银行信用评估模型效果的量化比较

信用评估模型能有效提高信用评估过程的科学性与结果的准确性.本文围绕主流信用评估模型在性能方面的差异化特征,基于德国信贷数据集、我国个人经营贷数据集与小微企业贷数据集,从六个模型性能评价维度对十二个代表性信用评估模型的拟合能力与泛化能力进行了深入研究.研究发现:(1)逻辑回归模型的总体性能最为优异,其次为判别分析、反向传播神经网络模型,其中逻辑回归模型与反向传播神经网络模型更适用于我国信贷场景;(2)基于无监督学习理论的自组织特征映射神经网络和k均值聚类模型,以及基于惰性学习理论的k最近邻模型的泛化能力较弱,表明各类有监督式主动学习模型更适用于解决信用评估问题;(3)模型理论与结构的复杂性并不必然能够使其在特定应用场景下获得较优的性能评价,结构简单、可解释性更强的模型往往稳健性更好.     

Bond and option pricing for interest rate model with clustering effects

This paper analyzes an interest rate model with self-exciting jumps, in which a jump in the interest rate model increases the intensity of jumps in the same model. This self-exciting property leads to clustering effects in the interest rate model. We obtain a closed-form expression for the conditional moment-generating function when the model coefficients have affine structures. Based on the Girsanov-type measure transformation for general jump-diffusion processes, we derive the evolution of the interest rate under the equivalent martingale measure and an explicit expression of the zero-coupon bond pricing formula. Furthermore, we give a pricing formula for the European call option written on zero-coupon bonds. Finally, we provide an interpretation for the clustering effects in the interest rate model within a simple framework of general equilibrium. Indeed, we construct an interest rate model, the equilibrium state of which coincides with the interest rate model with clustering effects proposed in this paper.     

Time varying betas and the unconditional distribution of asset returns

This paper draws attention to the fact that under standard assumptions the time varying betas model cannot capture the dynamics in beta. Conversely, evidence of time variation in beta using this model is equivalent to non-normality in the unconditional distribution of asset returns. Using the multivariate normal as a model for the joint distribution of returns on market indices and predetermined information variables, it is shown how to capture skewness and kurtosis in the unconditional distributions of asset returns. Under the assumptions of the model, asset returns are unconditionally distributed as an extended quadratic form (EQF) in normal variables. Expressions are given for the moment generating function and for the computation of the distribution and density functions. The market-timing model is derived formally using this model. The properties of bias when the standard linear betas model is used to estimate alpha when the correct model is the EQF are also investigated. It is shown that a different time varying betas model can arise as a consequence of portfolio selection. It is also shown that the predetermined information variables have the potential to account for the time series properties of returns, including heterogeneity of variance. An empirical study applies the model to returns on 46 UK bond funds. An analysis of the residuals shows that the model described in this paper is able to capture the dynamics of alpha and beta and properly account for other features of the time series of returns for 28 of these funds, of which 15 exhibit time variation in beta. The study reports the effect of the EQF model on the computation of VaR and CVaR and bias in the estimation of alpha.     

An Empirical Analysis of Exchange Ratio Determination Models for Merger: A Note

This paper examines the empirical validity of two exchange ratio determination models for merger, the Larson and Gonedes (LG) PE model and the Yagil dividend growth model. These two models formulate exchange ratios as a function of a different factor: expected post-merger price-earnings multiple and expected post-merger dividend growth, respectively. While the LG model has been tested in previous studies, the Yagil model has yet been subject to empirical testing. This paper finds empirical support for the LG model but finds weak support for the Yagil model. In particular, the results show that the number of stock mergers that result in wealth gains for both acquiring and target firms and hence conform to the rationality assumption of each model is substantially greater for the LG model than for the Yagil model. Regression analysis provides confirmatory evidence on the empirical validity of the LG model that PE-related variables play a more significant role in explaining the actual exchange ratios than growth-related variables.     

