Browsing by Author "Denis Pelletier, Committee Member"

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Assessing the Effects of Variability in Interest Rate Derivative Pricing
(2007-11-22) Crotty, Michael Thomas; Denis Pelletier, Committee Member; Sujit Ghosh, Committee Member; David Dickey, Committee Member; Peter Bloomfield, Committee Chair
Asymmetric Responses of Nominal Rates, TIPS Rates, Break-Even Inflation Rates, and the Stock-Bond Correlation to Macroeconomic Announcements
(2010-03-29) Darwin, Robert William; Michael Brandt, Committee Member; Walter Thurman, Committee Member; Denis Pelletier, Committee Member; Douglas Pearce, Committee Chair
Utilizing daily instantaneous forward rates of nominal and inflation-indexed bonds as well as realizations of stock and bond index returns, I examine the informational content of a broad set of macroeconomic announcements. I find evidence that, with a few exceptions, price variables mainly move break-even inflation rates, while real variables move TIPS rates and/or break-even inflation rates. An analysis of movements in the stock-bond correlation finds that, with some exceptions, expected future interest rates are the important component of the informational content of expansionary announcements to production variables and employment variables. In recessions, I find evidence that expectations of future economic growth or an equity risk premium are the important news conveyed by shocks to some production and employment variables, again with some exceptions. Similarly, for price variables I find evidence that in expansions shocks either proxy for future economic activity or provide information about expected future nominal rates which investors mistakenly use to value equities rather than expected real rates. In recessions (at least for core PPI) some evidence points to the news content referencing future economic growth or the equity risk premium. Consistent with previous results in the literature, results on movements in the stock-bond correlation agree with rising correlations in expansions and falling correlations in recessions. Additionally, in looking at monetary policy shocks to the federal funds target rate I notice that expectations of growth or the equity risk premium are embedded in shocks that `go against the grain' of the expected path given an economic state (negative expansionary and positive recessionary shocks). Formal tests for state and sign asymmetries in the magnitudes of responses to macroeconomic shocks generally yield sparse significant results, though for production variables mainly indicate greater effects of expansionary over recessionary and negative over positive shocks, with some exceptions. Finally, state asymmetry in the response of TIPS rates to monetary policy announcements indicates long-run expansionary momentum and long-run recessionary reversal in monetary policy.
Canonical Correlations and Instrument Selection in Econometrics
(2005-05-31) Jana, Kalidas; Denis Pelletier, Committee Member; Alastair R. Hall, Committee Chair; Peter Bloomfield, Committee Member; David A. Dickey, Committee Member
This dissertation relates to three recent methods of instrument selection in econometrics, namely, the Canonical Correlations Information Criterion (CCIC), the Relevant Moments Selection Criterion (RMSC) and the approximate Mean Square Error Criterion (MSE). Usual canonical correlations measure the degree of association between two random vectors and provide the basis for construction of the CCIC. A new kind of canonical correlations called Long Run Canonical Correlations (LRCC) has recently emerged in econometrics and provides the basis for construction of the RMSC. Although the concept of LRCC has emerged in the literature, methods of their estimation and inference have not been developed. Developing these methods constitutes the first chapter of the dissertation. In addition, this chapter illustrates the usefulness of LRCC beyond their usefulness in relevant moments selection for GMM models in dynamic nonlinear settings. In particular, it demonstrates how LRCC can be used to develop econometric tests that play a role in (i) structural stability testing, and (ii) exogeneity testing of regressors in time series models where the regressors are nonstationary. Although the properties of each of the above three methods of instrument selection have been explored by their proponents, there have been no comparative studies of these methods to date. The second chapter of this dissertation fills that gap. The final and third chapter extends the statistical theory of the CCIC by considering the case where the number of instruments tends to infinity at an appropriate rate as the sample size tends to infinity. The importance of this extension stems from the fact that this can lead to a further gain in efficiency of the estimator by systematically capturing all relevant instruments from the growing candidate set.
