Browsing by Author "John Seater, Committee Member"

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Affine Diffusion Modeling of Commodity Futures Price Term Structure
(2003-07-28) Tian, Yanjun; Nick Piggott, Committee Member; Peter Bloomfield, Committee Member; John Seater, Committee Member; Paul Fackler, Committee Chair
Diffusion modeling of commodity price behavior is important for commodity risk management. This research seeks to improve upon the existing commodity diffusion models by incorporating stochastic volatility and seasonality through the affine diffusion framework. In particular, it evaluates affine diffusion models' performance at modeling commodity futures price term structure. Six affine diffusion models are studied in this research. They are one, two, three-factor Gaussian model and one, two, three-factor stochastic volatility model with a single stochastic volatility factor. Seasonality is modeled by allowing the forcing terms of the instantaneous drift and the instantaneous covariance to be seasonal. Model estimation is done through Q-MLE, for which the state variables are filtered through the Kalman Filter. To build the connection between affine diffusion models and known market regularities, affine state variables are interpreted. Factor interpretations used include the log of the spot price, a spot drift factor, and a spot variance factor. Empirical analysis covers models' performance at fitting and predicting futures price term structures; behavior of the interpretable models; and model stability. Empirical studies are applied to the corn and the unleaded gasoline markets. The following conclusions can be drawn from both markets: 1. For the purpose of modeling futures price dynamics alone, stochastic volatility models have no advantage over Gaussian models; 2. At least two factors are needed to adequately model commodity futures price term structures; the advantage of three-factor models, which is better capturing the curvature of the term structures, become evident under extreme market conditions; 3. State independent seasonality modeling is effective under most market conditions, but under extreme market conditions, seasonality can be mis-represented and it is the source of big measurement errors and prediction errors. 4. Two and three-factor affine diffusion models are able to generate model behavior that is consistent with known market regularities.
Application of Perturbation Methods to Modeling Correlated Defaults in Financial Markets
(2007-03-21) Zhou, Xianwen; Kazufumi Ito, Committee Member; Tao Pang, Committee Member; Jean-Pierre Fouque, Committee Chair; John Seater, Committee Member
In recent years people have seen a rapidly growing market for credit derivatives. Among these traded credit derivatives, a growing interest has been shown on multi-name credit derivatives, whose underlying assets are a pool of defaultable securities. For a multi-name credit derivative, the key is the default dependency structure among the underlying portfolio of reference entities, instead of the individual term structure of default probabilities for each single reference entity as in the case of single-name derivative. So far, however, default dependency modeling is still the most demanding open problem in the pricing of credit derivatives. The research in this dissertation is trying to model the default dependency with aid of perturbation method, which was first proposed by Fouque, Papanicolaou and Sircar (2000) as a powerful tool to pricing options under stochastic volatility. Specifically, after a theoretic result regarding the approximation accuracy of the perturbation method and an application of this method to pricing American options under stochastic volatility by Monte Carlo approach, a multi-dimensional Merton model under stochastic volatility is studied first, and then the multi-dimensional generalization of the first-passage model under stochastic volatility comes next, which is then followed by a copula perturbed from the standard Gaussian copula.
Banking Structure and The Effect of Monetary Policy on Bank Lending.
(2005-08-11) Termos, Ali A; John Seater, Committee Member; John Lapp, Committee Member; Matt Holt, Committee Member; Douglas K. Pearce, Committee Chair
This dissertation examines the role of bank structure on the effectiveness of monetary policy. Using time series data for U.S. banks, I examine the varying effect of monetary policy on bank lending for the period 1976-2003. It is found that as the banking industry gets more concentrated (through mergers and acquisitions), the effect of monetary policy transmission (through open market operations) is being mitigated. That was the result of the deregulation of the banking sector that took place in the first half of the 1990s which led to an unprecedented wave of consolidation in the banking sector. Then I investigate the lending channel evidence at the bank level. That is, how important is the cross-sectional differences in the way that banks with varying characteristics respond to policy shocks. Three bank characteristics are highlighted: bank size, liquidity and capitalization. It is found that large, more liquid, and well capitalized banks are more impervious to changes in monetary policy than other banks. Real estate loans, agriculture, commercial and industrial (C&I), and consumer loans are analyzed. The size of the bank is found to be most crucial for real estate lending, where small banks are much more sensitive to changes in the federal funds rate compared to large banks. The effect is comparatively less pronounced for C&I and consumer lending and largely disappears when it comes to agriculture lending. Finally, the question of monetary policy asymmetry is examined. As expected, monetary policy has more effect on bank lending when it tightens than when it eases interest rates. This is found to be the case for all types of loans except for real estate loans, where a decline of FFR entices more real estate lending than a rise.
