Portfolio Capital Flows in Thailand: A Bayesian Model Averaging Approach

We study the gross and net terms of portfolio capital flows by examining their determinants. Through the application of the Bayesian model averaging method, the determinants are evaluated by a set of models instead of a single specification. Our findings show that the magnitude of both gross equity and gross debt flows are large, relative to their net terms. Equity inflows and outflows are quite symmetric with similar determinants; debt inflows and outflows are less symmetric. The paper provides partial evidence to support the importance of both internal and external factors as determinants of capital flows.     

Forecasting Commodity Market Returns Volatility: A Hybrid Ensemble Learning GARCH-LSTM based Approach

This study investigates the advantage of combining the forecasting abilities of multiple generalized autoregressive conditional heteroscedasticity (GARCH)-type models, such as the standard GARCH (GARCH), exponential GARCH (eGARCH), and threshold GARCH (tGARCH) models with advanced deep learning methods to predict the volatility of five important metals (nickel, copper, tin, lead, and gold) in the Indian commodity market. This paper proposes integrating the forecasts of one to three GARCH-type models into an ensemble learning-based hybrid long short-term memory (LSTM) model to forecast commodity price volatility. We further evaluate the forecasting performance of these models for standalone LSTM and GARCH-type models using the root mean squared error, mean absolute error, and mean fundamental percentage error. The results highlight that combining the information from the forecasts of multiple GARCH types into a hybrid LSTM model leads to superior volatility forecasting capability. The SET-LSTM, which represents the model that combines forecasts of the GARCH, eGARCH, and tGARCH into the LSTM hybrid, has shown the best overall results for all metals, barring a few exceptions. Moreover, the equivalence of forecasting accuracy is tested using the Diebold–Mariano and Wilcoxon signed-rank tests.     

Equivalizing Incomes: A Normative Approach

The paper deals with the comparisonof living standards and investigates two normative methods ofderiving equivalizing transformations for a population whichhas different household types. The first one equates the utilitylevels of representative household members belonging to differenthouseholds. The second method evaluates the well-being of householdsby a social welfare ordering defined by means of household utilityfunctions. The methods can determine the implicit normative assumptionsinvolved in conducting distributional analysis using any equivalizingtransformation. In particular income-level dependent equivalencescales can be founded in this framework. The assumptions underlyingboth approaches are examined and compared.     

Out-of-Sample Predictability in International Equity Markets: A Model Selection Approach

For 13 major international stock markets, there is much evidence of out-of-sample predictability for growth stocks especially when evaluated with economic criteria, and to a noticeably lesser extent for general stock markets and value stocks. Our results shed light on the recent debate about stock return predictability, using different assets (growth-style indexes), forecasting variables (past returns), forecasting models (nonlinear models), and alternative forecasting evaluation criteria (economic criteria). Our analysis suggests that (growth) stock returns might be predictable.     

A New Prepayment Model (with Default): An Occupation-time Derivative Approach

A new prepayment model is developed, which improves the modeling of the borrowers decision process by incorporating an occupation-time derivative in the valuation framework of a fixed-rate mortgage. This option-theoretic mortgage valuation model is based on stochastic house-price and interest-rate models, and requires a particularly subtle technique to incorporate a new type of occupation-time derivative, where the barrier (which activates the derivative) is in the value process and not the underlying process (as it is in standard occupation-time derivatives). This new model simulates a delay in prepayment by the borrower (beyond the time simple ruthless prepayment dictates), thus increasing the value of the mortgage to the lender, compared to the value gained using more basic models. This allows for a more advanced borrower decision process, where a rational exercise structure is retained in a modified form. Empirical evidence supports this theory, which should be beneficial for accurate mortgage-backed security pricing. The results in this paper explore thoroughly the effect on the mortgage value of a delay in prepayment by the borrower on the embedded options held and on the insurance component.

Market and Model Credit Default Swap Spreads: Mind the Gap!

Structural models of default establish a relation across the fair values of various asset classes (equity, bonds, credit derivatives) referring to the same company. In most circumstances such relation is verified in practice, as different financial assets tend to move in the same direction at similar speed. However, occasional deviations from the theoretical fair values occur, especially in times of financial turmoil. Understanding how the dynamics of the theoretical fair values of various assets compares to that of their market values is crucial to a number of market participants. This paper investigates whether a popular structural model, the CreditGrades approach proposed by Finger (2002) , Stamicar and Finger (2005) , succeeds in explaining the dynamic relation between equity/option variables and Credit Default Swap (CDS) premia at individual company level. We find that CDS model spreads display a significant correlation with CDS market spreads. However, the gap between the two is time varying and widens substantially in times of financial turbulence. The analysis of the gap dynamics reveals that this is partly due to episodes of decoupling between equity and credit markets, and partly due to shortcomings of the model. Finally, we observe that model spreads tend to predict market spreads.     