Entropy Based Moment Selection in Generalized Method of Moments
(2005-06-28) Shin, Changmock; Denis Pelletier, Committee Member; Peter Bloomfield, Committee Member; Atsushi Inoue, Committee Member; Alastair R. Hall, Committee Chair
GMM provides a computationally convenient estimation method and the resulting estimator can be shown to be consistent and asymptotically normal under the fairly moderate regularity conditions. It is widely known that the information content in the population moment condition has impacts on the quality of the asymptotic approximation to finite sample behavior. This dissertation focuses on a moment selection procedure that leads us to choose relevant (asymptotically efficient and non-redundant) moment conditions in the presence of weak identification. The contributions of this dissertation can be characterized as follows: in the framework of linear model, (i) the concept of nearly redundant moment conditions is introduced and the connection between near redundancy and weak identification is explored; (ii) performance of RMSC(c) is evaluated when weak identification is a possibility but the parameter vector to be estimated is not weakly identified by the candidate set of moment conditions; (iii) performance of RMSC(c) is also evaluated when the parameter vector is weakly identified by the candidate set; (iv) a combined strategy of Stock and Yogo's (2002) test for weak identification and RMSC(c) is introduced and evaluated; (v) (i) and (ii) are extended to allow for nonlinear dynamic models. The subsequent simulation results support the analytical findings: when only a part of instruments in the set of possible candidates for instruments are relevant and the others are redundant given all or some of the relevant ones, RMSC(c) chooses all the relevant instruments with high probabilities and improves the quality of the post-selection inferences; when the candidates are in order of their importance, a combined strategy of Stock and Yogo's (2002) pretest and RMSC(c) improves the post-selection inferences, however it tends to select parsimonious models; when all the possible candidates are equally important, it seems that RMSC(c) does not provide any merits. However, in the last case, asymptotic efficiency and non-redundancy can be achieved by basing the estimation and inference on all the possible candidates.
Essays on Environmental and Computational Economics
(2008-12-05) Balikcioglu, Metin; Jeffrey S. Scroggs, Committee Member; Denis Pelletier, Committee Member; Paul L. Fackler, Committee Chair; John J. Seater, Committee Member
The study consists of three separate essays. The first essay reassesses and extends the papers by Pindyck (2000, 2002) which analyze the effects of uncertainty and irreversibility on the timing of emissions reduction policy. It is shown that proposed solutions for some of the optimal stopping problems introduced in these papers are incorrect. Correct solutions are provided for both the incorrect special cases and the general model through a numerical method since closed form solutions do not exist for these problems. In the second essay, singular control framework is employed in order to allow for gradual emission reduction instead of once-for-all type policies. The solution for the model is obtained using the numerical method introduced in the last essay. The effects of uncertainty and irreversibility on optimal emission reduction policy are investigated. The model is illustrated for greenhouse gas mitigation in the context of climate change problem and some of the model parameters are estimated using a state space model. In the third essay, a unified numerical method is introduced for solving multidimensional singular and impulse control models. The link between regime switching and singular/impulse control problems is established. This link results in a convenient representation of optimality conditions for the numerical method. After solving the optimality conditions at a discrete set of points, an approximate solution can be obtained by solving an extended vertical linear complementarity problem using a variety of techniques. The numerical approach is illustrated with four examples from economics and finance literature.
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.
A Heuristic Approach to a Portfolio Optimization Model with Nonlinear Transaction Costs
(2009-05-21) Na, Sungsoo; Tao Pang, Committee Co-Chair; James R. Wilson, Committee Member; Denis Pelletier, Committee Member; Richard H. Bernhard, Committee Chair
In this thesis we extend the Markowitz Mean-Variance model to a rebalancing portfolio optimization problem incorporating realistic considerations such as transaction costs and a risk-free asset with short-selling allowed, and we apply the Tabu Search (TS) heuristic to solve practical portfolio problems. First of all, we propose a biobjective portfolio optimization model which we expect to yield a portfolio equilibrium by combining the two objectives: maximize the portfolioÃ¢â‚¬â„¢s expected return and minimize its risk. For realistic portfolio problems we consider the multi-objective portfolio optimization models incorporating the risk-free asset and its short-selling and nonlinear transaction costs based on a single-period and a rebalancing portfolio optimization problem. Especially, to solve the rebalancing portfolio problem, we develop an adaptive, advanced TS algorithm having an evolutionary neighborhood structure, and we solve the problem with an iterative folding back procedure in the decision tree structure. Computational studies are performed with a risk-free asset and the number of risky assets to be 5, 10, 12, and 15 for both the single-period and rebalancing portfolio problems.