An Empirical Analysis of Exchange Ratio Determination Models for Merger: A Note

This paper examines the empirical validity of two exchange ratio determination models for merger, the Larson and Gonedes (LG) PE model and the Yagil dividend growth model. These two models formulate exchange ratios as a function of a different factor: expected post-merger price-earnings multiple and expected post-merger dividend growth, respectively. While the LG model has been tested in previous studies, the Yagil model has yet been subject to empirical testing. This paper finds empirical support for the LG model but finds weak support for the Yagil model. In particular, the results show that the number of stock mergers that result in wealth gains for both acquiring and target firms and hence conform to the rationality assumption of each model is substantially greater for the LG model than for the Yagil model. Regression analysis provides confirmatory evidence on the empirical validity of the LG model that PE-related variables play a more significant role in explaining the actual exchange ratios than growth-related variables.     

Stock returns,dividend yield,and book-to-market ratio

A dividend yield model has been widely used in previous research that relates stock market valuations to cash flow fundamentals. Given controversies about using dividends as a proxy for cash flows, a loglinear book-to-market model has recently been proposed. However, these models rely on the assumption that dividend yield and book-to-market ratio are both stationary, and empirical evidence for this is, at best, mixed. We develop a new model, the loglinear cointegration model, that explains future profitability and excess stock returns in terms of a linear combination of log book-to-market ratio and log dividend yield. The loglinear cointegration model performs better than the log dividend yield model and the log book-to-market model in terms of cross-equation restriction tests and forecasting performance comparisons. The superior performance of the loglinear cointegration model suggests that the linear combination may be a better indicator of intrinsic fundamentals than the dividend yield or the book-to-market ratio separately.     

组合预测模型在货运量预测中的应用

本文对灰色预测模型和ARIMA预测模型进行组合,建立了组合模型,并应用于货运量的预测,实证预测表明,组合模型的预测精度优于单一的预测模型,预测结果与实际货运量拟合较好。     

Smooth Transition ARCH Models: Estimation and Testing

In this paper, we suggest an extension of the ARCH model, the smooth-transition autoregressive conditional heteroskedasticity (STARCH) model. STARCH models endogenously allow for time-varying shifts in the parameters of the conditional variance equation. The most general form of the model that we consider is a double smooth-transition model, the STAR-STARCH model, which permits not only the conditional variance, but also the mean, to be a function of a smooth-transition term. The threshold ARCH model, the Markov-ARCH model and the standard ARCH model are special cases of our STARCH model. We also develop Lagrange multiplier tests of the hypothesis that the smooth-transition term in the conditional variance is zero. We apply our STARCH model to excess Treasury bill returns. We find some evidence of a smooth transition in excess returns, but in contrast to previous studies, we find almost no evidence of volatility persistence once we allow for smooth transitions in the conditional variance. Thus, the apparent persistence in the conditional variance reported by many researchers could be a mere statistical artifact. We conduct in-sample tests comparing STARCH models to nested competitors; these suggest that STARCH models hold promise for improved predictions. Finally, we describe further extensions of the STARCH model and suggest issues in finance to which they might profitably be applied.     

An International Analysis of Historical and Forecast Earnings in Accounting-Based Valuation Models

Abstract:  In this paper we examine whether the valuation properties of historical accounting amounts, namely earnings and equity book value, differ from those of forecasted earnings for firms in 17 developed countries classified into six accounting regimes. We compare the performance of a historical model and a residual-income forecast model for explaining security prices. The historical model uses the book value of equities and actual historical earnings and the forecast model uses the book value of equities and analysts' forecasts of earnings in the residual income for estimating the intrinsic value of the firm. The results suggest that book values, historical earnings or forecasted earnings are value relevant in most regimes and countries examined. The forecast model offers significantly greater explanatory power for security prices than the historical model in the Anglo-Saxon and North American countries, Japan, Germany, and three Nordic countries. The explanatory power of the historical model is similar to that of the forecast model in the Latin countries, two Nordic countries, and Switzerland. We find that the forecast model performs similarly to the historical model where financial analysts' forecasts are noisy and analysts are less active. Further results indicate that the forecasted earnings are more value-relevant than the historical earnings in countries with stronger investor protection laws, less conservative GAAP, greater income conservatism, and more transparent accounting systems.     

