Bank mergers and the dynamics of deposit interest rates

Despite extensive research interest in the last decade, the banking literature has not reached a consensus on the impact of bank mergers on deposit rates. In particular, results on the dynamics of deposit rates surrounding bank mergers vary substantially across studies. In this paper, we aim for a comprehensive empirical analysis of a bank merger’s impact on deposit rate dynamics. We base the analysis on a unique dataset comprising deposit rates of 624 U.S. banks with a monthly frequency for the time period 1997–2006. These data are matched with individual bank and local market characteristics and the complete list of bank mergers in the United States. The data allow us to track the dynamics of bank mergers while controlling for the rigidity of the deposit rates and for a range of merger, bank, and local market features. An innovation of our work is the introduction of an econometric approach for estimating the change of the deposit rates given their rigidity.Bank mergers ; Bank deposits

Bank mergers and the dynamics of deposit interest rates

Despite extensive research interest in the last decade, the banking literature has not reached a consensus on the impact of bank mergers on deposit rates. In particular, results on the dynamics of deposit rates surrounding bank mergers vary substantially across studies. In this paper, we aim for a comprehensive empirical analysis of a bank merger's impact on deposit rate dynamics. We base the analysis on a unique dataset comprising deposit rates of 624 US banks with a monthly frequency for the time period 1997-2006. These data are matched with individual bank and local market characteristics and the complete list of bank mergers in the US. The data allow us to track the dynamics of bank mergers while controlling for the rigidity of the deposit rates and for a range of merger, bank and local market features. An innovation of our work is the introduction of an econometric approach of estimating the change of the deposit rates given their rigidity. --Deposit rate dynamics,bank mergers,deposit rate rigidity

Fusion of Hard and Soft Information in Nonparametric Density Estimation

This article discusses univariate density estimation in situations when the sample (hard information) is supplemented by “soft” information about the random phenomenon. These situations arise broadly in operations research and management science where practical and computational reasons severely limit the sample size, but problem structure and past experiences could be brought in. In particular, density estimation is needed for generation of input densities to simulation and stochastic optimization models, in analysis of simulation output, and when instantiating probability models. We adopt a constrained maximum likelihood estimator that incorporates any, possibly random, soft information through an arbitrary collection of constraints. We illustrate the breadth of possibilities by discussing soft information about shape, support, continuity, smoothness, slope, location of modes, symmetry, density values, neighborhood of known density, moments, and distribution functions. The maximization takes place over spaces of extended real-valued semicontinuous functions and therefore allows us to consider essentially any conceivable density as well as convenient exponential transformations. The infinite dimensionality of the optimization problem is overcome by approximating splines tailored to these spaces. To facilitate the treatment of small samples, the construction of these splines is decoupled from the sample. We discuss existence and uniqueness of the estimator, examine consistency under increasing hard and soft information, and give rates of convergence. Numerical examples illustrate the value of soft information, the ability to generate a family of diverse densities, and the effect of misspecification of soft information.U.S. Army Research Laboratory and the U.S. Army Research Office grant 00101-80683U.S. Army Research Laboratory and the U.S. Army Research Office grant W911NF-10-1-0246U.S. Army Research Laboratory and the U.S. Army Research Office grant W911NF-12-1-0273U.S. Army Research Laboratory and the U.S. Army Research Office grant 00101-80683U.S. Army Research Laboratory and the U.S. Army Research Office grant W911NF-10-1-0246U.S. Army Research Laboratory and the U.S. Army Research Office grant W911NF-12-1-027

Finite element analysis based on a parametric model by approximating point clouds

Simplified models are widely applied in finite element computations regarding mechanical and structural problems. However, the simplified model sometimes causes many deviations in the finite element analysis (FEA) of structures, especially in the non-designed structures which have undergone unknowable deformation features. Hence, a novel FEA methodology based on the parametric model by approximating three-dimensional (3D) feature data is proposed to solve this problem in the present manuscript. Many significant anci effective technologies have been developeci to detect 3D feature information accurately, e.g., terrestrial laser scanning (TLS), digital photogrammetry, and radar technology. In this manuscript, the parametric FEA model combines 3D point clouds from TLS and the parametric surface approximation method to generate 3D surfaces and models accurately. TLS is a popular measurement method for reliable 3D point clouds acquisition and monitoring deformations of structures with high accuracy and precision. The B-spline method is applied to approximate the measured point clouds data automatically and generate a parametric description of the structure accurately. The final target is to reduce the effects of the model description and deviations of the FEA. Both static and dynamic computations regarding a composite structure are carried out by comparing the parametric and general simplified models. The comparison of the deformation and equivalent stress of future behaviors are reflected by different models. Results indicate that the parametric model based on the TLS data is superior in the finite element computation. Therefore, it is of great significance to apply the parametric model in the FEA to compute and predict the future behavior of the structures with unknowable deformations in engineering accurately

