Price discovery and volatility spillovers in index futures markets: Some evidence from Mexico

This paper investigates the hypotheses that the recently established Mexican stock index futures market effectively serves the price discovery function, and that the introduction of futures trading has provoked volatility in the underlying spot market. We test both hypotheses simultaneously with daily data from Mexico in the context of a modified EGARCH model that also incorporates possible cointegration between the futures and spot markets. The evidence supports both hypotheses, suggesting that the futures market in Mexico is a useful price discovery vehicle, although futures trading has also been a source of instability for the spot market. Several managerial implications are derived and discussed.     

Modelling International Price Relationships and Interdependencies Between the Stock Index and Stock Index Futures Markets of Three EU Countries: A Multivariate Analysis

This paper addresses the important relationship between stock index and stock index futures markets in an international context. By simply examining the spot‐futures relationship within a single country as most of the extant literature does and thus ignoring possible market interdependencies between countries, the dynamics of price adjustments may be misspecified and thus findings misleading. The main contribution of the paper is to improve our understanding of the pricing relationship between spot and futures markets in the light of international market interdependencies. Using a multivariate VAR‐EGARCH methodology, the paper investigates stock index and stock index futures market interdependence, that is lead‐lag relationships and volatility interactions between the stock and futures markets of three main European countries, namely France, Germany and the UK. In addition, the paper explicitly accounts for potential asymmetries that may exist in the volatility transmission mechanism between these markets. The main conclusions of the paper imply that investors need to account for market interactions across countries to fully and correctly exploit the potential for hedging and diversification.     

On the Role of Futures Trading in Spot Market Fluctuations: Perpetrator of Volatility or Victim of Regret?

We examine the role of index futures trading in spot market volatility. We use the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) approach to measure volatility, analyze causality and feedback relations between volatilities in the spot and futures markets, and test various hypotheses in the context of a multivariate model that incorporates other macrostate variables. Our empirical results suggest index futures trading may not be blamed for the observed volatility in the spot market. Rather, we find stronger and more consistent support for the alternative posture that volatility in the futures market is an outgrowth of a turbulent cash market. We use the regret (cognitive dissonance) theory to explain our results.     

Forecasting stock market volatility: Further international evidence

Abstract

This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second half is for the forecast period. The following models are employed: a random walk model, a historical mean model, moving average models, weighted moving average models, exponentially weighted moving average models, an exponential smoothing model, a regression model, an ARCH model, a GARCH model, a GJR-GARCH model, and an EGARCH model. First, standard (symmetric) loss functions are used to evaluate the performance of the competing models: mean absolute error, root mean squared error, and mean absolute percentage error. According to all of these standard loss functions, the exponential smoothing model provides superior forecasts of volatility. On the other hand, ARCH-based models generally prove to be the worst forecasting models. Asymmetric loss functions are employed to penalize under-/over-prediction. When under-predictions are penalized more heavily, ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalized more heavily, the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters.     

Value-at-Risk Analysis for Taiwan Stock Index Futures: Fat Tails and Conditional Asymmetries in Return Innovations

This paper examines the forecasting performance of three value-at-risk (VaR) models (RiskMetrics, Normal APARCH and Student APARCH). We explore and compare two different possible sources of performance improvements: asymmetry in the conditional variance and fat-tailed distributions. Performance is assessed using a range of measures that address the accuracy and efficiency of each model.The TAIFEX and SGX-DT Taiwan stock index futures are studied using daily data. Our results suggest that for asset returns which exhibit fatter tails and volatility clustering, like the TAIFEX and SGX-DT futures, the VaR values produced by the Normal APARCH model are preferred at lower confidence levels. However, at high confidence levels, the VaR forecasts obtained by the Student APARCH model are more accurate than those generated using either the RiskMetrics or Normal APARCH models.     

基于滚动窗口马尔科夫链预测模型的股票指数波动情况研究

文章运用基于滚动窗口的马尔科夫链预测模型,对上证综指的变动进行研究,创新地给出概率转移矩阵、极限概率以及预测准确率的时变特征,并首次给出马尔科夫链预测模型的最优窗口长度和状态定义阀值。研究揭示,大盘波动幅度与大盘的极限概率有着密切的关系;股指期货推出后大盘平盘概率占据主导地位,平稳性显著提高,马尔科夫链预测模型的预测准确率也有了较大提高。     

Short-term Dynamics in the Cyprus Stock Exchange

Abstract

This paper investigates the short-term dynamics of stock returns in an emerging stock market namely, the Cyprus Stock Exchange (CYSE). Stock returns are modelled as conditionally heteroscedastic processes with time-dependent serial correlation. The conditional variance follows an EGARCH process, while for the conditional mean three nonlinear specifications are tested, namely: (a) the LeBaron exponential autoregressive model; (b) the Sentana and Wadhwani positive feedback trading model; and finally (c) a model that nests both (a) and (b). There is an inverse relationship between volatility and autocorrelation consistent with the findings from several other stock markets, including the US. This pattern could be the manifestation of a certain form of noise trading namely positive feedback trading or, momentum trading strategies. There is little evidence that market declines are followed with higher volatility than market advances, the so-called ‘leverage effect’, that has been observed in almost all developed stock markets. In out of sample forecasts, the nonlinear specifications provide better results in terms of forecasting both first and second moments of the distribution of returns.     