Essays on the Application and Computation of Real Options
(2009-06-22) Marten, Alex Lennart; Roger von Haefen, Committee Member; Paul Fackler, Committee Chair; Denis Pelletier, Committee Member; John Seater, Committee Member
This dissertation presents a series of three essays that examine applications and computational issues associated with the use of stochastic optimal control modeling in the field of economics. In the first essay we examine the problem of valuing brownfield remediation and redevelopment projects amid regulatory and market uncertainty. A real options framework is developed to model the dynamic behavior of developers working with environmentally contaminated land in an investment environment with stochastic real estate prices and an uncertain entitlement process. In a case study of an actual brownfield regeneration project we examine the impact of entitlement risk on the value of the site and optimal developer behavior. The second essay presents a numerical method for solving optimal switching models combined with a stochastic control. For this class of hybrid control problems the value function and the optimal control policy are the solution to a Hamilton-Jacobi-Bellman quasi-variational inequality. We present a technique whereby approximating the value function using projection methods the Hamilton-Jacobi-Bellman quasi-variational inequality may be recast as extended vertical non-linear complementarity problem that may be solved using Newton's method. In the third essay we present a new method for estimating the parameters of stochastic differential equations using low observation frequency data. The technique utilizes a quasi-maximum likelihood framework with the assumption of a Gaussian conditional transition density for the process. In order to reduce the error associated with the normality assumption sub-intervals are incorporated and integrated out using the Chapman-Kolmogorov equation and multi-dimensional Gauss Hermite quadrature. Further improvements are made through the use of Richardson extrapolation and higher order approximations for the conditional mean and variance of the process, resulting in an algorithm that may easily produce third and fourth order approximations for the conditional transition density.
Object-Image Correspondence of Under Projections.
(2010-04-29) Burdis, Joseph; Irina Kogan, Committee Chair; John Seater, Committee Member; Kailash Misra, Committee Member; Ernest Stitzinger, Committee Member
Statistical Issues in Coherent Risk Management
(2004-11-22) Lu, Na; Albert S. "Pete" Kyle, Committee Member; Sastry Pantula, Committee Member; Peter Bloomfield, Committee Chair; John Seater, Committee Member; Marc Genton, Committee Member
Measuring risk is a crucial aspect of the portfolio optimization problem in finance, and of capital adequacy assessment in risk management. Expected Shortfall (ES) has been proposed as a coherent risk measure, by contrast with Value-at-Risk (VaR) and the standard-deviation-type of measures. Based on a coherent risk measure, for instance ES, we can discuss a coherent capital allocation for the purpose of internal risk management and performance measure, if ES is used for economic capital held by financial firms as a cushion to absorb the unexpected losses. Properly allocating risk capital down to the business level is important for the purpose of risk management and portfolio performance measurement. Even if there is a doubt about the reason for allocating ES, instead of VaR, the statistical properties of the statistic, marginal ES, from the proposed coherent allocation rule, are still of interest, because it is exactly the sensitivity of the target portfolio's ES. The idea of a coherent capital allocation rule by using a cost sharing rule, the Aumann-Shapley value in game theory, proposed by Denault (2001), happens to result in the same formula as proposed by Tasche (2000), who independently develops the "suitable" allocation rule based on the discussion of risk-adjusted returns. The fact, that two aspects of the concerns are satisfied by the same allocation formula, brings two fields together in an integrated way, so that a systematic risk management in a banking system seems very promising. Fundamental statistical issues arise in several places in a coherent risk management system. Primary interests will be, and are always, in modeling the profit/loss (P/L) distributions. Statistical modeling is receiving more and more attention currently, as well as economic modeling. For our purpose, we place more emphasis on the estimation and inference of ES and allocation statistics (marginal contribution of ES) under different situations. We also modify the back-testing rules based on ES. We propose a collection of weighted test statistics aiming at detecting the underestimated ES. Asymptotic properties of the test statistics are offered. The power of the tests in the context of an exponential family and the local alternatives is provided and the optimal weighting scheme is discussed.
Three Essays on Trend Analysis and Misspecification in Structural Econometric Models
(2003-09-02) Doorn, David John; David Flath, Committee Member; David Dickey, Committee Member; Alastair Hall, Committee Chair; John Seater, Committee Member
The purpose of this research has been to look into several econometric issues of concern to researchers doing applied work in macroeconomics. The first essay looks at Bureau of Economic Analysis data on inventories and sales of finished goods often used in studies of inventory behavior. Applying recently developed methods, the series are rigorously tested to determine their stationarity properties. Results indicate that neither first differencing nor linearly detrending the data is appropriate. For most series a trend function with one or more breaks offers a better fit and also generates stationarity. The results are used to determine the impact on estimation in a simple production-smoothing model of inventory behavior. The impact of different trend specifications on univariate forecasting of inventories is also considered. The second essay considers an alternative method of detrending time series data — the Hodrick-Prescott (HP) filter. Previous research has shown that HP filtering can have serious adverse consequences when used to analyze co-movements between data series at business cycle frequencies. Despite this, the filter has also been used to induce stationarity in a data series prior to estimation of structural econometric models. Little work has been done in analyzing the possible effects this may have on parameter estimates from such models. A simulation study is conducted to assess the impact of HP filtering on parameter estimation and a comparison is made to other detrending methods. It is shown that the HP filter induces bias in the parameter estimates and also increases the root mean squared error of the estimates from the simulations. In addition, there is some adverse impact on the size of certain test statistics. The final essay looks at the impact of misspecification on estimation results from a structural econometric model when using a Generalized Method of Moments estimator. Simulated data consistent with a particular specification of the model is used to estimate two misspecified versions. It is shown that misspecification causes the probability limit of the estimator to differ from the true value. It is further shown that a popular specification test performs poorly in detecting the misspecification. An alternative method of model selection is shown to perform far better. Finally, because the use of conventional asymptotic theory is not appropriate in misspecified models, a recently proposed alternative asymptotic theory is tested to determine whether there is improvement in the ability to perform inference on the parameters from misspecified models.