Estimation of tail‐related risk measures in the Indian stock market: An extreme value approach

The purpose of the study is to estimate tail-related risk measures using extreme value theory (EVT) in the Indian stock market. The study employs a two stage approach of conditional EVT originally proposed by McNeil and Frey (2000) to estimate dynamic Value at Risk (VaR) and expected shortfall (ES). The dynamic risk measures have been estimated for different percentiles for negative and positive returns. The estimates of risk measures computed under different quantile levels exhibit strong stability across a range of the selected thresholds, implying the accuracy and reliability of the estimated quantile based risk measures.     

Public information arrival and volatility persistence in financial markets

This paper explores the relationship between daily market volatility and the arrival of public information in four different financial markets. Public information is measured as the daily number of economic news headlines, divided in six categories of news. Statistical analysis of the news data suggests the presence of particular seasonality effects, as well as a strong degree of autocorrelation. Over the period 1994–1998, significant effects of specific news categories on the volatility of US stocks, treasury bills, bonds and dollar were detected. However, the effects – in size and duration – vary by news category and by financial market. It is demonstrated that most of the volatility persistence, as observed by GARCH models, tends to disappear when news is included in the conditional variance equation.     

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.     

Modeling Conditional Skewness in Stock Returns

Abstract

In this paper, we propose a new GARCH-in-Mean (GARCH-M) model allowing for conditional skewness. The model is based on the so-called z distribution capable of modeling skewness and kurtosis of the size typically encountered in stock return series. The need to allow for skewness can also be readily tested. The model is consistent with the volatility feedback effect in that conditional skewness is dependent on conditional variance. Compared to previously presented GARCH models allowing for conditional skewness, the model is analytically tractable, parsimonious and facilitates straightforward interpretation.Our empirical results indicate the presence of conditional skewness in the monthly postwar US stock returns. Small positive news is also found to have a smaller impact on conditional variance than no news at all. Moreover, the symmetric GARCH-M model not allowing for conditional skewness is found to systematically overpredict conditional variance and average excess returns.     

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.     

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.     

Performance of Garch Models in Forecasting the Volatility of Exchange Rate for Developing Countries

This paper empirically investigates the performance of GARCH model in forecasting the volatility of exchange rate of some developing countries. We apply linear GARCH model and non-linear GARCH model. We fit these two models to some developing countries exchange rate index from January, 1998 to February, 2005. The return series of the developing countries＇ foreign exchange rate are leptokurtic, significantly skew, deviating from normality and volatile clustering as well. We find within-sample and out-of-sample evidence that conditional estimates of non-linear GARCH model outperform the conditional estimations of linear GARCH models. In our comparisons in most of the developing countries, the non-linear GARCH model produce better results than the linear GARCH model tor forecasting the volatility of exchange rate.     

Asset allocation and multivariate position based trading

I add a second risky asset and a risk free bond to the univariate artificial market investigated by Lux and Marchesi (Int J Theor Appl Finance 3(4):675–702, 2000), keeping track of traders aggregate positions and wealth. Asset allocation and security selection are modeled as separate decision processes, as is common practice in financial institutions. Introducing position based trading avoids inconsistencies in traders inventories resulting from the order based setup of the original model, while preserving its ability to reproduce the stylized facts of financial return series.      

中国碳排放权交易市场收益率波动性特征研究

以北京、上海、广东、湖北和重庆碳排放权交易市场为研究对象,运用GARCH族模型研究中国碳排放权交易市场收益率波动性特征。结果表明,5个碳排放权交易市场收益率的波动聚集性、持续性表现不完全一致;北京、上海和重庆存在负向的杠杆效应,广东和湖北不存在杠杆效应;从波动溢出效应关系看,5个碳排放权交易市场间的整体联动性不强;运用方差比率检验法得出,5个碳排放权交易市场均未达到弱势有效市场。这些特征反映出中国碳排放权交易市场的运行机制仍然存在缺陷,建议加强顶层设计,完善碳排放权交易体系。     

亚洲地区股票指数收益率的波动性研究——基于GARCH族模型

为防范股票市场上的不确定性和风险,有效地度量股票指数收益率的波动性显得尤为重要。本文运用GARCH族模型,拟合了股票指数收益率的波动性方程,并实证研究了亚洲地区四个最具代表性国家:日本、中国、印度和韩国的股票指数收益率的波动性。结果表明:亚洲地区股票指数收益率的波动呈现出聚集性和持续性,股票市场存在着冲击的非对称性;中国和印度的股票市场抗风险能力比日本和韩国弱,股票指数收益率的波动性带来的负面影响更大。