A Proportional Hazards Model of Commercial Mortgage Default with Originator Bias

A proportional hazards model with competing risks is specified and is extended to correct for the possibility of originator bias. The model is used to examine the ability of option-theoretic models of mortgage pricing to forecast commercial mortgage defaults. Among the findings, those especially of interest include the influence of contemporaneous loan-to-value and debt-service-coverage ratios on commercial mortgage default probabilities. The paper also finds that option-theoretic models of mortgage pricing are quite capable of producing default estimates that fit the actual default rates well, especially when the model is corrected for originator bias.     

The Dynamic Speed of Cash-Holding Adjustment in a Transition Economy: A New Approach and Evidence

Using a new approach, we estimate the speed of cash-holding adjustment for a typical transitional economy by using Chinese listed firms’ samples over 1999–2011. First, we use model-averaging techniques to identify reliably important cash-holding determinants. Second, we conduct Monte Carlo simulation using the real finance data to evaluate appropriateness of the empirical estimator from a variety of dynamic estimation methods and suggest an optimized system of generalized method of moments (OPT-GMM) as an appropriate econometric approach for speed estimation. Finally, we get the speed of 46 percent, which is significantly lower than the contemporary speed in the United Kingdom and the United States.     

Predicting Corporate Financial Distress: A Time-Series CUSUM Methodology

The ability to predict corporate financial distress can be strengthened using models that account for serial correlation in the data, incorporate information from more than one period and include stationary explanatory variables. This paper develops a stationary financial distress model for AMEX and NYSE manufacturing and retailing firms based on the statistical methodology of time-series Cumulative Sums (CUSUM). The model has the ability to distinguish between changes in the financial variables of a firm that are the result of serial correlation and changes that are the result of permanent shifts in the mean structure of the variables due to financial distress. Tests performed show that the model is robust over time and outperforms similar models based on the popular statistical methods of Linear Discriminant Analysis and Logit.     

A Unified Model of Corporate Acquisitions and Divestitures: An Incentive Perspective

The purpose of this paper is to unify corporate acquisitions and divestitures (e.g., spin-offs, equity carve-outs and sell-offs) into a simple but comprehensive agency model where risk and managerial incentives interact to determine an optimal corporate governance and an incentive compensation scheme. Emphasizing human and nonhuman aspect of corporate assets, the model not only explains existing empirical evidence regarding contractual and organizational changes, but also suggests new perspective regarding firms' behavior around corporate acquisitions and divestitures. Thus, it attempts to reconcile between synergistic and agency viewpoints in the takeover literature and provides determining factors in choosing between spinoffs and equity carve-outs.     

A Prepayment Model for the Japanese Mortgage Loan Market: Prepayment-Type-Specific Parametric Model Approach

This paper proposes a framework for construction of a prepayment model suitedto the Japanese mortgage loan market and assesses the validity of thisframework based on an empirical analysis using data from Japan. In thisframework, a model is constructed for each of three prepayment types, namely,`full prepayment', `partial prepayment', and `subrogation', using a parametricproportional hazards model, which was also employed by Schwartz and Torous(1989). Combining these three types of models allows one to take into accountthe effects of partial prepayments, which are frequently used in the Japanesemortgage market, and to simultaneously construct a model for both prepaymentand default. Time-dependent (path-dependent) covariates are introduced intothe model, which are estimated by the maximum likelihood method based on thefull likelihood that takes into account the time-dependence of the covariates.Results of the empirical analysis indicate that the hazard functions differsubstantially depending on the prepayment type. In addition, results indicatethat the fit of the model can be improved by the distinction of prepaymenttypes and the introduction of the market interest rates as path-dependentcovariates.     

Corporate Policy and Market Value: A q-Theory Approach

In this paper, we show that Tobin's q has a significant predictive power in explaining valuation consequences of major corporate policy variables. Our empirical results reveal that, depending upon whether a firm is overinvesting or underinvesting, financial markets respond quite differently to its capital structure, dividend payout, financial slack, and R & D decisions. Overall, the empirical results suggest that both high debt ratios and greater payouts are favorably viewed by the market when firms are overinvesting. For firms with growth opportunities, however, large debt is unfavorably viewed by the market. In addition, financial slack and R & D expenditures are favorably received by the market for growth firms but not for overinvesting firms.     