Value-at-Risk Forecasting of Chinese Stock Index and Index Future Under Jumps,Permanent Component,and Asymmetric Information

This article investigates the performance of time series models considering the jumps, permanent component of volatility, and asymmetric information in predicting value-at-risk (VaR). We use evaluation statistics including size and variability, accuracy, and efficiency to determine some suitable VaR measures for the Chinese stock index and its futures. The results reveal that models with jumps can provide VaR series that are less average conservative and have higher variability. Furthermore, additional considering the permanent component of volatility and asymmetric effect can induce more accurate and efficient risk measure in the long and short positions of the stock index and its futures.     

上证指数收益率波动的实证分析：基于ARCH族模型

股价指数的收益率序列具有时变波动性、厚尾特征、波动性群集等特点,传统的计量分析无法刻画这些特点。文章利用ARCH族模型,选取2003年1月20日～2013年12月12日上证指数每日收益率共2621个数据对其波动进行定量与定性的分析,结果显示,上证指数日收益率存在高阶的ARCH效应,杠杆效应,波动集聚性特征,条件方差对日收益率有很强的影响,其中EGARCH模型在反映股市波动性方面优于其他模型。     

Mean and volatility dynamics of Indian rupee/US dollar exchange rate series: an empirical investigation

The paper is concerned with time series modelling of foreign exchange rate of an important emerging economy, viz., India, with due consideration to possible sources of misspecification of the conditional mean like serial correlation, parameter instability, omitted time series variables and nonlinear dependences. Since structural change is pervasive in economic time series relationships, the paper first studies this aspect of the exchange rate series in detail and finds the existence of four structural breaks. Accordingly, the entire sample period is divided into five sub-periods of stable parameters each, and then the appropriate mean specification for each of these sub-periods is determined by incorporating functions of recursive residuals. Thereafter, the GARCH and EGARCH models are considered to capture the volatility contained in the data. The estimated models thus obtained suggest that return on Indian exchange rate series is marked by instabilities and that the appropriate volatility model is EGARCH. Further, out-of-sample forecasting performance of the model has been studied by standard forecasting criteria, and then compared with that of an AR model only to find that the findings are quite favorable for the former.      

沪深300股指期货与现货市场间风险传染效应及影响因素

本文选取2005—2019年我国沪深300股指期货和沪深300股票指数日收盘价数据,结合股票推出时间、股价波动性,设置样本组、对照组,运用GARCH模型、DCC-GARCH模型、Granger因果关系检验及多元线性回归模型分析了沪深300股指期货与现货间的风险传染效应及影响因素,并结合研究结论提出对策,以期促进资本市场健康发展。结果表明:沪深300股指期货市场与现货市场间存在双向的风险传染效应,且经DCC-GARCH模型分析表明风险传染效应在动荡期尤为明显;影响这种风险传染效应的因素有很多,主要表现为微观因素中的股票市场流动性和股票市场不确定性与极端事件两个方面。     

Volatility Forecasting in the Hang Seng Index using the GARCH Approach

The aim of this paper is to add to the literature on volatility forecasting using data from the Hong Kong stock market to determine if forecasts from GARCH based models can outperform simple historical averaging models. Overall, unlike previous studies we find that the GARCH models with non-Normal distributions show a robust volatility forecasting performance in comparison to the historical models. The results indicate that although not all models outperform simple historical averaging, the EGARCH based models, with non-normal conditional volatility, tend to produce more accurate out-of-sample forecasts using both standard measures of forecast accuracy and financial loss functions. In addition we test for asymmetric adjustment in the Hang Seng, finding strong evidence of asymmetries due to the domination of financial and property firms in this market.     

我国股指期货与现货市场信息传递与波动溢出关系研究

股指期货与现货市场关系是监管者关注的重点问题。本文采用我国股指期货上市以来1分钟级高频数据,应用向量误差修正模型、方差分解、多元T-GARCH等,考察期现两市信息传递、波动溢出效应的影响。实证结果表明,尽管股指期货和股票市场之间短期内存在相互引导关系,但股票市场价格变动更多来自于自身影响,起主导作用,而且两市长期均衡收敛也是以股票市场占主导地位;两市存在显著的双向波动溢出,期货市场的波动溢出效应强于股票市场的波动溢出效应;两市场存在明显的非对称效应,期货市场对坏消息更为敏感,而现货市场对好消息更为敏感。     

Volatility spillover and investment strategies among sustainability-related financial indexes: Evidence from the DCC-GARCH-based dynamic connectedness and DCC-GARCH t-copula approach

This study analyzes the dynamic connectedness between the ESG stock index, the renewable energy stock index, the green bond stock index, the sustainability stock index, and the carbon emission futures by employing a novel method: the DCC-GARCH-based dynamic connectedness approach. Given the strong volatility spillover among these indexes, we adopt the DCC-GARCH t-copula model to calculate these indexes' hedging ratios and portfolio weights. Our findings show that the carbon emission futures are the volatility transmitter, and the green bond is the volatility receiver. The total dynamic connectedness is affected by international political, economic, and other events. Furthermore, for stock market volatility investors, taking the long position in carbon emission futures and the short position in renewable energy stock can achieve the highest hedging effect.     

