电子商务模式研究

电子商务模式是网络企业生存和发展的核心,本文在对商务模式和电子商务模式的概念进行综述的基础上,分析和评价了国内外关于电子商务模式的七种分类方法,指出基于价值链的分类是企业进行电子商务模式选择和创新的较好依据,并给出了利用这种方法进行模式选择和创新的一般性思路.     

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.     

Tests of alternate models for the pricing of Korean Treasury bond futures contracts

It is known that KTB futures contracts are significantly underpriced when the model price is calculated using the ad hoc cost-of-carry model employed in industry. This paper examines whether this underpricing phenomenon is caused by using the wrong model to price the futures contracts. This paper documents that the difference between the model price and the market price of KTB futures decreases substantially if the correct term–structure-based model is used to estimate the model price of KTB futures. In addition, even though the underpricing phenomenon can be exploited to generate some trading profits, the profits cannot be regarded as arbitrage profits. Thus, we believe that the underpricing phenomenon is illusory, and that much of it can be attributed to the wrong model being used in the industry.     

An extention of Samuelson's warrant valuation model and its application to Japanese data

It is well known among warrant traders that the Black & Scholes model cannot be directly used for warrant and convertible bond valuation. In this paper, a new warrant valuation model based on both the Samuelson and the Barone-Adesi & Whaley model is proposed, and the model is applied to convertible bond valuation. Our model is an extension of Samuelson's perpertual warrant model, whose parameters depend on stock price, volatility, term to maturity and interest rate.     

Mortgage prepayment with an uncertain holding period

A discrete-time-option pricing model is developed to value a mortgage and its embedded prepayment option when the effective life of the mortgage is a random variable with a probability distribution of known parameters. The model can be applied when the borrower's ex ante expectation of his tenure follows any probability distribution bounded to the left at zero. The Gamma distribution is used to illustrate the model.The pricing model is further applied to determine the conditions under which financially motivated prepayment is optimal. The results show that the certainty model understates the Interest Rate Differential needed to justify prepayment (IRD) for short Expected Holding Period (EHP) borrowers and overstates the IRD for long EHP borrowers. When the EHP is relatively long, the certainty model provides relatively good estimates of IRD during the beginning years of the mortgage life. Under most other conditions, the estimates of the certainty holding period model are biased.     

A nonparametric approach to measuring the sensitivity of an asset’s return to the market

In the market model the return on an asset is modeled as a linear function of the return on a market index with slope parameter beta. The coefficient beta is often used as a measure of the sensitivity of the asset’s return to the market and to measure the component of the variance of the return that is explained by the market. However, both of these interpretations require the additional assumption that the error term in the market model has mean 0 conditional on the return on the market index, an assumption that is often difficult to verify in practice. In this paper, a nonparametric version of the market model is proposed that does not require such an assumption. This nonparametric model replaces the beta coefficient of the market model with a “beta curve” describing the relationship between the asset’s return and that of the market locally near a given value of the market return. The proposed model is applied to stock returns, as well as to returns on mutual funds. Corresponding tests of the market model are given and it is shown that the nonparametric model often provides an improvement over the standard parametric market model.     

A STATISTICAL MODEL OF CHANGES IN ASSET PRICES EMPLOYING INTRADAY DATA: A RECURSIVE APPROACH

This paper develops a statistical model of changes in asset prices employing intraday data. The procedure proposed in this paper is an alternative to the Hausman, Lo, and MacKinlay (1992) ordered probit model. Similar to the ordered probit model, our model also contains the linear regression model as a special case. However, compared to the ordered probit model, our specification is parsimonious. Parsimony does come at a cost, but for certain applications where, for example, a benchmark return is needed in intraday studies, there is value in terms of the computational effort required and the method's robustness to various empirical microstructure phenomena. The parsimony is achieved when statistical implications arising from intraday structural changes, which may due to such factors as concentrated trading patterns, are incorporated into a statistical model.     

