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.     

Banking,debt, and currency crises in developed countries: Stylized facts and early warning indicators

We construct and explore a new quarterly dataset covering crisis episodes in 40 developed countries over 1970–2010. First, we present stylized facts on banking, debt, and currency crises. Using panel vector autoregression we find that banking and debt crises are interrelated and both typically precede currency crises, but not vice versa. Banking crises are the most costly in terms of the overall output loss, and output takes about six years to recover. Second, on a reduced sample we try to identify early warning indicators of crises specific to developed economies, accounting for model uncertainty by means of Bayesian model averaging. The most consistent result across the various specifications and time horizons is that significant growth of domestic private credit precedes banking crises, while rising money market rates and global corporate spreads are also leading indicators worth monitoring. For currency crises, we also corroborate the role of rising domestic private credit and money market rates and detect the relevance of domestic currency overvaluation. The role of other indicators differs according to the type of crisis and the warning horizon selected, but it mostly seems easier to find reliable predictors at a horizon shorter than two years. Early warning indicators of debt crises are difficult to uncover due to the low occurrence of such episodes in our dataset. We also employ a signaling approach to derive the threshold value for the best single indicator (domestic private credit), and finally we provide a composite early warning index that further increases the usefulness of the model.     

An empirical comparison of GARCH option pricing models

Recent empirical studies have shown that GARCH models can be successfully used to describe option prices. Pricing such contracts requires knowledge of the risk neutral cumulative return distribution. Since the analytical forms of these distributions are generally unknown, computationally intensive numerical schemes are required for pricing to proceed. Heston and Nandi (2000) consider a particular GARCH structure that permits analytical solutions for pricing European options and they provide empirical support for their model. The analytical tractability comes at a potential cost of realism in the underlying GARCH dynamics. In particular, their model falls in the affine family, whereas most GARCH models that have been examined fall in the non-affine family. This article takes a closer look at this model with the objective of establishing whether there is a cost to restricting focus to models in the affine family. We confirm Heston and Nandi's findings, namely that their model can explain a significant portion of the volatility smile. However, we show that a simple non affine NGARCH option model is superior in removing biases from pricing residuals for all moneyness and maturity categories especially for out-the-money contracts. The implications of this finding are examined. JEL Classification G13     

What drives U.S. financial sector volatility? A Bayesian model averaging perspective

We investigate the driving forces behind the quarterly stock price volatility of firms in the U.S. financial sector over the period from 1990 to 2017. The driving forces represent a set of 28 economic indicators that are routinely used to detect financial instability and crises and correspond to the development of the financial, monetary, real, trade and fiscal sector as well as to the development of the bond and equity markets. The dimensionality and model choice uncertainty are addressed using Bayesian model averaging, which led to the identification of only seven variables that tend to systematically drive the stock price volatility of financial firms in the U.S.: housing prices, short-term interest rates, net national savings, default yield spread, and three credit market variables. We also confirm that our results are not an artefact of volatility associated with market downturns (for negative semi-volatility), as the results are similar even when market volatility is associated with market upsurge (positive semi-volatility). Given the identified drivers, our results provide supporting empirical evidence that dampening credit cycles might lead to decreased volatility in the financial sector.     

Linear Accounting Valuation When Abnormal Earnings Are AR(2)

The Ohlson (1995) model assumes that abnormal earnings follow an AR(1) process primarily for reasons of mathematical tractability. However, the empirical literature on the Garman and Ohlson (1980) model finds that the data support an AR(2) lag structure for earnings, book values and dividends. Moreover, the AR(2) process encompasses a far richer variety of time series patterns than does the AR(1) process and includes the AR(1) process as a special case. This paper solves the Ohlson model directly for an AR(2) abnormal earnings dynamic. The model is estimated on a time series firm-level basis following the approach used by Myers (1999). It is found that, like the Ohlson AR(1) model, the Ohlson AR(2) model severely underestimates market prices even relative to book values. These results further bring into question the empirical validity of the Ohlson model.     

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.     

变量序别化在信用评分模型中的应用研究

本文通过将连续数值变量进行序别化转换赋值,并基于这些变量建立Log- it信用评分模型,通过使用统计量AUC值与条件熵比率来检验序别化转换前后所建立回归模型的违约预测力。结果发现,连续数值变量经序别化转换后可提高模型的违约预测力及其韧性。     

经济学教育改革：国际国内背景与改革路径

经济学作为一级学科,在社会科学体系中居于基础性地位并发挥着辐射带动作用。经济学专业教育模式与水平直接影响着树状层级中的二级学科、三级学科乃至更多层级的学科发展,也关系着毕业生的行为模式和政策取向,而这恰恰是最深远的社会经济影响。但这种影响并未得到应有重视,综观国内国际经济学教育,不仅出现了趋同化,而且危机涌现。经济学教育改革迫在眉睫,包容、兼容的经济学体系与培养计划的构建成为改革的方向,而应用流程化设计思想和波特五力竞争模型理念,对经济学专业教育进行全面诊断与优化重组,创新人才培养模式则是可行路径。     

Reduced-form valuation of callable corporate bonds: Theory and evidence

We develop a reduced-form approach for valuing callable corporate bonds by characterizing the call probability via an intensity process. Asymmetric information and market frictions justify the existence of a call-arrival intensity from the market's perspective. Our approach both extends the reduced-form model of Duffie and Singleton (1999) for defaultable bonds to callable bonds and captures some important differences between call and default decisions. A comprehensive empirical analysis of callable bonds using both our model and the more traditional American option approach for valuing callable bonds shows that the reduced-form model fits callable bond prices well and that it outperforms the traditional approach both in- and out-of-sample.     

西方资产定价理论中的实体经济因素考察——以CAPM模型与B-S期权定价模型为例

本文根据马克思主义经济学有关实体经济因素决定虚拟资本价格的基本原理,分析和评价了CAPM与B-S模型;认为CAPM模型的建模思路存在重大错误;同时还发现,利息率是虚拟资本价格决定中与实体经济连接的重要桥梁,但在虚拟资本价格决定中,仅有利息率一个指标并不能充分反映实体经济的决定作用;本文还因B-S模型建模思路充分考虑了实体经济因素,而从马克思主义经济学角度给予客观评价。     

我国零售业国际化战略的研究——基于钻石模型的分析

当前,国际化已成为世界零售业的一大发展趋势,其对于在全球范围内实现资源优化配置、扩大规模经济效益,提高国际竞争力有着极其重要的意义。在我国零售业全面对外开放的环境下,我国零售业面临着巨大的机遇与挑战。本文试图运用波特的"钻石模型"理论对我国零售业进行分析,进而提出我国零售业在国际化经营中应该采取的战略措施。     

Does sentiment affect stock returns? A meta-analysis across survey-based measures

We are the first to meta-analyze the literature on the relationship between sentiment and stock returns, a topic that reacts to the history of systemic events causing asset bubbles in financial markets. We focus on three questions — whether the literature is biased; what is the “true effect” beyond this bias; and what are the key determinants of the variance among the estimates in the literature. To answer those questions we collect 1311 point estimates from 30 primary studies and use state-of-art meta-analytical approaches. Both linear and non-linear tests for publication bias suggest that the “true effect” of an improvement in sentiment is non-negligible and negative. In the majority of specifications, researchers tend to report this effect as being much stronger than it actually is. Next, using Bayesian model averaging we show that the effect of sentiment on future returns is significantly stronger for individual investors than for large institutions, and in US stock markets compared with European ones. The effect also depends on several data and model characteristics.