房地产业呼唤多元化融资模式

众所周知,房地产开发需要引进的资金额度非常大,单纯依靠信托或银行贷款都不能完全解决问题.而我国目前的房地产开发企业的融资结构具有明显的贷款依赖型特征.资金来源主要是自筹资金、银行贷款以及包括商品房预售款、施工企业垫付的工程施工款在内的其他资金来源.从整个行业来看,国内房地产开发的资金80%来自银行(包括预售款)是不争的事实.正因为房地产市场发展过分依赖于银行信贷,从而也就形成了以银行信贷为主的单一的房地产金融模式.可以说,尽管这种融资模式的生成有其现实之原因,但我们却不可忽略它将给国内金融市场及社会经济带来的风险.     

Generic market models

Currently, there are two market models for valuation and risk management of interest rate derivatives: the LIBOR and swap market models. We introduce arbitrage-free constant maturity swap (CMS) market models and generic market models featuring forward rates that span periods other than the classical LIBOR and swap periods. We develop generic expressions for the drift terms occurring in the stochastic differential equation driving the forward rates under a single pricing measure. The generic market model is particularly apt for pricing of, e.g., Bermudan CMS swaptions and fixed-maturity Bermudan swaptions.     

A comparison of alternative bankruptcy prediction models

Early models of bankruptcy prediction employed financial ratios drawn from pre-bankruptcy financial statements and performed well both in-sample and out-of-sample. Since then there has been an ongoing effort in the literature to develop models with even greater predictive performance. A significant innovation in the literature was the introduction into bankruptcy prediction models of capital market data such as excess stock returns and stock return volatility, along with the application of the Black–Scholes–Merton option-pricing model. In this note, we test five key bankruptcy models from the literature using an up-to-date data set and find that they each contain unique information regarding the probability of bankruptcy but that their performance varies over time. We build a new model comprising key variables from each of the five models and add a new variable that proxies for the degree of diversification within the firm. The degree of diversification is shown to be negatively associated with the risk of bankruptcy. This more general model outperforms the existing models in a variety of in-sample and out-of-sample tests.     

A comparison of models of long-term inflationary expectations

This paper compares several models of long-term inflationary expectations, including time series models, models drawn from interest rate relationships, and a structural model developed from a portfolio balance framework. Their within-sample performance over 1961–1974 and out-of-sample performance over periods covering 1975–1982 are compared. Evaluation criteria include accuracy of forecasts and ability to capture changes in the trend of long-term inflation.The structural model is judged best. Its forecasts are generally the most accurate and it performs well in periods of rising and falling inflation, an attribute not present in other models.     

LIBOR and swap market models and measures

A self-contained theory is presented for pricing and hedging LIBOR and swap derivatives by arbitrage. Appropriate payoff homogeneity and measurability conditions are identified which guarantee that a given payoff can be attained by a self-financing trading strategy. LIBOR and swap derivatives satisfy this condition, implying they can be priced and hedged with a finite number of zero-coupon bonds, even when there is no instantaneous saving bond. Notion of locally arbitrage-free price system is introduced and equivalent criteria established. Stochastic differential equations are derived for term structures of forward libor and swap rates, and shown to have a unique positive solution when the percentage volatility function is bounded, implying existence of an arbitrage-free model with such volatility specification. The construction is explicit for the lognormal LIBOR and swap “market models”, the former following Musiela and Rutkowski (1995). Primary examples of LIBOR and swap derivatives are discussed and appropriate practical models suggested for each.     

Manipulation in market order models

We analyze a dynamic market order model similar to Kyle (Econometrica 53 (1985) 1315). We show that when the market faces uncertainty about the existence of the insider in the market, the equilibrium outcome changes in a significant way. In particular, the insider manipulates (i.e., trades in the wrong direction and undertakes short term losses) in every equilibrium, given a long enough horizon, and independently of the precise nature of noise trading in the market.     