Intra- and inter-regional return and volatility spillovers across emerging and developed markets: Evidence from stock indices and stock index futures

We provide empirical evidence on the patterns of intra- and inter-regional transmission of information across 10 developed and 11 emerging markets in Asia, the Americas, Europe and Africa using both stock indices and stock index futures. The main transmission channels are examined in the period from 2005 to 2014 through the analysis of return and volatility spillovers around the most recent crises based on the generalized vector autoregressive framework. Our findings demonstrate that markets are more susceptible to domestic and region-specific volatility shocks than to inter-regional contagion. A novel result reported in our study is a difference in patterns of international signals transmission between models employing indices and futures data. We conclude that futures data provide more efficient channels of information transmission because the magnitude of return and volatility spillovers across futures is larger than across indices. Our findings are relevant to practitioners, such as stock market investors, as well as policy makers and can help enhance their understanding of financial markets interconnectedness.     

基于人工神经网络的沪锌期货价格预测研究

分析沪锌期货的特征,发现沪锌期货价格存在非线性和波动集聚性的特点.选择沪锌期货的相关指标作为参数,运用人工神经网络训练数据,进行价格涨跌预测,构建BP神经网络和卷积神经网络沪锌期货预测模型.实证研究结果表明:模型预测准确率高,预测效果良好,在盘整行情中可获得较高收益,为投资决策提供重要参考,并可在期货市场中进行广泛应用.     

The forecasting ability of solar and space weather data on NASDAQ’s finance sector price index volatility

We examine the impact of solar and space weather events on the Financial Select Sector SPDR Fund (XLF) price index volatility, spanning the period 1998-2018. Comparing MAPE and RMSFE forecasting criteria, for the ARIMA-GARCH model, augmented with exogenous variables, we find that solar and space weather variables contribute statistically significant information with regard to volatility forecasting.     

Impact of speculation and economic uncertainty on commodity markets

We examine the interactions between commodity futures returns and five driving factors (financial speculation, exchange rate, stock market dynamics, implied volatility for the US equity market, and economic policy uncertainty). Nonlinear causality tests are implemented after controlling for cointegration and conditional heteroscedasticity in the data over the period May 1990 – April 2014. Our results show strong evidence of unidirectional linear causality from commodity returns to excess speculation for the majority of the considered commodities, in particular for agriculture commodities. This evidence casts doubt on the claim that speculation is driving food prices. We also find unidirectional linear causality from energy futures markets to exchange rates and strong evidence of nonlinear causal dependence between commodity futures returns, on the one hand, and stock market returns and implied volatility, on the other hand. Overall, the new evidence found in this paper can be utilized for policy and investment decision-making.     

Examining the volatility of soybean market in the MIDAS framework: The importance of bagging-based weather information

Based on the close relationship between the global soybean market and weather variables, current studies regarding soybean volatility forecasting under weather information are limited. The aim of our study is to fill this gap and examine the predictive power of soybean volatility by separately adding normal and bagging-based weather information. Methodologically, two types of extended GARCH-MIDAS approaches with weather variables, the GARCH-MIDAS-W and GARCH-MIDAS-W-MBB models, are first introduced into soybean volatility forecasting. By using the prices of soybean futures and weather information including clear-sky index, cloud cover, relative humidity, atmospheric pressure, precipitation, temperature and wind speed, our findings provide fresh evidence that predictive models that incorporate bagging-based weather information significantly outperform the models with raw weather indicators and the model without weather information. Finally, our conclusions are robust to further robustness checks. Our novel bagging-related GARCH-MIDAS-W-MBB model with weather indicators can provide fresh insights into soybean volatility forecasting.     

The innovations of e-mini contracts and futures price volatility components: The empirical investigation of S&P 500 stock index futures

The effect of the initiation of e-mini stock index futures (ESIFs) on the volatility components of S&P 500 stock index futures is herein investigated. The study decomposes S&P 500 stock index-related observed volatilities into unobserved fundamental volatility and transitory noise and utilizes the decomposition to test two hypotheses: the “clientele factor hypothesis” and the “information adjustment hypothesis”. The first hypothesis proposes that the ESIFs attract more noisy traders who prefer trading the friendly-size futures contracts. The second one proposes that the innovations of ESIFs improve the information flow of the futures markets. Using a stochastic volatility model, the empirical results are consistent with both of our proposed hypotheses.