商业银行内部评级体系构建的模型风险研究

自上个世纪70年代以来,风险管理模型为银行的风险量化管理提供了工具,但也同时引致了模型风险。除少数银行外,大多数商业银行在实施内部评级法时都着力构建自己的风险管理模型体系。不论是直接引入外部模型还是自我构建模型,都必然存在模型风险的问题,其模型风险主要产生于基础模型和构建过程两个方面。由于中国正处于转轨经济阶段,因此中国商业银行在内部评级体系构建中的模型风险除了来源于基础模型、模型数据以外,模型使用环境的特殊性也是一个不可忽视的因素。压力测试和极端值方法是避免模型风险的有效技术手段,而风险文化的建设则是规避模型风险的根本所在。     

Option pricing using a binomial model with random time steps (A formal model of gamma hedging)

This paper provides a new option pricing model which justifies the standard industry implementation of the Black-Scholes model. The standard industry implementation of the Black-Scholes model uses an implicit volatility, and it hedges both delta and gamma risk. This industry implementation is inconsistent with the theory underlying the derivation of the Black-Scholes model. We justify this implementation by showing that these adhoc adjustments to the Black-Scholes model provide a reasonable approximation to valuation and delta hedging in our new option pricing model.     

基于沪铜期货的套期保值比率与效率比较的实证分析

本文以沪铜期货的多头套期保值为研究对象,分别利用OLS模型、ECM模型和GARCH模型对一月期铜和三月期铜的套期保值比例及保值效果进行了分析,发现OLS模型对一月期铜的套期保值效果要优于其他模型的保值效果,而ECM模型和GARCH模型在三月期铜的套期保值方面显示的效果更好。这说明在一般情况下,具有动态特征的计量模型适合于较长的期货合约,其套期保值效果更好。     

Option valuation with long-run and short-run volatility components

This paper presents a new model for the valuation of European options, in which the volatility of returns consists of two components. One is a long-run component and can be modeled as fully persistent. The other is short-run and has a zero mean. Our model can be viewed as an affine version of Engle and Lee [1999. A permanent and transitory component model of stock return volatility. In: Engle, R., White, H. (Eds.), Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W.J. Granger. Oxford University Press, New York, pp. 475–497], allowing for easy valuation of European options. The model substantially outperforms a benchmark single-component volatility model that is well established in the literature, and it fits options better than a model that combines conditional heteroskedasticity and Poisson–normal jumps. The component model's superior performance is partly due to its improved ability to model the smirk and the path of spot volatility, but its most distinctive feature is its ability to model the volatility term structure. This feature enables the component model to jointly model long-maturity and short-maturity options.     

Can Markov switching models replicate chartist profits in the foreign exchange market?

In this paper we show that the Markov switching model is a relevant statistical alternative to the classical martingale model for exchange rates. By extending the standard Markov switching model we decisively reject the martingale model. Moreover, the model generates autocorrelations and linear structures in line with what is observed in reality. Subsequently, we test whether this model can explain chartist profits. We find that the extended Markov switching model is able to explain the profitability of a simple MA-30 rule. Finally, we decompose the profitability of the MA-30 rule into a linear and nonlinear part. We find that, although the implied linear structure of the Markov model explains a substantial part of the profitability, part of the profits of the MA-30 rule can be attributed to the specific nonlinearities implicit in the Markov model.     

Are RiskMetrics forecasts good enough? Evidence from 31 stock markets

Academic research has highlighted the inherent flaws within the RiskMetrics model and demonstrated the superiority of the GARCH approach in-sample. However, these results do not necessarily extend to forecasting performance. This paper seeks answer to the question of whether RiskMetrics volatility forecasts are adequate in comparison to those obtained from GARCH models. To answer the question stock index data is taken from 31 international markets and subjected to two exercises, a straightforward volatility forecasting exercise and a Value-at-Risk exceptions forecasting competition. Our results provide some simple answers to the above question. When forecasting volatility of the G7 stock markets the APARCH model, in particular, provides superior forecasts that are significantly different from the RiskMetrics models in over half the cases. This result also extends to the European markets with the APARCH model typically preferred. For the Asian markets the RiskMetrics model performs well, and is only significantly dominated by the GARCH models for one market, although there is evidence that the APARCH model provides a better forecast for the larger Asian markets. Regarding the Value-at-Risk exercise, when forecasting the 1% VaR the RiskMetrics model does a poor job and is typically the worst performing model, again the APARCH model does well. However, forecasting the 5% VaR then the RiskMetrics model does provide an adequate performance. In short, the RiskMetrics model only performs well in forecasting the volatility of small emerging markets and for broader VaR measures.     