A comparison of MAD and CVaR models with real features

In this paper we consider two different mixed integer linear programming models for solving the single period portfolio selection problem when integer stock units, transaction costs and a cardinality constraint are taken into account. The first model has been formulated by using the maximization of the worst conditional expectation as objective function. The second model is based on the maximization of the safety measure corresponding to the mean absolute deviation. Extensive computational results are provided to compare the financial characteristics of the optimal portfolios selected by the two models on real data from European stock exchange markets. Some simple heuristics are also introduced that provide efficient and effective solutions when an optimal integer solution cannot be found in a reasonable amount of time.     

A latent factor model for the Chinese stock market

We propose a new latent factor model for the Chinese stock market based on an instrumented principal component analysis (IPCA). Compared with other common asset pricing models, the new latent factor model explains a larger proportion of individual and portfolio return variation and shows significant out-of-sample predictability. The long-short investment strategy formed by the IPCA factor also presents the highest average return and Sharpe ratio. Subsample and different horizon results are robust. Market beta, profitability and momentum emerge as the most important characteristics in driving the latent factors. We also provide evidence on the economic grounds of the new latent factor model.     

A comparison of diffusion models of the term structure

A number of different continuous time approaches that have been developed to model the term structure of interest rates are examined. These techniques span the interest rate literature over the last 20 years or so, and are the most commonly used among both academics and practitioners. We view this paper as a reference for the different term structure models, aiming to bring together the three most commonly used approaches, emphasizing their differences, analysing their respective advantages and disadvantages, and with explicit representations where they exist for prices of discount bonds.     

Forecasting market turbulence using regime-switching models

We propose an early warning system to timely forecast turbulence in the US stock market. In a first step, a Markov-switching model with two regimes (a calm market and a turbulent market) is developed. Based on the time series of the monthly returns of the S&P 500 price index, the corresponding filtered probabilities are successively estimated. In a second step, the turbulent phase of the model is further specified to distinguish between bullish and bearish trends. For comparison only, a Markov-switching model with three states (a calm market, a turbulent bullish market, and a turbulent bearish market) is examined as well. In a third step, logistic regression models are employed to forecast the filtered probabilities provided by the Markov-switching models. A major advantage of the presented modeling framework is the timely identification of the factors driving the different phases of the capital market. In a fourth step, the early warning system is applied to an asset management case study. The results show that explicit consideration of the models’ signals yields better portfolio performance and lower portfolio risk compared to standard buy-and-hold and constant proportion portfolio insurance strategies.     

On a class of diverse market models

A market model in Stochastic Portfolio Theory is a finite system of strictly positive stochastic processes. Each process represents the capitalization of a certain stock. If at any time no stock dominates almost the entire market, which means that its share of total market capitalization is not very close to one, then the market is called diverse. There are several ways to outperform diverse markets and get an arbitrage opportunity, and this makes these markets interesting. A feature of real-world markets is that stocks with smaller capitalizations have larger drift coefficients. Some models, like the volatility-stabilized model, try to capture this property, but they are not diverse. In an attempt to combine this feature with diversity, we construct a new class of market models. We find simple, easy-to-test sufficient conditions for them to be diverse and other sufficient conditions for them not to be diverse.     

中国金融市场信用风险模型研究与应用

文章运用中国金融市场和原银行信贷登记系统数据及人民银行组织的信用评级等数据资源,在金融工程理论和技术方法创新的基础上,借鉴国际信用风险模型中违约模式代表——KMV模型原理,实证建立由判别函数和违约强度共同构成的中国金融市场违约预警模型;借鉴国际信用风险模型中盯市模式代表——CreditMetrics模型原理,使用蒙特卡罗模拟方法实证建立中国金融市场信用组合计量模型;探索这两类模型在中国信贷市场、外汇市场、货币市场和债券市场风险管理实务中的应用;并在此基础上提出了政策性建议。     

