人民币汇率预期：基于ARCH族模型的实证分析

人民币汇率升值预期持续强化,观察其变化的重要指标主要是境外人民币无本金交割远期汇率(CNYNDF)。本文建立CNYNDF的高频日汇率随机游走模型,运用ARCH族模型检验该模型的残差后发现,其时间序列数据具有"尖峰厚尾性"和集群性特征。在信息不对称的条件下,市场对人民币升值和贬值存在不同程度的反应,2002年以来,市场预期人民币升值,市场信息具有杠杆作用。2005年7月21日人民币汇率形成机制改革以来,境内人民币即期汇率和远期汇率开始受CNYNDF报价引导,逐步形成稳定的Granger因果关系。为取得人民币汇率定价的主动权,中国需加快外汇交易的微观制度建设,加强汇率与利率改革的协调性,为规避汇率风险创造市场条件。     

Detecting ARCH Effects in Non-Gaussian Time Series

Engles ARCH test has become the standard test for ARCH effectsin applied work. Under non-normality the true rejection probabilityof this test can differ substantially from the nominal level,however. Bootstrap and Monte Carlo versions of the test maythen be used instead. This paper proposes an alternative testprocedure. The new test exploits the empirical distributionof the data and an extended probability integral transformation.The test is compared with the former tests in Monte Carlo experiments.Under normality, the new test works as well as the conventionalMonte Carlo test and the bootstrap. Under non-normality, thetest tends to be more accurate and more powerful than the bootstrappedARCH test. The procedure is then used to test for ARCH effectsin S&P 500 returns sampled at different frequencies. Incontrast to the standard and the bootstrapped ARCH tests, thenew test detects ARCH effects in the transformed low-frequencyreturns.     

基于ARCH类模型的VaR方法在外汇风险计量中的应用

本文将残差项服从t分布的ARCH类模型应用于我国外汇风险的计量。通过美元/人民币汇率日波动率VaR值的实证分析发现:(1)ARCH类模型预测得到的VaR值都能很好地拟合美元/人民币汇率日波动率的实际情况,美元/人民币汇率存在明显的ARCH效应;(2)基于ARCH类模型预测的VaR值其计算精度基本上都超过了J.P.Morgan公司RiskMetrics所采用的EWMA模型,这验证了本文选取ARCH类模型以及考虑残差项服从t分布的合理性;(3)ARCH类模型中以TARCH-M(1,1)模型计算结果最为理想。本研究可为金融机构、监管部门以及外汇投资者规避外汇风险提供决策依据和理论参考。     

银行信贷、房地产价格与经济增长的非线性关系研究——基于省际面板平滑转换模型的实证检验

选用中国2000~2014年31个省的面板数据,以银行信贷为转换变量,通过构建面板平滑转换模型对我国房地产价格与经济增长的非线性关系进行考察。研究发现：当信贷增长率低于28.74%时,房价增长率对经济增长产生比较显著的正向影响;当信贷增长率高于28.74%时,房价增长率对经济增长起到了明显的阻碍作用。因此,为了实现“稳定房价和保持经济平稳增长”的目标,央行应将信贷增速维持在低增长体制的最优区间中。同时,央行还应该改善信贷结构,鼓励和引导金融资源进行合理配置。     

Testing for Serial Correlation in the Presence of Stochastic Volatility

This paper develops a regression-based testing procedure for serial correlation in the presence of stochastic volatility. The asymptotic distribution of the test is derived, and the finite sample properties are investigated. Monte Carlo results shows that the test is reliable in terms of both size and power performances, when the underlying process is a log-linear stochastic volatility. Moreover, the test is superior to Woolridge's (1991) robust LM tests in terms of size in finite sample. Serial correlation tests were conducted for nominal returns of ten exchange rates, and indicated that there is a strong evidence of serial correlation for Yen/Dollar exchange rates.     

Regime Change and the Role of International Markets on the Stock Returns of Small Open Economies

We examine the influence of US, UK and German macroeconomic and financial variables on the stock returns of two relatively small, open European economies, Ireland and Denmark. Within a nonlinear framework, we allow for time variation via regime switching using a smooth transition regression (STR) model. We find that US (global) and UK and German (regional) stock returns are significant determinants of returns in both markets. Further, global information represented by oil and US asset price movements drive changes between states in each market. Significantly, the role of country‐specific domestic variables is typically confined to a single state while global and regional variables pervade all states.     

