经济增长推动房地产市场发展的实证研究——以上海为例

租金、售价等价格指标及其周期性波动,常用于衡量房地产市场的发展程度。在运用三部门模型分析区域经济增长如何影响区域房地产市场发展的基础上,提出上海经济增长推高房地产整体市场和住宅子市场的租金、售价的假设。选取5项指标,利用上海1998—2012年的城市统计年鉴数据,通过格兰杰因果检验方法进行实证检验。结果表明,上海整体房地产市场和住宅子市场的租金增长,均以经济增长为格兰杰因;两个市场的售价与经济增长无法体现因果关系。因此,租金是比价格更有效、更稳定的市场指标。     

Do Power GARCH models really improve value-at-risk forecasts?

Traditional heteroskedastic models either rely on the specification of the conditional variance as in Bollerslev (1986) or on a direct modeling of the conditional standard deviation as in Taylor (1986). With its endogenous estimation of the optimal power transformation, the Power GARCH (PGARCH) of Ding, Granger, and Engle (1993) represents a flexible alternative that also nests the previous competing families. Building on a “dynamic” estimation and out-of-sample tests, the current paper undertakes a comparison of the three models in a value-at-risk setting. Despite existing fluctuations in the optimal power transformation obtained with the Ding, Granger, and Engle model, our empirical investigations suggest that the parameter is rarely found different from one or two. Although the volatility dynamics may switch from Taylor's to Bollerslev's specification during the life of the future contract, the measures of accuracy and efficiency used to assess the performance of VaR forecasts indicate that the additional flexibility brought by the PGARCH model provides little, if any, improvement for risk management. *** DIRECT SUPPORT *** A00DH023 00004     

Another look at Swedish business cycles, 1861–1988

The linearity of nine long Swedish macroeconomic time series, whose business cycle properties were discussed by Englund, Persson, and Svensson (1992), is tested and rejected for all but two. Non-linear (STAR) models are estimated, and their properties are investigated. Business cycle frequency variation does not seem to be constant over time for all series; it is difficult to find a ‘Swedish business cycle’. Pairwise Granger non-causality tests are adapted to the STAR case, and non-causality is tested. The results point at strong temporal interactions and indicate that the functional form (linear or STAR) strongly affects the outcome of these tests. Copyright © 1999 John Wiley & Sons, Ltd.     

A re-examination of the “zero is enough” hypothesis in the emergence of financial stylized facts

In recent years, a growing literature has claimed that the market microstructure is sufficient to generate the so-called stylized facts without any reference to the behaviour of market players. Indeed, qualitative stylized-facts can be generated with zero-intelligence traders (ZITs) but we stress that they are without any quantitative predictive power. In this paper we show that in most of the cases, such qualitative stylized facts hide unrealistic price motions at the intraday level and ill-calibrated return processes as well. To generate realistic price motions and return series with adequate quantitative values is out-of-reach using pure ZIT populations. To do so, one must increasingly constrain agents?? choices to a point where it is hard to claim that their behaviour is completely random. In addition we show that even with highly constrained ZIT agents, one cannot reproduce real time series from these. Except in a few cases, first order moments of ZITs never equal real data ones. We therefore claim that stylized facts produced by means of ZIT agents are useless for financial engineering.     

Time series properties of ARCH processes with persistent covariates

We investigate the time series properties of a volatility model, whose conditional variance is specified as in ARCH with an additional persistent covariate. The included covariate is assumed to be an integrated or nearly integrated process, with its effect on volatility given by a wide class of nonlinear volatility functions. In the paper, such a model is shown to generate many important characteristics that are commonly observed in financial time series. In particular, the model yields persistence in volatility, and also well predicts leptokurtosis. This is true for any type of volatility functions considered in the paper, as long as the covariate is integrated or nearly integrated. Stationary covariates cannot produce important characteristics observed in many financial time series. We present two empirical applications of the model, which show that the default premium (the yield spread between Baa and Aaa corporate bonds) affects stock return volatility and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that the model generally outperforms GARCH and FIGARCH at relatively lower frequencies.     

