum winds and/or vorticity vary nonlinearly with initial condition differences and depend on the timing and intensity of small mesoscale features such as VHTs and cold pools. Finally, the strong sensitivity to initial condition differences in both cases exemplifies the inherent uncertainties in hurricane intensity prediction. This study illustrates the need for implementing advanced data analysis schemes and ensemble prediction systems to provide more accurate and event-dependent probabilistic forecasts.Item Understanding seasonal climate predictability in the Atlantic sector(Texas A&M University, 2005-02-17) Barreiro, MarceloThis dissertation aims at understanding ocean-atmosphere interactions in the Atlantic basin, and how this coupling may lead to increased climate predictability on seasonal-to-interannual time scales. Two regions are studied: the South Atlantic convergence zone (SACZ), and the tropical Atlantic. We studied the SACZ during austral summer and separated its variability into forced and internal components. This was done by applying a signal-to-noise optimization procedure to an ensemble of integrations of the NCAR Community Climate Model (CCM3)forced with observed Sea Surface Temperature (SST). The analysis yielded two dominant responses: (1) a response to local Atlantic SST consisting of a dipole-like structure in precipitation close to the coast of South America; (2) a response to Pacific SST which manifests mainly in the upper-level circulation consisting of a northeastward shift of the SACZ during El Ni?o events. The land portion of the SACZ was found to be primarily dominated by internal variability, thereby having limited potential predictability at seasonal time scales. We studied two aspects of tropical Atlantic Variability (TAV). First, we investigated the effect of extratropical variability on the gradient mode. We found that the intensive Southern Hemisphere (SH) winter variability can play a pre-conditioning role in the onset of the interhemispheric anomalies in the deep tropics during boreal spring. This SH influence on TAV is contrasted with its northern counterpart that primarily comes from the North Atlantic Oscillation during boreal winter. Second, we explored the importance of ocean dynamics in the predictability of TAV. We used the CCM3 coupled to a slab ocean as a tier-one prediction system. The ocean processes are included as a statistical correction that parameterizes the heat transport due to anomalous linear ocean dynamics. The role of ocean dynamics was studied by comparing prediction runs with and without the correction. We showed that in the corrected region the corrected model outperforms the non-corrected one particularly at long lead times. Furthermore, when the model was initialized with global initial conditions, tropical Atlantic SST anomalies are skillfully predicted for lead times of up to six months. As result, the corrected model showed high skill in predicting rainfall in the ITCZ during boreal spring.