Bayesian Semiparametric Multivariate Density Deconvolution

We consider the problem of multivariate density deconvolution when the interest lies in estimating the distribution of a vector-valued random variable but precise measurements of the variable of interest are not available, observations being contaminated with additive measurement errors. The existing sparse literature on the problem assumes the density of the measurement errors to be completely known. We propose robust Bayesian semiparametric multivariate deconvolution approaches when the measurement error density is not known but replicated proxies are available for each unobserved value of the random vector. Additionally, we allow the variability of the measurement errors to depend on the associated unobserved value of the vector of interest through unknown relationships which also automatically includes the case of multivariate multiplicative measurement errors. Basic properties of finite mixture models, multivariate normal kernels and exchangeable priors are exploited in many novel ways to meet the modeling and computational challenges. Theoretical results that show the flexibility of the proposed methods are provided. We illustrate the efficiency of the proposed methods in recovering the true density of interest through simulation experiments. The methodology is applied to estimate the joint consumption pattern of different dietary components from contaminated 24 hour recalls

Rules versus discretion in loan rate setting.

Market; Order; Rules;

Rules versus Discretion in Loan Rate Setting

We propose a heteroscedastic regression model to identify the determinants of the dispersion in interest rates on loans granted to small and medium sized enterprises. We interpret unexplained deviations as evidence of the banks’ discretionary use of market power in the loan rate setting process. “Discretion” in the loan-pricing process is most important, we find, if: (i) loans are small and uncollateralized; (ii) firms are small, risky and difficult to monitor; (iii) firms’ owners are older, and, (iv) the banking market where the firm operates is large and highly concentrated. We also find that the weight of “discretion” in loan rates of small credits to opaque firms has decreased somewhat over the last fifteen years, consistent with the proliferation of information-technologies in the banking industry. Overall, our results reflect the relevance in the credit market of the costs firms face in searching information and switching lenders.financial intermediation;loan rates;price discrimination;variance analysis

Rules versus discretion in loan rate setting

We propose a heteroscedastic regression model to identify the determinants of the dispersion in interest rates on loans granted to small and medium sized enterprises. We interpret unexplained deviations as evidence of the banks’ discretionary use of market power in the loan rate setting process. “Discretion” in the loan-pricing process is most important, we find, if: (i) loans are small and uncollateralized; (ii) firms are small, risky and difficult to monitor; (iii) firms’ owners are older, and, (iv) the banking market where the firm operates is large and highly concentrated. We also find that the weight of “discretion” in loan rates of small credits to opaque firms has decreased somewhat over the last fifteen years, consistent with the proliferation of information-technologies in the banking industry. Overall, our results reflect the relevance in the credit market of the costs firms face in searching information and switching lenders.financial intermediation, loan rates, price discrimination, variance analysis.

Forecasting Volatility in Stock Market Using GARCH Models

Forecasting volatility has held the attention of academics and practitioners all over the world. The objective for this master's thesis is to predict the volatility in stock market by using generalized autoregressive conditional heteroscedasticity(GARCH) methodology. A detailed explanation of GARCH models is presented and empirical results from Dow Jones Index are discussed. Different from other literatures in this field, this paper studies forecasting volatility from a new perspective by comparing GARCH(P,Q) model with GJR-GARCH(P,Q) model and EGARCH(P,Q) model. GJR-GARCH(P,Q) model turns out to be more powerful than GARCH(P,Q) model due to catching some leverage effects successfully. This makes our prediction more reliable and accurate. This paper also shows that both GARCH(P,Q) model and GJR-GARCH(P,Q) model are good choices for dealing with heteroscedastic time series

Rules versus Discretion in Loan Rate Setting

We propose a heteroscedastic regression model to identify the determinants of the dispersion in interest rates on loans granted to small and medium sized enterprises. We interpret unexplained deviations as evidence of the banks’ discretionary use of market power in the loan rate setting process. “Discretion” in the loan-pricing process is most important, we find, if: (i) loans are small and uncollateralized; (ii) firms are small, risky and difficult to monitor; (iii) firms’ owners are older, and, (iv) the banking market where the firm operates is large and highly concentrated. We also find that the weight of “discretion” in loan rates of small credits to opaque firms has decreased somewhat over the last fifteen years, consistent with the proliferation of information-technologies in the banking industry. Overall, our results reflect the relevance in the credit market of the costs firms face in searching information and switching lenders.financial intermediation;loan rates;price discrimination;variance analysis

Rules versus Discretion in Loan Rate Setting

We propose a heteroscedastic regression model to identify the determinants of the dispersion in interest rates on loans granted to small and medium sized enterprises. We interpret unexplained deviations as evidence of the banks’ discretionary use of market power in the loan rate setting process. “Discretion” in the loan-pricing process is most important, we find, if: (i) loans are small and uncollateralized; (ii) firms are small, risky and difficult to monitor; (iii) firms’ owners are older, and, (iv) the banking market where the firm operates is large and highly concentrated. We also find that the weight of “discretion” in loan rates of small credits to opaque firms has decreased somewhat over the last fifteen years, consistent with the proliferation of information-technologies in the banking industry. Overall, our results reflect the relevance in the credit market of the costs firms face in searching information and switching lenders.financial intermediation. loan rates, price discrimination, variance analysis

Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics

First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news.Web of Science421049

Bayesian nonparametric multivariate convex regression

In many applications, such as economics, operations research and reinforcement learning, one often needs to estimate a multivariate regression function f subject to a convexity constraint. For example, in sequential decision processes the value of a state under optimal subsequent decisions may be known to be convex or concave. We propose a new Bayesian nonparametric multivariate approach based on characterizing the unknown regression function as the max of a random collection of unknown hyperplanes. This specification induces a prior with large support in a Kullback-Leibler sense on the space of convex functions, while also leading to strong posterior consistency. Although we assume that f is defined over R^p, we show that this model has a convergence rate of log(n)^{-1} n^{-1/(d+2)} under the empirical L2 norm when f actually maps a d dimensional linear subspace to R. We design an efficient reversible jump MCMC algorithm for posterior computation and demonstrate the methods through application to value function approximation