Non-causality in bivariate binary time series

In this paper we develop a dynamic discrete-time bivariate probit model, in which the conditions for Granger non-causality can be represented and tested. The conditions for simultaneous independence are also worked out. The model is extended in order to allow for covariates, representing individual as well as time heterogeneity. The proposed model can be estimated by Maximum Likelihood. Granger non-causality and simultaneous independence can be tested by Likelihood Ratio or Wald tests. A specialized version of the model, aimed at testing Granger non-causality with bivariate discrete-time survival data is also discussed. The proposed tests are illustrated in two empirical applications.     

Money‐output Causality Revisited – A Bayesian Logistic Smooth Transition VECM Perspective*

This article proposes a Bayesian approach to examining money‐output causality within the context of a logistic smooth transition vector error correction model. Our empirical results provide substantial evidence that the postwar US money‐output relationship is nonlinear, with regime changes mainly governed by the output growth and price levels. Furthermore, we obtain strong support for nonlinear Granger causality from money to output, although there is also some evidence for models indicating that money is not Granger causal or long‐run causal to output.     

On the plausibility of sunspot equilibria

This study examines the plausibility of the emergence of sunspot equilibria in an agent-based artificial stock market. Using the agent-based model, we make the sunspots explicit so that we can test, e.g., by means of the Granger causality test, whether purely extrinsic uncertainty can influence price dynamics. In addition, through agent-based simulation, the coordination process, which is mainly driven by genetic programming, becomes observable, which enables us to analyze what agents perceive and whether they believe in sunspots. By manipulating different control variables, three series of experiments are conducted. Generally speaking, the chances of observing “sunspot equilibria” in this agent-based artificial stock market are small. However, the sunspot believers can never be driven out of the market. Nevertheless, they are always outnumbered by fundamental believers, which is evidence that the market as collective behavior is rational. We also find that lengthening the time horizon will make it difficult for sunspot believers to survive.     

Measuring and testing Granger causality over the spectrum: An application to European production expectation surveys

Decomposing Granger causality over the spectrum allows us to disentangle potentially different Granger causality relationships over different frequencies. This may yield new and complementary insights compared to traditional versions of Granger causality. In this paper, we compare two existing approaches in the frequency domain, proposed originally by Pierce [Pierce, D. A. (1979). R-squared measures for time series. Journal of the American Statistical Association, 74, 901–910] and Geweke [Geweke, J. (1982). Measurement of linear dependence and feedback between multiple time series. Journal of the American Statistical Association, 77, 304–324], and introduce a new testing procedure for the Pierce spectral Granger causality measure. To provide insights into the relative performance of this test, we study its power properties by means of Monte Carlo simulations. In addition, we apply the methodology in the context of the predictive value of the European production expectation surveys. This predictive content is found to vary widely with the frequency considered, illustrating the usefulness of not restricting oneself to a single overall test statistic.     

What factors drive the price–rent ratio for the housing market? A modified present-value analysis

We consider which factors determined the price–rent ratio for the housing market in 18 U.S. metropolitan statistical areas (MSAs) and at the national level over the period of 1975–2014. Based on a present-value framework, our proposed empirical model separates the price–rent ratio for a given market into unobserved components related to the expected real rent growth and the expected housing return, but is modified from standard present-value analysis by also including a residual component that captures non-stationary deviations of the price–rent ratio from its present-value level. Estimates for the modified present-value model suggest that the present-value residual (PVR) component is always important and sometimes very large at the national and MSA levels, especially for MSAs that have experienced frequent booms and busts in the housing market. In further analysis, we find that house prices in MSAs that have larger PVR components are more sensitive to mortgage rate changes. These are also the MSAs with less elastic housing supply. Also, comparing our results with a recent statistical test for periodically-collapsing bubbles, we find that MSAs with large estimated PVR components are the same MSAs that test positively for explosive sub-periods in their price–rent ratios, especially during the 2005–2007 subsample. Our approach allows us to estimate the correlation between shocks to expected rent growth, the expected housing return, and the PVR component. We find that the expected housing return and movements in the PVR component are highly positively correlated implying an impact of the expected housing return on house prices that is amplified from what a standard present-value model would imply. Our results also show that most of the variation in the present-value component of the price–rent ratio arises due to the variation in the expected housing return.     