Information and Price Response in Storable Commodity Futures Markets: An Application to Lumber Contracts
(2007-08-20) Karali, Berna; Walter N. Thurman, Committee Chair; Douglas K. Pearce, Committee Member; Denis Pelletier, Committee Member; David A. Dickey, Committee Member
An Investigation of Spatial and Temporal Concepts in U.S. Corn and Soybean Markets
(2009-04-16) Bekkerman, Anton; Nicholas E. Piggott, Committee Co-Chair; Barry K. Goodwin, Committee Co-Chair; Michael J. Roberts, Committee Member; Denis Pelletier, Committee Member
Spatial and temporal issues are often important concepts within agricultural economics research. Understanding these issues and developing models that incorporate spatio-temporal frameworks can lead to more accuracy in answering important economic questions. This thesis uses the spatio-temporal framework to analyze topics that pertain to modeling disease risk of soybeans, estimating welfare effects from wind-borne diseases, and examining price transmissions in North Carolina soybean and corn markets. First, economic impacts of soybean rust in the United States are examined by using zero-inflated count-data models that are adjusted for potential endogeneity between inspections and infection finds. Past soybean rust finds and inspections in the county and in the surrounding counties, weather and overwintering conditions, and plant maturity groups and planting dates are all found to be significant aspects of determining soybean rust. These results are then used to accordingly price annual insurance contracts that cover soybean rust damages. Next, welfare impacts of wind-borne disease outbreaks in the United States are investigated under two alternative indemnification policies.The standard insurance program and a proposed check-off and mitigation scheme are compared, and simulation estimates are provided for a soybean rust outbreak in the U.S. soybean industry. The results indicate that welfare benefits may be as high as $1.7 billion under the check-off and mitigation plan. Finally, linkages between spatially separated corn markets and soybean markets in North Carolina are analyzed by extending the constant threshold autoregressive model, which is the methodology found in the current literature. The more flexible asymmetric variable thresholds model, which allows the transaction costs neutral band to vary according to external factors, statistically outperforms the alternative specifications, might better represent long time series data, and indicates that the constant threshold models can underestimate the time-to-convergence and magnitude of a price shock in linked markets.
Issues Regarding Price Risk in Agricultural Commodity Markets.
(2010-06-29) Tejeda, Hernan; Barry Goodwin, Committee Chair; Sujit Ghosh, Committee Member; Denis Pelletier, Committee Member; Nicholas Piggott, Committee Member
Multivariate Robust Estimation of DCC-GARCH Volatility Model.
(2010-05-07) LaBarr, Aric; Peter Bloomfield, Committee Chair; Howard Bondell, Committee Member; Denis Pelletier, Committee Member; David Dickey, Committee Member
Recursive Quantile Estimation with Application to Value at Risk
(2008-04-25) Ruan, Chen; Dave Dickey, Committee Member; Dennis Boos, Committee Member; Denis Pelletier, Committee Member; Peter Bloomfield, Committee Chair
Seasonal Unit Root Tests: A Comparison
(2008-10-26) Zhang, Qianyi; Denis Pelletier, Committee Member; Peter Bloomfield, Committee Member; Sastry G. Pantula, Committee Co-Chair; David A. Dickey, Committee Co-Chair
Three major regression-based seasonal unit root tests: the DHF test introduced by Dickey et al (1984), the HEGY test proposed by Hylleberg et al. (1990) and the Kunst test introduced by Kunst (1997) are compared. The regression model for the DHF test is a reduced form of that for the Kunst test. We modify the Kunst test by using the t-statistic instead of Kunst's proposed joint F-statistic to study the influence of additional variables in the Kunst model. Also, we modify the HEGY test to test the presence of all four quarterly unit roots against the presence of roots 1 and -1. Through the comparison between the DHF test and the modified HEGY test, we find that the DHF test does not have asymptotic power one when the series only have some of the seasonal unit roots but not all of them. We call this case of partial unit roots. The asymptotic distributions derived in the paper provide the explanation of this limitation for the DHF test. Using simulation, we find that the probability that the DHF test will lead researchers to accept the seasonal unit root null hypothesis increases when the series contains more partial unit roots. For the DHF test, the test power depends on the augmented model. We derive limits of the related estimates from two augmented models for the DHF test. Both estimates are inconsistent. The test statistic obtained from the augmented model suggested by Ghysels et al. (1992) has relatively low power. For the HEGY/Kunst test, most limiting distributions for the test statistics depend on the lag augmentation but the test statistics have few problems caused by inconsistent estimates. However, the augmented models for the HEGY/Kunst test have more variables than those for the DHF test. Based on our simulation study results, the inclusion of more variables results in more loss in power when a redundant variable is included, and more sensitivity to the size distortion when the augmented lag length is less than the true lag length.