Cointegration Analysis of Audit Pricing Model: A Panel Unit Root Test Approach

In this study, we provide evidence on the stationarity of real audit fees and the major explanatory variables frequently used in the audit pricing models from a pooled data set, using panel unit root tests developed by Im et al. (1997). The panel unit root test supports the hypothesis of non‐stationarity of audit fees and their major determinants. We demonstrate that variables in the audit pricing model that were previously found to have impact on audit fees may turn out to be useless when more powerful tests like panel tests are applied to these variables. Our evidence implies that failing to employ appropriate procedure to test cointegration and to specify the appropriate model for audit fees and their determinants would generate results that may have exaggerated the effects of some variables on audit fees.     

违约与杠杆周期——一个带有救助的金融加速器模型

我国为应对2008年国际金融危机的冲击采取了一系列经济刺激政策,在发挥"稳增长"作用的同时,也在一定程度上导致我国企业部门杠杆水平快速上升,但与此同时,不良贷款率并没有随企业部门杠杆的上升而显著增加。为了解释企业部门违约与杠杆的周期特征,本文在金融加速器模型(Bernanke et al.,1999)基础上,引入政府对企业部门的违约救助机制,建立DSGE模型进行讨论。进一步地,本文还通过一个不合意的去杠杆政策试验表明,忽略资产价格稳定(或者说金融稳定)前提下的去杠杆政策,反而会使企业部门的杠杆和违约率同时上升到一个较高水平。最后,引入一个盯住预期资产价格的动态救助规则能够发挥稳定经济的作用,并提高社会福利水平。     

Information Asymmetry and Corporate Investment Decisions: A Dynamic Approach

This paper develops a dynamic model of the financing and operating decisions of firms in the presence of information asymmetry. When the value of growth opportunities is not fully recognized, securities are undervalued, thus influencing the financing and investment decisions. The agency‐based underinvestment problem is re‐examined under information asymmetry. For firms with greater growth opportunities, the investment distortion resulting from information asymmetry is especially significant. Information asymmetry also increases the expected bankruptcy cost. The cost of information asymmetry in terms of both the firm value and the information spread under the optimal capital structure could be substantial.     

Voluntary Carbon Reporting Prediction: A Machine Learning Approach

In this paper we address the impact of the introduction of the National Greenhouse and Energy Reporting scheme on corporate carbon reporting, and subsequently identify factors that influence the level of voluntary carbon reporting. A review of the literature demonstrates a large number of potential factors have been previously deployed to explain voluntary reporting practices; however, the analytical and empirical methods widely used in the literature have limiting statistical assumptions and confine analysis to a small number of explanatory factors. To address this limitation in prior research we apply advanced machine learning methods, such as gradient boosting machines and random forests, to identify predictive variables through analytical means. We compare the performance of machine learning methods with traditional methods such as logistic regression. We find that machine learning methods significantly outperform logistic regression and provide fundamentally different interpretations of the role and influence of different predictive variables on voluntary carbon reporting. While most variables were not statistically significant in the logit results, a number of key proxies for financial performance, corporate governance, and corporate social responsibility have out-of-sample predictive power of the level of voluntary carbon reporting in the machine learning analysis.     

Predicting Corporate Failure in the UK: A Multidimensional Scaling Approach

Scaling techniques are proposed as a tool for the analysis and prediction of corporate failure. This approach, while keeping a strong theoretical basis, has the advantage of visualising the main features of the data in the form of statistical maps that lend themselves to intuitive interpretation. The maps contain directional statistics to help with interpretation. The methodology is demonstrated using a sample of UK industrial companies. A future‐dated holdout sample is also employed to illustrate how the Multidimensional Scaling technique can aid practitioners when assessing the financial health of a company.     

商业银行内部评级法的违约概率预测新方法——基于二值响应面板数据模型的研究

违约概率(PD)的计量是商业银行内部评级体系的基础,它对整个内部评级体系的效果有根本性的影响.目前各种违约概率计量方法最大的缺陷是忽略了时间效应的影响.本文提出的含随机截距项的二值响应面板数据模型是对现有各种方法的深入和完善.首先,它成功地将二值响应模型融合在面板数据分析中;其次,它特别考虑了因为观测时间不同而产生的时间效应,依此在模型中加入了随机截距项.实证结果表明,这一方法具有更好的解释能力和预测效果,是银行业进行内部评级工作理想的模型,因此具有很强的理论价值和实践意义.     

Counterparty Risk for Credit Default Swaps: Markov Chain Interacting Intensities Model with Stochastic Intensity

We analyze the counterparty risk for credit default swaps using the Markov chain model of portfolio credit risk of multiple obligors with interacting default intensity processes. The default correlation between the protection seller and underlying entity is modeled by an increment in default intensity upon the occurrence of an external shock event. The arrival of the shock event is a Cox process whose stochastic intensity is assumed to follow an affine diffusion process with jumps. We examine how the correlated default risks between the protection seller and the underlying entity may affect the credit default premium in a credit default swap.