基于稳定分布条件的GARCH模型研究

本文应用GARCH模型对1995～2008年的沪深A股指数收益率序列进行分析,对正态分布、学生t分布、广义误差分布以及稳定分布下的GARCH模型进行对比研究,发现基于极大似然准则和AIC信息准则下,新信息服从稳定分布的GARCH模型优于其他模型。     

服务出口与实际汇率动态关系分析:基于美国数据的考察

基于代表性家庭追求效用最大化,文中构建了一个由外国实际收入、汇率水平、货物出口以及经GARCH(1,1)模型估计所得条件方差作为汇率风险代理变量组成的服务出口方程.采用自回归分布滞后估计方程参数,并将汇率变动影响货物出口进而传导至服务出口的间接效应纳入分析框架.估计结果显示,汇率水平变动与服务出口之间存在负相关关系,而...     

Causality and volatility patterns between gold prices and exchange rates

This paper provides a new perspective on the link between gold prices and exchange rates. Based on gold prices denominated in five different currencies and the related bilateral exchange rates, we put causalities and short-run volatility transmission under closer scrutiny. We provide evidence that the identification of a strong hedge function of gold requires an explicit modeling of the volatility component. For all currencies, exchange rate depreciations initially have a negative impact on the gold price after one day which turns out to be positive after two days in most of the cases. Contrary to previous studies, our results point to a specific role of the dollar in the context of gold-exchange rate relationships: volatility of dollar exchange rates more frequently results in strong hedging functions of gold prices. Furthermore, the gold price denominated in the US dollar tends to increase after a depreciation of the dollar.     

Forecasting the volatility of asset returns: The informational gains from option prices

A new class of forecasting models is proposed that extends the realized GARCH class of models through the inclusion of option prices to forecast the variance of asset returns. The VIX is used to approximate option prices, resulting in a set of cross-equation restrictions on the model’s parameters. The full model is characterized by a nonlinear system of three equations containing asset returns, the realized variance, and the VIX, with estimation of the parameters based on maximum likelihood methods. The forecasting properties of the new class of forecasting models, as well as a number of special cases, are investigated and applied to forecasting the daily S&P500 index realized variance using intra-day and daily data from September 2001 to November 2017. The forecasting results provide strong support for including the realized variance and the VIX to improve variance forecasts, with linear conditional variance models performing well for short-term one-day-ahead forecasts, whereas log-linear conditional variance models tend to perform better for intermediate five-day-ahead forecasts.     

Detecting nonlinearity in time series by model selection criteria

This article analyzes the use of model selection criteria for detecting nonlinearity in the residuals of a linear model. Model selection criteria are applied for finding the order of the best autoregressive model fitted to the squared residuals of the linear model. If the order selected is not zero, this is considered as an indication of nonlinear behavior. The BIC and AIC criteria are compared to some popular nonlinearity tests in three Monte Carlo experiments. We conclude that the BIC model selection criterion seems to offer a promising tool for detecting nonlinearity in time series. An example is shown to illustrate the performance of the tests considered and the relationship between nonlinearity and structural changes in time series.     

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.     

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.     

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.