Testing conditional factor models: A nonparametric approach

Some recent studies of conditional factor models do not specify conditioning information but use data from small windows to estimate the time series of conditional alphas and betas. In this paper, we propose a nonparametric method using an optimal window to estimate time-varying coefficients. In addition, we offer two empirical tests of a conditional factor model. Using our new method, we examine the performance of the conditional CAPM and the conditional Fama-French three-factor model in explaining the return variations of portfolios sorted by size, book-to-market ratios, and past returns, for which recent literature has generated controversial results. We find that, although in general the conditional FF model outperforms the conditional CAPM, both models fail to explain well-known asset-pricing anomalies. Moreover, for both models, the failure is more pronounced for the equally-weighted portfolios than for the value-weighted ones.     

A factor analysis approach to measuring European loan and bond market integration

By using an existing and a new convergence measure, this paper assesses whether bank loan and bond interest rates are converging for the non-financial corporate sector across the euro area. Whilst we find evidence for complete bond market integration, the market for bank loans remains segmented, albeit to various degrees depending on the type and size of the loan. Factor analysis reveals that rates on large loans and small loans with long rate fixation periods have weakly converged in the sense that, up to a fixed effect, their evolution is driven by common factors only. In contrast, the price evolution of small loans with short rate fixation periods is still affected by country-specific dynamic factors. There are few signs that bank loan rates are becoming more uniform with time.     

A comparison of some structural models of private information arrival

We show that the PIN and the Duarte and Young (2009) (APIN) models do not match the variability of noise trade in the data and that this limitation has severe implications for how these models identify private information. We examine two alternatives to these models, the Generalized PIN model (GPIN) and the Odders-White and Ready (2008) model (OWR). Our tests indicate that measures of private information based on the OWR and GPIN models are promising alternatives to the APIN’s Adj.PIN and PIN.     

Oil market modelling: A comparative analysis of fundamental and latent factor approaches

We formally compare fundamental factor and latent factor approaches to oil price modelling. Fundamental modelling has a long history in seeking to understand oil price movements, while latent factor modelling has a more recent and limited history, but has gained popularity in other financial markets. The two approaches, though competing, have not formally been compared as to effectiveness. For a range of short- medium- and long-dated WTI oil futures we test a recently proposed five-factor fundamental model and a Principal Component Analysis latent factor model. Our findings demonstrate that there is no discernible difference between the two techniques in a dynamic setting. We conclude that this infers some advantages in adopting the latent factor approach due to the difficulty in determining a well specified fundamental model.     

A comparison of models for pricing interest rate derivative securities

This paper looks at the different approaches and different models that have been developed to value interest rate-dependent securities, providing a survey of pricing procedures which are based on mathematical models of the term structure. It can be viewed as a reference for the different interest rate models with explicit representations, where they exist, for prices of derivative instruments and an an analysis of their respective advantages and disadvantages.     

Autoregressive stochastic volatility models with heavy-tailed distributions: A comparison with multifactor volatility models

This paper examines two asymmetric stochastic volatility models used to describe the heavy tails and volatility dependencies found in most financial returns. The first is the autoregressive stochastic volatility model with Student's t-distribution (ARSV-t), and the second is the multifactor stochastic volatility (MFSV) model. In order to estimate these models, the analysis employs the Monte Carlo likelihood (MCL) method proposed by Sandmann and Koopman [Sandmann, G., Koopman, S.J., 1998. Estimation of stochastic volatility models via Monte Carlo maximum likelihood. Journal of Econometrics 87, 271–301.]. To guarantee the positive definiteness of the sampling distribution of the MCL, the nearest covariance matrix in the Frobenius norm is used. The empirical results using returns on the S&P 500 Composite and Tokyo stock price indexes and the Japan–US exchange rate indicate that the ARSV-t model provides a better fit than the MFSV model on the basis of Akaike information criterion (AIC) and the Bayes information criterion (BIC).