Modelling the impact of climate change on health

The main objective of this thesis is to develop a robust statistical model by accounting the non-linear relationships between hospital admissions due to lower respiratory (LR) disease and factors of climate and pollution, and their delayed effects on hospital admissions. This study also evaluates whether the model fits can be improved by considering the non-linearity of the data, delayed effect of the significant factors, and thus calculate threshold levels of the significant climate and pollution factors for emergency LR hospital admissions. For the first time three unique administrative datasets were merged: Hospital Episode Statistics, Met office observational data for climate factors, and data from London Air Quality Network. The results of the final GLM, showed that daily temperature, rain, wind speed, sun hours, relative humidity, and PM10 significantly affected the LR emergency hospital admissions. Then, we developed a Distributed lag non-linear model (DLNM) model considering the significant climate and pollution factors. Time and ‘day of the week’ was incorporated as linear terms in the final model. Higher temperatures around ≥270C a quicker effect of 0-2 days lag but lower temperatures (≤00C) had delayed effects of 5-25 days lag. Humidity showed a strong immediate effect (0-3 days) of the low relative humidity at around ≤40% and a moderate effect for higher humidity (≥80%) with lag period of 0-2 days. Higher PM10 around ≥70-μg/m3 has both shorter (0-3 days) and longer lag effects (15-20 days) but the latter one is stronger comparatively. A strong effect of wind speed around ≥25 knots showed longer lag period of 8-15 days. There is a moderate effect for a shorter lag period of 0-3 days for lower wind speed (approximately 2 knots). We also notice a stronger effect of sun hours around ≥14 hours having a longer lag period of 15-20 days and moderate effect between 1-2 hours of 5-12 days lag. Similarly, higher amount of rain (≥30mm) has stronger effects, especially for the shorter lag of 0-2 days and longer lag of 7- 10 days. So far, very little research has been carried out on DLNM model in such research area and setting. This PhD research will contribute to the quantitative assessment of delayed and non-linear lag effects of climate and pollutants for the Greater London region. The methodology could easily be replicated on other disease categories and regions and not limited to LR admissions. The findings may provide useful information for the development and implementation of public health policies to reduce and prevent the impact of climate change on health problems

Semiparametric Characteristics-based Models of Asset Returns

This thesis, which includes three chapters, studies asset-specific characteristics such as capitalization, book-to-market ratio etc., and their implications on assets prices and portfolio management. This thesis selects characteristics that have prediction powers on assets excess returns and specifies a flexible regression model, including linear, non-linear and pairwise interactive parts. This thesis further analyses whether characteristics are relevant as mispricing components and factor loadings in an asset pricing factor model. Finally, this thesis develops an optimal portfolio selection method based on the constructed characteristics-based asset pricing model. Methodologies in this thesis are mainly proposed for two popular questions in financial econometrics, namely, high dimensional analysis and the approximation of uni-variate and multi-variate unknown functions. The tools extended by this thesis are B-splines and orthogonal series, and multi-variate unknown functions are approximated by tensor products. In terms of high dimensional problems, which are caused by both abundant financial data and diverging B-splines bases used to approximate unknown functions, they are solved by LASSO-style selection model and power enhanced hypothesis tests. The details of the three chapters are summarized below: Specification LASSO and an Application in Financial Markets This chapter proposes the method of Specification-LASSO in a flexible semi-parametric regression model that allows for the interactive effects between different covariates. Specification-LASSO extends LASSO and Adaptive Group LASSO to achieve both relevant variable selection and model specification. Specification-LASSO also gives preliminary estimates that facilitate the estimation of the regression model. Monte Carlo simulations show that the Specification-LASSO can accurately specify partially linear additive models with interactive effects. Finally, the proposed methods are applied in an empirical study, which examines the topic proposed by \cite{freyberger2020dissecting}, arguing that firms’ sizes may have interactive effects with other security-specific characteristics, which can explain the stocks excess returns together. Dynamic Peer Groups of Arbitrage Characteristics This chapter proposes an asset pricing factor model constructed with semi-parametric characteristics-based mispricing and factor loading functions. We approximate the unknown functions by B-splines sieve where the number of B-splines coefficients is diverging. We estimate this model and test the existence of the mispricing function by a power-enhanced hypothesis test. The enhanced test solves the low power problem caused by diverging B-splines coefficients, with the strengthened power approaches one asymptotically. We also investigate the structure of mispricing components through Hierarchical K-means Clusterings. We apply our methodology to CRSP (Center for Research in Security Prices) and Compustat data for the US stock market with one-year rolling windows during 1967-2017. This empirical study shows the presence of mispricing functions in certain time blocks. We also find that distinct clusters of the same characteristics lead to similar arbitrage returns, forming a “peer group” of arbitrage characteristics. A Dynamic Semiparametric Characteristics-based Model for Optimal Portfolio Selection This paper develops a two-step semiparametric methodology for portfolio weight selection for characteristics-based factor-tilt and factor-timing investment strategies. We build upon the expected utility maximization framework of \cite{brandt1999estimating} and \cite{ait2001variable}. We assume that assets returns obey a characteristics-based factor model with time-varying factor risk premia as in \cite{li2020dynamic}. We prove under our return-generating assumptions that an approximately optimal portfolio can be established using a two-step procedure in a market with a large number of assets. The first step finds optimal factor-mimicking sub-portfolios using a quadratic objective function over linear combinations of characteristics-based factor loadings. The second step dynamically combines these factor-mimicking sub-portfolios based on a time-varying signal, using the investor’s expected utility as the objective function. We develop and implement a two-stage semiparametric estimator. We apply it to CRSP (Center for Research in Security Prices) and FRED (Federal Reserve Economic Data) data and find excellent in-sample and out-sample performance consistent with investors’ risk aversion levels

Large Term Structure Movements in a Factor Model Framework

This paper analyzes US Term Structure changes using linear factor models based on principal components analysis and the model of Diebold and Li. The analysis of factors time series could not reject the hypothesis of normality for changes in the first two factors that accounts for level and slope effects. This enables the assumption that factors follow correlated Ornstein-Uhlenbeck processes, and then construct 95% confidence ellipses that allow us to identify large movements that are interpreted as unanticipated by market participants. The results suggest the importance of the economic assessment released by the monetary authority, and the ability of agents to anticipate Fed’s actions over the sample period 1997:01 2005:04.