A tractable yield-curve model that guarantees positive interest rates

Yield-curve models suggested previously in the literature seem always to make a tradeoff between analytical tractability and a realistic behavior of the interest rates. In this paper we analyze a model that combines both features into one model: the interest rates are always positive and the model has a rich analytical structure. Not only is our model theoretically appealing, we also provide empirical evidence that our model can fit observed cap and floor prices better than the Hull-White model.The author is grateful to Stephen Figlewski, Ton Vorst, Carien Dam, Douglas Bongartz-Renaud, participants of the Second International Conference on Computing in Finance and Economics in Geneva and especially Marti Subrahmanyam and two anonymous referees for comments and helpful suggestions.     

Evaluating cross-sectional forecasting models for implied cost of capital

The computation of implied cost of capital (ICC) is constrained by the lack of analyst forecasts for half of all firms. Hou et al. (J Account Econ 53:504–526, 2012, HVZ) present a cross-sectional model to generate forecasts in order to compute ICC. However, the forecasts from the HVZ model perform worse than those from a naïve random walk model and the ICCs show anomalous correlations with risk factors. We present two parsimonious alternatives to the HVZ model: the EP model based on persistence in earnings and the RI model based on the residual income model from Feltham and Ohlson (Contemp Account Res 11:689–732, 1996). Both models outperform the HVZ model in terms of forecast bias, accuracy, earnings response coefficients, and correlations of the ICCs with future returns and risk factors. We recommend that future research use the RI model or the EP model to generate earnings forecasts.     

典型事实、极值理论与金融市场动态风险测度研究

本文在金融市场典型事实约束下,运用ARFIMA模型对金融市场条件收益率建模,运用GARCH、GJR、FIGARCH、APARCH、FIAPARCH等5种模型对金融波动率进行建模,进而运用极值理论(EVT)对标准收益的极端尾部风险建模来测度各股市的动态风险,并用返回测试(Back-testing)方法检验模型的适应性。实证结果表明,总的来说,FIAPARCH-EVT模型对各个市场具有较强的适应性,风险测度能力较为优越。进一步,本文在ARFIMA-FIAPARCH模型下,假定标准收益分别服从正态分布(N)、学生t分布(st)、有偏学生t分布(skst)、广义误差分布(GED)共4种分布,对各股市的动态风险测度的准确性进行检验,并和EVT方法的测度结果进行对比分析。结果表明,EVT方法风险测度能力优于其他方法,有偏学生t分布假设下的风险测度模型虽然略逊于EVT方法,但也不失为一种较好的方法;ARFIMA-FI-APARCH-EVT不仅在中国大陆沪深股市表现最为可靠,而且在其他市场也表现出同样的可靠性。     

Trading Frictions and House Price Dynamics

We model liquidity in housing markets. The model provides a simple characterization for the joint process of prices, sales, and inventory. We compare the implications of the model to certain properties of housing markets. The model can generate the large price changes and the positive correlation between prices and sales that we see in the data. Unlike the data, prices are negatively autocorrelated and high inventory predicts price appreciation. We investigate several amendments to the model. Informational frictions show promise.     

Tactical Industry Allocation and Model Uncertainty

We use Bayesian model averaging to analyze industry return predictability in the presence of model uncertainty. The posterior analysis shows the importance of inflation and earnings yield in predicting industry returns. The out‐of‐sample performance of the Bayesian approach is, in general, superior to that of other statistical model selection criteria. However, the out‐of‐sample forecasting power of a naive i.i.d. forecast is similar to the Bayesian forecast. A variance decomposition into model risk, estimation risk, and forecast error shows that model risk is less important than estimation risk.