Asia-Pacific Stock Market Integration: New Evidence by Incorporating Regime Changes

ABSTRACT

This work provides new evidence of Asia-Pacific stock market integration by incorporating the regime changes of each stock market through the smooth transition autoregressive (STAR) model. According to empirical results, most Asia-Pacific stock market returns follow STAR dynamics to a significant degree with more rapid and frequent regime changes of a shorter nature compared with G7 markets. A series of STAR-based Granger causality tests reveal evidence of stronger equity market integration compared with linear Granger causality tests. We also find that Asia-Pacific stock markets are integrated in different levels. Finally, we provide evidence that in the early twenty-first century the influence of China and the United States on Asia-Pacific stock markets has been maintained while that of Japan has been weakened.     

Doorsteps Toward Political and Economic Openness: Testing the North-Wallis-Weingast Transition Framework

In this paper, we test the theoretical framework developed by North, Wallis, and Weingast (2009), who posit that limited-access societies need to meet three doorstep conditions before they can transit into open-access societies: (1) establishment of rule of law among elites, (2) adoption of perpetually existing organizations, and (3) political control of the military. We identify indicators reflecting these doorsteps and econometrically test their relationships with specific political and economic variables. We broadly confirm the logic behind the doorsteps as necessary conditions in the transition to open-access societies. The doorsteps influence economic and political processes, as well as each other, with varying intensities.     

Stochastic chaos or ARCH effects in stock series?: A comparative study

Recent empirical studies have shown that the chaotic behaviour and excess volatility of financial series are the result of interactions between heterogeneous investors. In our article, we propose verifying this hypothesis. Thus, we use the Chen et al. [Testing for non-linear structure in an artificial financial market. Working Paper, University of Bonn (2000).] model to show that the modification of the agents' homogeneity hypothesis can drive to stochastic chaotic evolution of price series. Then, through an econometric procedure, we try to identify the underlying process of the Paris Stock Exchange returns series (CAC40). To this end, we apply several different tests: (1) dealing with long-memory components derives from the fractional integration test of Geweke and Porter-Hudak (GPH) [J. Time Ser. Anal. 4 (1983) 221.] and (2) dealing with chaotic structures comes from the work on correlation dimension of Grassberger and Procaccia [Physica 9D (1983) 189.] and the Lyapunov exponents method of Gençay and Dechert [Physica D (1992) 142.]. Finally, we forecast the CAC40 returns series using the recent methods of Principal Components Regression (PCR) and Radial Basis Functions (RBF). We conclude with the implications of the presence of chaotic structures in stock markets and future research on ARCH and chaotic models' relationships.     

A link between complete models with stochastic volatility and ARCH models

In this paper, we propose a heteroskedastic model in discrete time which converges, when the sampling interval goes to zero, towards the complete model with stochastic volatility in continuous time described in Hobson and Rogers (1998). Then, we study its stationarity and moment properties. In particular, we exhibit a specific model which shares many properties with the GARCH(1,1) model, establishing a clear link between the two approaches. We also prove the consistency of the pseudo conditional likelihood maximum estimates for this specific model.Received: December 2002Mathematics Subject Classification: 90A09, 60J60, 62M05JEL Classification: C32This work was supported in part by Dynstoch European network. Thanks to David Hobson for introducing me to these models, and to Valentine Genon-Catalot for numerous and very fruitful discussion on this work. The author is also grateful to Uwe Kuchler for various helpful suggestions, and to two referees and an associate editor for their comments and suggestions.     

ARCH and GARCH models vs. martingale volatility of finance market returns

ARCH and GARCH models assume either i.i.d. or ‘white noise’ as is usual in regression analysis, while also assuming memory in a conditional mean square fluctuation with stationary increments. We will show that ARCH/GARCH is inconsistent with uncorrelated increments, violating the i.i.d. and ‘white’ assumptions, and violating finance data and the efficient market hypothesis as well.     

A New Approach to Markov-Switching GARCH Models

The use of Markov-switching models to capture the volatilitydynamics of financial time series has grown considerably duringpast years, in part because they give rise to a plausible interpretationof nonlinearities. Nevertheless, GARCH-type models remain ubiquitousin order to allow for nonlinearities associated with time-varyingvolatility. Existing methods of combining the two approachesare unsatisfactory, as they either suffer from severe estimationdifficulties or else their dynamic properties are not well understood.In this article we present a new Markov-switching GARCH modelthat overcomes both of these problems. Dynamic properties arederived and their implications for the volatility process discussed.We argue that the disaggregation of the variance process offeredby the new model is more plausible than in the existing variants.The approach is illustrated with several exchange rate returnseries. The results suggest that a promising volatility modelis an independent switching GARCH process with a possibly skewedconditional mixture density.     