我国通货膨胀与房地产价格的相关性研究

通货膨胀和房地产价格都是当前中国国民经济发展中遇到的热点问题,文章从探讨两者相关性出发,首先定性地讨论它们之间的有关理论和关系,然后通过实际数据,采用房地产价格、通货膨胀率、产出和利率等变量对其进行数据处理,再进行协整检验分析,由Granger因果检验得出我国房地产价格波动与通货膨胀率之间具有双向因果关系,最后给出了脉冲响应函数,得出结论：我国房地产价格波动与通货膨胀有一定的相关性。     

Interdependence between Eastern and Western European stock markets: Evidence from intraday data

We analyze comovements among three stock markets in Central and Eastern Europe and, in addition, interdependence which may exist between Western European (DAX, CAC, UKX) and Central and Eastern European (BUX, PX-50, WIG-20) stock markets. The novelty of our paper rests mainly on the use of 5-min tick intraday price data from mid-2003 to early 2005 for stock indices and on the wide range of econometric techniques employed. We find no robust cointegration relationship for any of the stock index pairs or for any of the extended specifications. There are signs of short-term spillover effects both in terms of stock returns and stock price volatility. Granger causality tests show the presence of bidirectional causality for returns as well as volatility series. The results based on a VAR framework indicate a more limited number of short-term relationships among the stock markets.     

Decision analysis and coordination of dual supply chain with retailer's offline return service and online reviews

Based on the centralized and decentralized decision-making cases, this study constructs a dual-channel supply chain game model that considers offline return service and online reviews, and explores the pricing decisions and benefits of supply chains under the dual influence of return service level and online reviews. The impact of return rate, return service level and perceived quality of online reviews on optimal supply chain decisions and profits are further analyzed. Finally, considering the phenomenon of double marginalization in the supply chain, this study coordinates the supply chain based on the benefit-sharing contract coordination model to achieve Pareto improvement. The research shows that: The manufacturer's optimal wholesale price, the optimal online selling price and the retailer's optimal offline price are positively correlated with the perceived quality of online reviews and the level of return service. Compared with the return service level, the perceived quality of online reviews has a greater impact on the overall profit of the supply chain. Under the dual influence of the perceived quality of online reviews and the level of return service, the supply chain profit in the centralized model is better than that in the decentralized model. The coordination model can effectively coordinate the supply chain. Moreover, when the sharing ratio of manufacturers is obviously lower than that of retailers, the coordination effect is better at this time.     

Intraday portfolio risk management using VaR and CVaR:A CGARCH-EVT-Copula approach

The study forecast intraday portfolio VaR and CVaR using high frequency data of three pairs of stock price indices taken from three different markets. For each pair we specify both the marginal models for the individual return series and a joint model for the dependence between the paired series. We have used CGARCH-EVT-Copula model, and compared its forecasting performance with three other competing models. Backtesting evidence shows that the CGARCH-EVT-Copula type model performs relatively better than other models. Once the best performing model is identified for each pair, we develop an optimal portfolio selection model for each market, separately.     

On the globalization of stock markets: An application of Vector Error Correction Model,Mutual Information and Singular Spectrum Analysis to the G7 countries

This paper analyzes stock market relationships among the G7 countries between 1973 and 2009 using three different approaches: (i) a linear approach based on cointegration, Vector Error Correction (VECM) and Granger Causality; (ii) a nonlinear approach based on Mutual Information and the Global Correlation Coefficient; and (iii) a nonlinear approach based on Singular Spectrum Analysis (SSA). While the cointegration tests are based on regression models and capture linearities in the data, Mutual Information and Singular Spectrum Analysis capture nonlinear relationships in a non-parametric way. The framework of this paper is based on the notion of market integration and uses stock market correlations and linkages both in price levels and returns. The main results show that significant co-movements occur among most of the G7 countries over the period analyzed and that Mutual Information and the Global Correlation Coefficient actually seem to provide more information about the market relationships than the Vector Error Correction Model and Granger Causality. However, unlike the latter, the direction of causality is difficult to distinguish in Mutual Information and the Global Correlation Coefficient. In this respect, the nonlinear Singular Spectrum Analysis technique displays several advantages, since it enabled us to capture nonlinear causality in both directions, while Granger Causality only captures causality in a linear way. The results also show that stock markets are closely linked both in terms of price levels and returns (as well as lagged returns) over the 36 years analyzed.     