Second Order Approximations to GMM Statistics
(2006-05-08) Kyriakoulis, Konstantinos; Denis Pelletier, Committee Member; David Dickey, Committee Member; Atsushi Inoue, Committee Member; Alastair Hall, Committee Chair
This thesis uses second order approximations to study the finite sample behavior of statistics that are employed under the GMM setting. We present a Nagar (1959) approximation to the MSE of the IV estimates, when the disturbances are elliptically distributed. The accuracy of the approximation is illustrated through a comparison of the Nagar-type expansion with the exact finite sample MSE, that was derived in Knight (1985). The comparison suggests that second order approximations can be quite accurate, even when the sample size is 60 observations. This, alongside with the fact that exact results are more difficult to derive and harder to interpret, suggests that second-order approximations are powerful alternatives to the standard, first-order, asymptotic approximations. We proceed by analyzing the finite sample behavior of the LMstatistic, as it is employed under the GMM setting. This is achieved through a second order expansion, known as Edgeworth Expansion, of the distribution of the LM statistic. Our analysis suggests that the passage from the finite to the limiting distribution of the LM test is based on several measures, such as the variance-covariance matrix of the moments and its first derivative, the fourth product moment of the population moment condition, the covariance between the moments and their variance, the number of parameters, and the number of moments. We conclude with a simulation study that illustrates how these measures drive the passage from the finite sample to the asymptotic distribution.
A Stochastic Volatility Model and Inference for the Term Structure of Interest
(2007-04-25) Liu, Peng; A. Ronald Gallant, Committee Member; Denis Pelletier, Committee Member; William H. Swallow, Committee Member; Peter Bloomfield, Committee Chair; David Dickey, Committee Member
This thesis builds a stochastic volatility model for the term structure of interest rates, which is also known as the dynamics of the yield curve. The main purpose of the model is to propose a parsimonious and plausible approach to capture some characteristics that conform to some empirical evidences and conventions. Eventually, the development reaches a class of multivariate stochastic volatility models, which is flexible, extensible, providing the existence of an inexpensive inference approach. The thesis points out some inconsistency among conventions and practice. First, yield curves and its related curves are conventionally smooth. But in the literature that these curves are modeled as random functions, the co-movement of points on the curve are usually assumed to be governed by some covariance structures that do not generate smooth random curves. Second, it is commonly agreed that the constant volatility is not a sound assumption, but stochastic volatilities have not been commonly considered in related studies. Regarding the above problems, we propose a multiplicative factor stochastic volatility model, which has a relatively simple structure. Though it is apparently simple, the inference is not, because of the presence of stochastic volatilities. We first study the sequential-Monte-Carlo-based maximum likelihood approach, which extends the perspectives of Gaussian linear state-space modeling. We propose a systematic procedure that guides the inference based on this approach. In addition, we also propose a saddlepoint approximation approach, which integrates out states. Then the state propagates by an exact Gaussian approximation. The approximation works reasonably well for univariate models. Moreover, it works even better for the multivariate model that we propose. Because we can enjoy the asymptotic property of the saddlepoint approximation.