Value at risk methodology of international index portfolio under soft conditions (fuzzy-stochastic approach)

The approach to modelling uncertainty of the international index portfolio by the value at risk (VAR) methodology under soft conditions by fuzzy-stochastic methodology is described in the paper. The generalised term uncertainty is understood to have two aspects: risk modelled by probability (stochastic methodology) and vagueness sometimes called impreciseness, ambiguity, softness is modelled by fuzzy methodology. Thus, hybrid model is called fuzzy-stochastic model. Input data for a stochastic model are unique distribution functions and crisp (real) data. Input data for fuzzy model are fuzzy numbers and crisp (real) data. Input data for hybrid model are fuzzy probability distribution functions, unique distribution functions, and crisp (real) data. Softly defined VAR model is constructed as hybrid model because it is supposed that the input data are difficult to determine as crisp numbers or as some unique distribution functions. Risk is modelled by stochastic methodology on the VAR basis and vagueness is modelled through the fuzzy numbers. The analytical delta normal VAR methodology for international index portfolio under soft conditions is described including illustrative example. It is shown, that methodology described could be considered to be generalised sensitivity analysis.     

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.     

A Regime-Switching Model of Long-Term Stock Returns

Abstract

In this paper I first define the regime-switching lognormal model. Monthly data from the Standard and Poor’s 500 and the Toronto Stock Exchange 300 indices are used to fit the model parameters, using maximum likelihood estimation. The fit of the regime-switching model to the data is compared with other common econometric models, including the generalized autoregressive conditionally heteroskedastic model. The distribution function of the regime-switching model is derived. Prices of European options using the regime-switching model are derived and implied volatilities explored. Finally, an example of the application of the model to maturity guarantees under equity-linked insurance is presented. Equations for quantile and conditional tail expectation (Tail-VaR) risk measures are derived, and a numerical example compares the regime-switching lognormal model results with those using the more traditional lognormal stock return model.     

Application of Heston’s Model to the Chinese Stock Market

This article applies Heston’s (1993) stochastic volatility model to the Chinese stock market indices and subsequently assesses its pricing performance. A two-step estimation procedure is adopted to calibrate Heston’s model. First, we find that the option price is affected by both the moneyness and the maturity. Second, Heston’s model is more likely to overprice options, whereas the BS model tends to underestimate options. Finally, Heston’s model, by employing volatility as a random process, significantly improves the pricing accuracy compared to the BS model. Therefore, Heston’s model is tractable to analyze the Chinese stock market indices, and there is volatility risk that must not be overlooked in the Chinese stock market.     

It takes a model to beat a model: Volatility bounds

Models like the CAPM and Fama–French three-factor models are commonly used as benchmarks for calculating cost of capital and evaluating portfolio performance, despite the empirical evidence to reject them. For many practical purposes, “it takes a model to beat a model.” In this paper we derive restrictions on models that could “beat” a bench-mark model but might still be misspecified. In these “takes-a-model-to-beat-a-model” (TMBM) bounds, model A beats model B if model A's quadratic form of pricing errors is smaller. The bounds generalize the Hansen–Jagannathan bound and distance measure. We use the TMBM bounds to evaluate various linear factor models and consumption-based models. The failure of the power utility model is much less extreme when it is compared with the CAPM and Fama–French model. For reasonable utility curvature, the Ferson–Constantinides model and Epstein–Zin model perform best among the consumption-based models, beating the model of Campbell and Cochrane, in which model the value of the persistence parameter that matches the time-series properties of aggregate stock market returns seems too low for cross-sectional asset pricing.     

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.     

A Modified GARCH Model with Spells of Shocks

The GARCH model is modified to capture the effect on volatilities of the consecutive number of positive or negative shocks. The new model is tested against the Shanghai Shcomp and Nikkei225 indices and found particularly useful in analyzing the Shcomp index. Similarly, the EGARCH model is extended along the same line as the GARCH model and is applied to the same sets of data. Stationarity of the new GARCH (1, 1) model is proved, and also derived is the asymptotic distribution of the quasi-maximum likelihood estimator.     

