The generalized ridge estimator and improved adjustments for regression parameters

Summary The generalized ridge estimator, which considers generalizations of mean square error, is presented, and a mathematical rule of determining the optimalk-value is discussed. The generalized ridge estimator is examined in comparison with the least squares, the pseudoinverse, theJames-Stein-type shrinkage, and the principal component estimators, especially focusing their attention on improved adjustments for regression coefficients. An alternative estimation approach that better integrates a priori information is noted. Finally, combining the generalized ridge and robust regression methods is suggested.     

How Do Small Businesses Finance Their Growth Opportunities?—The Case of Recovery from the Lost Decade in Japan

Previous studies argue that bank loans are cheaper than trade credit, so less‐constrained firms largely depend on bank loans and use trade credit less, especially in financially developed economies. However, the Japanese evidence does not support this view. First, small businesses with higher credit demand increase trade credit more during the period of the recovery from a severe recession. Second, creditworthy firms also increase trade credit to finance their growth opportunities. Third, firms in unstable industries increase trade credit more. This suggests that suppliers are able to offer credit, unlike banks, as they have a relative advantage in day‐by‐day monitoring. Copyright © 2012 John Wiley & Sons, Ltd.     

Changing banking relationships and client‐firm performance: Evidence from Japan for the 1990s

The extant literature generally suggests that the performance of client firms deteriorates if their distressed main bank reduces the supply of credit. However, this insight is only consistent with the notion that main banks have an information advantage over other banks to the extent that a client firm has trouble getting access to credit if the firm changes its main bank. This paper shows that Japanese firms did change their main banking relationship when their main banks become distressed in a period with financial shocks. Surprisingly, these firms did not suffer from loss of access to credit and actually their performance significantly improved after their change of main banks.     

Taxation In The Two‐Sector Neoclassical Growth Model With Sector‐Specific Externalities And Endogenous Labour Supply*

This paper examines the long‐run impacts of selective (or sector‐specific) commodity, payroll, and profit taxes in a two‐sector endogenous growth model with sector‐specific production externalities, in which one sector produces consumption goods and the other produces investment goods. The novelty of the model is that it allows not only for endogenous labour supply but also for the intersectoral allocation of resources, which may together lead to indeterminacy. We analytically show that the stability properties of the long‐run equilibrium critically affect the long‐run effects of these selective taxes, which may reverse the standard results of the growth effects of distortionary taxes.     

A New Computational Scheme for Computing Greeks by the Asymptotic Expansion Approach

We developed a new scheme for computing “Greeks” of derivatives by an asymptotic expansion approach. In particular, we derived analytical approximation formulae for Deltas and Vegas of plain vanilla and average European call options under general Markovian processes of underlying asset prices. Moreover, we introduced a new variance reduction method of Monte Carlo simulations based on the asymptotic expansion scheme. Finally, several numerical examples under CEV processes confirmed the validity of our method.     

Effects of Jumps and Small Noise in High-Frequency Financial Econometrics

Several new statistical procedures for high-frequency financial data analysis have been developed to estimate risk quantities and test the presence of jumps in the underlying continuous-time financial processes. Although the role of micro-market noise is important in high-frequency financial data, there are some basic questions on the effects of presence of noise and jump in the underlying stochastic processes. When there can be jumps and (micro-market) noise at the same time, it is not obvious whether the existing statistical methods are reliable for applications in actual data analysis. We investigate the misspecification effects of jumps and noise on some basic statistics and the testing procedures for jumps proposed by Ait-Sahalia and Jacod (Ann Stat 37–1:184–222 2009; 38–5:3093–3123 2010) as an illustration. We find that their first test (testing the presence of jumps as a null-hypothesis) is asymptotically robust in the small-noise asymptotic sense against possible misspecifications while their second test (testing no-jumps as a null-hypothesis) is quite sensitive to the presence of noise.     

Applying Time Series Decomposition to Construct Index-Tracking Portfolio

This study proposes a new method for creating an index-tracking portfolio using time series decomposition. First, we construct index-tracking portfolios of stocks chosen because their price movements mimic that of the Dow-Jones Industrial Average. Our method utilizes similarities of constituent stocks to the benchmark that are assessed by distances of time series trends derived from decomposing original series. Although the portfolios chosen by our method reasonably tracked the performance of the benchmark, they did not surpass the clustering approach discussed in earlier studies. Therefore, we examined what causes tracking error and found that two causes for deficiencies in our similarity-based method, which are unintended irregular movements of holding stocks and highly correlated relationships within stocks in the portfolio. To overcome them and to improve tracking performance, we propose a similarity-balanced approach that is another index-tracking method with alternate use of similarity. Doing so improved the tracking performance by avoiding the problem of high correlation among the stocks chosen under the initial method.     

Process and context in choice models

We develop a general framework that extends choice models by including an explicit representation of the process and context of decision making. Process refers to the steps involved in decision making. Context refers to factors affecting the process, focusing in this paper on social networks. The extended choice framework includes more behavioral richness through the explicit representation of the planning process preceding an action and its dynamics and the effects of context (family, friends, and market) on the process leading to a choice, as well as the inclusion of new types of subjective data in choice models. We discuss the key issues involved in applying the extended framework, focusing on richer data requirements, theories, and models, and present three partial demonstrations of the proposed framework. Future research challenges include the development of more comprehensive empirical tests of the extended modeling framework.     

Loan officers and relationship lending to SMEs

Previous research suggests that loan officers may play a critical role in relationship lending by producing soft information about SMEs. For the first time, we analyze this hypothesis and find empirical evidence that indicates that loan officer activities are associated with bank production of soft information. We also find that loan officers at small banks produce more soft information than at large banks, but large banks appear to have the equivalent potential to underwrite relationship loans. Nevertheless, large banks choose instead to focus their resources on transactions lending.