Measuring liquidity with return volatility: An analytical approach based on heavy-tailed Censored-GARCH model

The measurement of liquidity based on low-frequency data is a crucial issue in the financial market microstructure literature. This paper extends the commonly used LOT liquidity model by incorporating the characterization of the volatility dynamics and distributional properties of return series, thereby significantly improving both the goodness-of-fit of liquidity model and the estimation performance of the existing low-frequency liquidity measures. The new models, which have a special form of heavy-tailed Censored-GARCH model, have challenges in estimation. We then provide an approximate maximum likelihood estimation method to circumvent this problem. A real data analysis is conducted to evaluate the performance of the new liquidity measures. The results show overwhelming evidence that our new measures have advantages over the existing measures in both estimation error and correlation coefficient with high-frequency bid-ask spread. The robustness check analysis further illustrates that the advantages of our new measures are stable across different stock industries and different turnover levels.     

Time-varying correlations in oil,gas and CO2 prices: an application using BEKK,CCC and DCC-MGARCH models

Previous literature has identified oil and gas prices as being the main drivers of CO₂ prices in a univariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) econometric framework (Alberola et al., 2008; Oberndorfer, 2009). By contrast, we argue in this article that the interrelationships between energy and emissions markets shall be modelled in a Vector Autoregressive (VAR) and Multivariate GARCH (MGARCH) framework, so as to reflect the dynamics of the correlations between the oil, gas and CO₂ variables overtime. Using the Baba–Engle–Kraft–Kroner (BEKK), Constant Conditional Correlation (CCC) and Dynamic Conditional Correlation MGARCH (DCC-MGARCH) models on daily data from April 2005 to December 2008, we highlight significant own-volatility, cross-volatility spillovers, and own persistent volatility effects for nearly all markets, indicating the presence of strong Autoregressive Conditional Heteroscedasticity (ARCH) and GARCH effects. Besides, we provide strong empirical evidence of time-varying correlations in the range of [?0.3;?0.3] between oil and gas, [?0.05;?0.05] between oil and CO₂, and [?0.2;?0.2] between gas and CO₂, that have not been considered by previous studies. These findings are of interest for traders and utilities in the energy sector, but also for a broader applied economics audience.     

The Saturday effect: an interesting anomaly in the Saudi stock market

The vast majority of empirical research on calendar anomalies has studied financial markets in countries where the Western calendar is used. This article investigates day of the week effects in Saudi Arabia’s stock market, where an Islamic calendar is used and where Saturday is the first working day of the week over the sample period considered. The Saudi stock market is the largest in the Gulf region, and we consider both total market data (the TASI index) and data for 15 sector indices. Our investigation reveals the existence of a positive Saturday effect, which contrasts with the results on first day of the week effects that are typically obtained for Western calendar markets.     

Long memory in the volatility of China stock returns

In this paper, we empirically examine the volatility process of China's stock market returns using daily and weekly Shanghai and Shenzhen stock indices during January 1990 to August 2008. To investigate the property of the process, we used the FIGARCH (fractionally integrated GARCH) model including GARCH and IGARCH processes as special cases. Since the FIGARCH model allows fractional integration order, it can detect hyperbolically decaying volatility processes which cannot be explained by previous models with integer integration order. Our results show that the Shanghai and Shenzhen stock indices exhibit long-term dependencies. The long memory properties of the Shanghai and Shenzhen stock markets do not seem to be spuriously induced without exception.     

On inferring individual behaviour from market behaviour in a predetermined quantities model

This article develops the theoretical basis of individual behaviour recovered from market behaviour in a predetermined quantities model. As applied economists argue, an inverse demand system may be empirically sound within the framework of classical demand theory. However, it should not lead to the conclusion that the market responses for changes in quantity should be used to see welfare effects instead of the individual responses by price changes as far as the market is concerned. It shows theoretically and empirically how individual responses can be recovered from market responses in a predetermined quantities model. It suggests that the fundamental results of this article should be used on interpreting empirical results from the predetermined quantities models.     

基于CARR模型和GARCH模型的股市波动性预测研究

金融资产波动性建模和预测是金融理论与实践中的一个重要课题,已经有了许多建模与预测方法。本文利用我国股市的高频数据进行实证研究,分别运用CARP,模型和GARCH模型进行波动性预测,进而对两个模型的预测能力进行对比,结果表明：CARP,模型在波动性预测方面比GARCH模型的效果更好。     

The response of term rates to monetary policy uncertainty

This paper shows that greater uncertainty about monetary policy can lead to a decline in nominal interest rates. In the context of a limited participation model, monetary policy uncertainty is modeled as a mean preserving spread in the distribution for the money growth process. This increase in uncertainty lowers the yield on short-term maturity bonds because the household sector responds by increasing liquidity in the banking sector. Long-term maturity bonds also have lower yields but this decrease is a result of the effect that greater uncertainty has on the nominal intertemporal rate of substitution—which is a convex function of money growth. We examine the nature of these relations empirically by introducing the GARCH-SVAR model—a multivariate generalization of the GARCH-M model. The predictions of the model are broadly supported by the data: higher uncertainty in the federal funds rate can lower the yields of the three- and six-month treasury bill rates.     

中国和印度股市波动性的比较研究

作为“金砖四国”中的成员,中印两国股票市场具有较强的可比性。比较分析金融危机发生后两国的股市波动性特征,对中国股市发展具有理论和现实双重借鉴意义。文章利用ARCH族模型对上证综合指数和印度孟买30指数日收盘价数据展开实证研究,比较解析金融危机发生后中印两国的股市波动性特质,分析表明可变性和波动集簇性是两国收益率波动均呈现出的明显特质,而且印度比中国有更强的显示度;此外,中印两国股市收益正的风险溢价表现不显著;杠杆效应在上证综合指数收益率和印度孟买30指数收益率中均有体现,而且杠杆效应在印度股市的影响要高于中国股市。这对于确保中国股票和证券市场持续、稳定、强劲发展具有显著的理论说服力及重大的实践意义。     

Intraday bidirectional volatility spillover across international stock markets: does the global financial crisis matter?

This article studies volatility spillover between the US and the three largest European stock markets (Frankfurt, London and Paris) around the time of the recent Subprime crisis. In order to investigate the impact of the latter, we break our sample down into two sub-periods: a pre-crisis period and a post-crisis period, using a structural break test that has the advantage of endogenously testing for further breaks in the data. Unlike previous studies that have frequently investigated this issue using low frequency data, our article makes use of intraday data. Accordingly, using Threshold generalized autoregressive conditional heteroscedasticity (GARCH) model estimations, we find weak evidence of volatility transmission between the two regions before the Subprime crisis. However, during the post-crisis period, we record returns and volatility spillover from US to European markets and vice versa at different times of the trading day, indicating that the two regions became more dependent during the recent Subprime crisis, a finding that supports the contagion hypothesis between the US and European stock markets.