引入WFCM算法能提高信用违约测度模型准确率吗?——以沪深A股制造业上市公司为样本的实证研究

选取沪深 A股上市的制造业公司财务变量构建信用风险评价体系,在利用因子分析法对其进行维数约简后,采用数据挖掘技术和统计学方法对信用违约概率测度作了有价值的探索.模型包含两个阶段,聚类阶段采用加权模糊C均值聚类(WFCM)算法将样本聚成同质的类,使同簇样本更具代表性;违约测度阶段应用 Logistic回归方法分别对不同组样本进行测度.实证结果表明:在 Logistic 模型中引入 WFCM算法能显著提高预测样本的违约概率测度准确率;对于样本总体与 ST企业而言,其违约预测准确率比 Lo-gistic模型分别提高了10.7%和20%;ROC检验结果也说明 WFCM-Logistic模型具有更强适用性.     

A duscrete-time model of high-frequency stock returns

A model of intraday financial time series is developed. The model is a dynamic factor model consisting of two equations. First, a rate of return of a ‘stock’ in a single day is assumed to be generated by serveral common factors plus some additive erros (‘intraday equation’). Secondly, the joint distribution of those common factors is assumed to depend on the hidden state of the day, which fluctuates according to a Markov chain (‘day-by-day equation’). Together the equations compose a hidden Markov model.

We investigate properties of the model. Among them is a central limit theorem for cumulative returns, which agrees with the well-known empirical phenomenon in the stock markets that the distributions of longer-horizon returns are closer to the normal. We propose a two-step procedure consisting of the method of principal components and the EM algorithm to estimate the model parameters as well as the unboservable states. In addition, we propose a procedure for predicting intraday returns. Finally, the model is fitted to empirical data, the Standard&Poors 500 Index 5 min return data, to see if the model is capable of describing intraday movements of the index.     

A Comparative Analysis of American and French Financial Reporting Philosophies: The Case for International Accounting Standards

Two very dominant financial reporting systems compete today in the international accounting arena: the Anglo-Saxon model of financial reporting and the continental European model. Adopted and extended by the American financial reporting system, the Anglo-Saxon model is riding the wave of world market globalization and it is consolidating its presence on the international scene. On the other hand, the European model, at least in its Latin-German school, strives to maintain its own international position and tries even hardly to grow beyond its own traditional boundaries. Both models, rooted in a rich history and a strong cultural environment, differ at both substantive and philosophical levels. The impact of the industrial revolution and the protestant thinking is more visible in the Anglo-Saxon financial model, while the impact of law and the major wars is more noticeable in the continental European model. This paper uses the American financial reporting model to represent the Anglo Saxon model and the French reporting model to represent the dominant European model. It questions the ability of either of these models to respond to the needs of international users for financial information and advocates that the most viable alternative to these models lies in a strong commitment to worldwide standards.     

A Comparative Analysis of the Market Model and the Multiple-Factor Market Model

This study uses the framework of Patell (1979) to determine whether a multiple-factor market model or a traditional market model is more effective for information content studies of multinational firms. The multiple-factor market model is found to be superior to the traditional market model. Researchers wishing to test for information content for events with long event windows should consider using multiple factor models rather than the traditional market model, as it will increase the power of the test.     

Vergleich zweier Modelle zur Bewertung der Kapitalausstattung deutscher Lebensversicherungsunternehmen

The result of the comparison between the capital adequacy model published by the rating agency Standard & Poor’s (S&P) and the supervisory model of the German Insurance Association (GDV) points up the both models aim at measuring the German life insurers’ capital adequacy. The capital adequacy model is part of the Insurer Financial Strength Rating analyzing the financial security of insurance companies. The supervisory model is part of the recommandations by the GDV to reform the insurance control within the Solvency II project. Furthermore, the research includes the GDV’s proposal for the Solvency II standard model following the supervisory model as recommandation to the Solvency II project. The risk based capital computation’s analysis shows that the S&P model is more comprehensive on the assets and the supervisory model is more comprehensive on the liabilities. In addition, S&P differentiates in his model in a more quantitative way, the GDV in a more qualitative way. The standard model balances out the supervisory model’s lower number of quantitative differentiating factors.     

Termination of distressed residential mortgages: An empirical analysis

A sample of 209 distressed mortgages is used to analyze the terminations of distressed mortgages. An option-based model is compared to a traditional default model. Results show that the traditional model is statistically superior. However, the model's ability to identify a default is similar to that of the simpler option-based model. Alternative measures of borrower's equity are compared. Measuring borrower's equity using total debt more accurately explains default than using either the mortgage balance or the mortgage value.