Implications of ERM2 for Poland's monetary policy

We propose an extension to the inflation targeting regime currently pursued by Poland. It incorporates the exchange rate stability constraints as imposed by the obligatory participation in the ERM2 that Poland needs to satisfy prior to adopting the euro. The modified policy is based on the forward-looking inflation targeting supplemented with the exchange rate stability objective. Its effective implementation depends on the determined long-term equilibrium exchange rate and the observed degree of exchange rate volatility. Both are empirically estimated by employing the Johansen cointegration tests and the threshold generalized autoregressive heteroscedasticity model with the in-mean extension and generalized error distribution (TGARCH-M-GED).     

Modelling dependence between tourism demand and exchange rate using the copula-based GARCH model

This paper investigates dependence between tourism demand and exchange rate, using the case of China, and from a new perspective by using copula–GARCH models. The empirical results show that the volatility of exchange rate is not a determinant factor in fluctuation of China's inbound tourism demand from the countries being studied. Furthermore, only Russia exhibits risk-adverse behaviour with extreme SUR depreciation, or CNY appreciation associated with an extreme decline in arrivals. Third, introducing the tail dependence and dynamic dependence between growth rates of tourism demand and exchange rate add much to the explanatory ability of the model. The findings of this study have important implications for destination manager and travel agent as it helps to understand the impact of exchange rates on China inbound tourism demand and provide a complementary academic approach on evaluating the role of exchange rates in the international tourism demand model.     

Self-weighted and local quasi-maximum likelihood estimators for ARMA-GARCH/IGARCH models

The limit distribution of the quasi-maximum likelihood estimator (QMLE) for parameters in the ARMA-GARCH model remains an open problem when the process has infinite 4th moment. We propose a self-weighted QMLE and show that it is consistent and asymptotically normal under only a fractional moment condition. Based on this estimator, the asymptotic normality of the local QMLE is established for the ARMA model with GARCH (finite variance) and IGARCH errors. Using the self-weighted and the local QMLEs, we construct Wald statistics for testing linear restrictions on the parameters, and their limiting distributions are given. In addition, we show that the tail index of the IGARCH process is always 2, which is independently of interest.     

条件自回归极差模型与波动率估计

无论是在理论研究领域还是在应用领域，波动率的预测已经成为现代金融经济学和金融工程的重要课题。Chou（2005）针对极差提出了条件自回归极差模型（CARR）。本文在Parkinson（1980）的基础上，对极差作出了一个简单的修正，使得相应的CARR模型成为标准差的动态模型；然后以上证指数2001年4月27日至2005年12月5日的周收益率数据为样本，采用滚动样本的方法，利用CARR模型和GARCH模型分析了样本数据，作出了上证指数波动率样本外1至8周的预测，在多种事后波动率的测度下比较了修正后的CARR模型与GARCH模型对上证指数波动率的预测能力，证实了CARR模型在理论上的有效性。     

变结构门限t-GARCH模型及其伪持续性研究

为了反映金融时间序列的波动集聚性、非对称性、厚尾性以及在实证研究中表现出的伪持续性，本文结合门限GARCH模型以及变结构的方法提出了变结构门限t—GARCH模型。首先用Monte Carlo模拟的方法考虑了变结构GARCH模型中存在的伪持续性问题；其次针对金融时间序列非对称性、厚尾性以及强持续性的特点提出了变结构门限t—GARCH模型，总结了关于变结构点检验的几个主要方法；最后用该模型来拟合沪市和深市两个股市的周收益率序列，得到了比GARCH模型更好的拟合结果。     

Conditional correlations in the returns on oil companies stock prices and their determinants

The identification of the forces that drive stock returns and the dynamics of their associated volatilities is a major concern in empirical economics and finance. This analysis is extremely important for determining optimal hedging strategies. This paper investigates the stock prices’ returns and their financial risk factors for several integrated oil companies, namely Bp (BP), Chevron-Texaco (CVX), Eni (ENI), Exxon-Mobil (XOM), Royal Dutch (RD) and Total-Fina Elf (TFE). We measure the actual co-risk in stock returns and their determinants “within” and “between” the different oil companies, using multivariate cointegration techniques in modelling the conditional mean, as well as multivariate GARCH models for the conditional variances. The distinguishing features of this paper are: (i) focus on the determinants of the market value of each company using the cointegrated VAR/VECM methodology; (ii) specification of the conditional variances of VECM residuals with the Constant Conditional Correlation (CCC) multivariate GARCH model of Bollerslev [(1990) Review of Economics and Statistics 72:498–505] and the Dynamic Conditional Correlation (DCC) multivariate GARCH model of Engle [(2002) Journal of Business and Economic Statistics 20:339–350]; (iii) discussion of the performance of optimal hedge ratios calculated with the DCC estimates. The “within” and “between” DCC indicate time-varying interdependence between stock return volatilities and their determinants. Moreover, DCC models are shown to produce more accurate hedging strategies.     

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.     

Multimodality in GARCH regression models

It is shown empirically that mixed autoregressive moving average regression models with generalized autoregressive conditional heteroskedasticity (Reg-ARMA-GARCH models) can have multimodality in the likelihood that is caused by a dummy variable in the conditional mean. Maximum likelihood estimates at the local and global modes are investigated and turn out to be qualitatively different, leading to different model-based forecast intervals. In the simpler GARCH(p,q) regression model, we derive analytical conditions for bimodality of the corresponding likelihood. In that case, the likelihood is symmetrical around a local minimum. We propose a solution to avoid this bimodality.     

交易量适合作为股价波动信息的代理变量吗?——来自中国沪深股市的证据

本文利用中国沪深股市日交易数据,采用多元GARCH模型从信息传递的角度进行实证研究,结果表明:股价对交易量具有显著的波动溢出效应,但交易量对股价的波动溢出效应不明显。这种波动的单向溢出说明在应对信息的冲击上股价比交易量能更快地做出反应,其后才通过波动溢出在交易量上得到反映,股价波动对成交量波动具有先导作用。因此,从波动冲击传导和信息传递的角度看,单纯地将交易量视为股价变动信息的代理变量还缺乏稳健的统计证据。