Information flow between prediction markets,polls and media: Evidence from the 2008 presidential primaries

Are the forecast errors of election-eve polls themselves forecastable? We present evidence from the 2008 Democratic Party nomination race between Barack Obama and Hillary Clinton showing that the answer is yes. Both cross-sectional and time series evidence suggests that market prices contain information about election outcomes that polls taken shortly before the contests do not. Conversely, election surprises relative to polls too Granger cause subsequent price movements. We then investigate whether the additional information in prices could come from the media coverage of these campaigns, and uncover a set of complex relationships between pollster’s surprise, price movements, and various aspects of media coverage. Prices anticipate the balance and content of media coverage, but not the volume. On the other hand, it is the volume of media coverage, not the balance or content, that anticipates the surprise element in election outcomes. Moreover, Granger causality between prices and election surprises barely changes after controlling for media coverage, and causality from media volume to surprises persists too after controlling for price movements. Taken together, the results suggest that both prices and the volume of media coverage contain independent election-relevant information that is not captured in polls.     

上海黄金期货市场有效性的实证分析

黄金因为其兼有货币、商品和金融三大属性的特征历来受到人们的重视。今年年初黄金期货在上海期货交易所的上市,填补了我国长期缺乏金融期货的空白。同时管理层与市场各参与主体对黄金期货给予很高的期望与关注,因此研究上海黄金期货市场的有效性具有重要意义。通过运用ADF单位根检验、协整检验、误差修正模型以及格兰杰因果检验等时间序列与计量经济方法,对上海期货交易所黄金期货市场的有效性进行实证分析。结果表明,上海黄金期货市场尚未达到有效,并且黄金现货价格单向引导期货价格。     

Carbon option price forecasting based on modified fractional Brownian motion optimized by GARCH model in carbon emission trading

With the rapid growth of carbon trading, the development of carbon financial derivatives such as carbon options has become inevitable. This paper established a model based on GARCH and fractional Brownian motion (FBM), hoping to provide reference for China's upcoming carbon option trading through carbon option price forecasting research. The fractal characteristic of carbon option prices indicates that it is reasonable to use FBM to predict option prices. The GARCH model can make up for the lack of fixed FBM volatility. In this paper, the daily closing prices of EUA option contracts on the European Energy Exchange are selected as samples for price prediction. The GARCH model was used to determine the return volatility, and then the FBM was used to calculate the forecast price for the next 60 days. The results showed that the predicted price can better fit the actual price. This paper further compares the price prediction results of this model with the other three models through line graphs and error evaluation indicators such as MAPE, MAE and MSE. It is confirmed that the prediction results of the model in this paper is the closest to the actual price.     

Household borrowing and metropolitan housing price dynamics – Empirical evidence from Helsinki

This article argues that, especially in the absence of sufficient direct data on credit constraints, it is reasonable to add a household debt variable in an empirical model studying housing price dynamics. This is because household borrowing is likely to reveal information regarding the credit constraints faced by households. Moreover, debt may also give information on expected income growth and interest rate movements. The aim of this study is to examine empirically if household borrowing data, indeed, is of importance in a dynamic housing price model. In line with the prior expectations, it is found that housing appreciation in the Helsinki Metropolitan area is Granger caused by the household debt-to-GDP ratio both in the short and in the long run. Causality from the housing market to credit, in turn, seems to run only through a cointegrating long-run relation. While the estimated long-run relation between housing prices, income and debt-to-GDP ratio appears to have remained stable through the sample period (1975Q1-2006Q2), the short-run dynamics changed somewhat due to the financial liberalization in the late 1980s. The stability of the long-run relation implies that the loan data are able to cater, at least to a notable extent, for the effect of the changes in Finnish households’ liquidity constraints on housing demand. In line with previous literature, it is also found that housing price adjustment is sluggish and includes notable backward-looking features.     

钢材期货价格发现功能研究——基于印度市场信息共享模型的分析

本文借助Johansen协整检验、Granger因果检验、信息共享模型、方差分解等方法进行多层次实证研究,定量地刻画出期货市场在价格发现中作用的大小。研究结果显示：印度板材期货价格和现货价格之间存在长期均衡关系,在价格引导上仅存在现货对期货价格的单向引导关系,期货对现货没有引导关系;通过方差分解发现,现货市场在价格发现功能中处于主导地位,说明印度钢材期货市场效率有待提高,板材期货没有实现其应有的价格发现功能。