Fast and realistic European ARCH option pricing and hedging

The behavior of the implied volatility surface for European options was analysed in detail by Zumbach and Fernandez for prices computed with a new option pricing scheme based on the construction of the risk-neutral measure for realistic processes with a finite time increment. The resulting dynamics of the surface is static in the moneyness direction, and given by a volatility forecast in the time-to-maturity direction. This difference is the basis of a cross-product approximation of the surface. The subsequent speed-up for option pricing is large, allowing the computation of Greeks and the delta replication strategy in simulations with the cost of replication and the replication risk. The corresponding premia are added to the option arbitrage price in order to compute realistic implied volatility surfaces. Finally, the cross-product approximation for realistic prices can be used to analyse European options on the SP500 in depth. The cross-product approximation is used to compute a mean quotient implied volatility, which can be compared with the full theoretical computation. The comparison shows that the cost of hedging and the replication risk premium have contributions to the implied volatility smile that are of similar magnitude to the contribution from the process for the underlying asset.     

Resuscitating real interest rate parity: new evidence from panels

This paper considers the real interest rate parity (RIRP) in OECD countries applying a sequential panel selection (SPS) method on alternative panel unit-root tests. Our approach exploits the enhanced power of panels to uncover evidence of stationarity, but also identifies the exact countries for which the RIRP holds in a panel. Moreover, we construct real interest rate measures using alternative approaches, including a Markov regime-switching procedure, which is consistent with the forward-looking nature of inflation expectations formation. Considering US as the benchmark economy, we produce strong evidence of stationarity in real interest rate differentials, which resuscitates RIRP, especially given the inconclusive results in the related literature. Our results are robust to different panel unit-root tests, measures of inflation expectations, and interest rate maturities. The RIRP appears quite resilient in the face of the global financial crisis and the low real interest rate environment after the great recession. The SPS allows to calculate half-lives, which avoid the pitfalls of over/underestimating the speed of adjustment and are lower as compared to the typical estimates in the literature.     

Dynamic Portfolio Selection: the Relevance of Switching Regimes and Investment Horizon

This paper investigates the questions of dynamic portfolio selection and intertemporal hedging within a Markovian regime‐switching framework. The investment opportunity set is spanned by a well‐diversified home‐market portfolio and the risk‐free asset. Our results highlight the economic importance of regimes, as optimal portfolio weights are clearly dependent on the prevailing regime. We present evidence that the question of intertemporal hedging is a more complex issue than is hinted in the previous literature, since demand for intertemporal hedging is present in some regimes, but not in others. Finally, our main findings are qualitatively unchanged across the four largest stock markets in the world.     

Emerging Markets and Stock Market Bubbles: Nonlinear Speculation?

Daily returns of stock markets in emerging markets in Asia, Africa, South America, and Eastern Europe from the early 1990s through 2006 are analyzed for the possible presence of nonlinear speculative bubbles. The absence of these is tested for by studying residuals of vector autoregressive-based fundamentals, using the Hamilton regimeswitching model and the rescaled range analysis of Hurst. For the first test, absence of bubbles is rejected for twenty-four countries (except Mexico, Sri Lanka, and Taiwan); for the second test, it is rejected for twenty-six countries (except Malaysia). BDS testing on these residuals after autoregressive conditional heteroskedasticity (ARCH) effects are removed fails to reject further nonlinearity (except for Israel). Policy issues are discussed, noting that what is appropriate varies from country to country and time period to time period.     

The Fisher Equation: A Nonlinear Panel Data Approach

This article reinvestigates the Fisher equation. Using the panel smooth transition regression (PSTR) model, it was found that there is a significant regime-switching effect concerning the impact of inflation on interest rates. Specifically, inflation is found to raise the interest rates and the effect becomes stronger in magnitude with inflation. However, the data do not provide evidence in support of the one-for-one Fisher effect. The evidence is robust to interest rates with different maturities and subsamples.     

Estimating unknown join points: Determination of the yen-dollar exchange rate

We propose a Bayesian procedure to estimate a switching regression in which the number of switching points (i.e. join points) is not known. We apply the Bayesian procedure to a regression model for the yen-dollar exchange rate using monthly data from January 1973 to June 1992. We identify three join points in January 1978, September 1988, and March 1990. We compare the post-sample forecast performances of our switching regression model to those of other regression models. The post-sample forecasts show that the Bayesian switching model performs better than the other models.