Time‐varying jump risk premia in stock index futures returns

This study tests the presence of time‐varying risk premia associated with extreme news events or jumps in stock index futures return. The model allows for a dynamic jump component with autoregressive jump intensity, long‐range dependence in volatility dynamics, and a volatility in mean structure separately for the normal and extreme news events. The results show significant jump risk premia in four stock market index futures returns including the DAX, FTSE, Nikkei, and S&P500 indices. Our results are robust to various specifications of conditional variance including the plain GARCH, component GARCH, and Fractionally Integrated GARCH models. We also find the time‐varying risk premium associated with normal news events is not significant across all indices. © 2011 Wiley Periodicals, Inc. Jrl Fut Mark 32:639–659, 2012     

Improving volatility prediction and option valuation using VIX information: A volatility spillover GARCH model

We develop a new generalized autoregressive conditional heteroskedasticity (GARCH) model that accounts for the information spillover between two markets. This model is used to detect the usefulness of the CBOE volatility index (VIX) for improving the performance of volatility forecasting and option pricing. We find the significant ability of VIX to predict stock volatility both in-sample and out-of-sample. VIX information also helps to greatly reduce the option pricing error. The proposed volatility spillover GARCH model performs better than the related approaches proposed by Kanniainen et al. (2014, J Bank Finance, 43, pp. 200-211) and P. Christoffersen et al. (2014, J Financ Quant Anal, 49, pp. 663–697).     

股指期货与股票市场波动性关系的实证研究

以日本的N225指数期货、韩国KOSPI200指数期货和我国台湾地区证交所加权指数(TWSE)期货作为样本,通过GARCH模型的序列建模,从样本总体和分阶段子样本分别对其股指期货推出与股票市场波动性的关系进行实证检验。结果表明,台湾地区的股票市场引入股指期货后现货市场的波动性并未受到影响,而日本和韩国股票市场在引入股指期货之后其波动性加剧,但这种波动性的加剧仅仅是短期性的,长期内并无影响。     

New evidence on expiration‐day effects using realized volatility: An intraday analysis for the Spanish stock exchange

Additional evidence is provided on expiration effects in the Ibex 35 stock index futures market using realized volatility as proposed by T. G. Andersen, T. Bollerslev, F. X. Diebold, and P. Labys (2003). Findings reveal not only a significant increase in spot trading activity, but also the existence of a significant jump in spot volatility at index futures expiration. Moreover, the importance of the data frequency considered is analyzed. Our research reveals that the use of GARCH methodology from daily data does not have the ability to statistically assess such expiration‐day effect. Additional empirical evidence is provided for the S&P 500 stock index futures market. Neither unconditional nor conditional realized volatility has a significant increase at expiration for the U.S. market, suggesting that this effect is specific for the Spanish market, at least for the period analyzed. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:923–938, 2006     

Testing mean reversion in financial market volatility: Evidence from S&P 500 index futures

This article presents a comprehensive study of continuous time GARCH (generalized autoregressive conditional heteroskedastic) modeling with the thintailed normal and the fat‐tailed Student's‐t and generalized error distributions (GED). The study measures the degree of mean reversion in financial market volatility based on the relationship between discrete‐time GARCH and continuoustime diffusion models. The convergence results based on the aforementioned distribution functions are shown to have similar implications for testing mean reversion in stochastic volatility. Alternative models are compared in terms of their ability to capture mean‐reverting behavior of futures market volatility. The empirical evidence obtained from the S&P 500 index futures indicates that the conditional variance, log‐variance, and standard deviation of futures returns are pulled back to some long‐run average level over time. The study also compares the performance of alternative GARCH models with normal, Student's‐ t, and GED density in terms of their power to predict one‐day‐ahead realized volatility of index futures returns and provides some implications for pricing futures options. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1–33, 2008     

上证50指数收益率特征及其波动性分析

研究国内非综合指数即成份指数(上证50指数)的收益率特征及其波动性,可以估计得出对指数风险收益具有较好预测作用的自回归———GARCH(1,1)-M模型,并实证分析指数收益的风险特性、稳定性、波动性等特征,这对当前探讨上证50指数相关指数衍生品推出及其投资分析均具有重要意义。     

Implied volatility forecasts in the grains complex

This article finds that the implied volatilities of corn, soybean, and wheat futures options 4 weeks before option expiration have significant predictive power for the underlying futures contract return volatilities through option expiration from January 1988 through September 1999. These implied volatilities also encompass the information in out‐of‐sample seasonal Glosten, Jagannathan, and Runkle (GJR;1993) volatility forecasts. Evidence also demonstrates that when corn‐implied volatility rises relative to out‐of‐sample seasonal GJR volatility forecasts, implied volatility substantially overpredicts realized volatility. However, simulations of trading rules that involve selling corn option straddles when corn‐implied volatility is high relative to out‐of‐sample GJR volatility forecasts indicate that none of the trading rules would have been significantly profitable. This finding suggests that these options are not necessarily overpriced. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:959–981, 2002     

Switching asymmetric GARCH and options on a volatility index

Few proposed types of derivative securities have attracted as much attention and interest as option contracts on volatility. Grunbichler and Longstaff (1996) is the only study that proposes a model to value options written on a volatility index. Their model, which is based on modeling volatility as a GARCH process, does not take into account the switching regime and asymmetry properties of volatility. We show that the Grunbichler and Longstaff (1996) model underprices a three‐month option by about 10%. A Switching Regime Asymmetric GARCH is used to model the generating process of security returns. The comparison between the switching regime model and the traditional uni‐regime model among GARCH, EGARCH, and GJR‐GARCH demonstrates that a switching regime EGARCH model fits the data best. Next, the values of European call options written on a volatility index are computed using Monte Carlo integration. When comparing the values of the option based on the Switching Regime Asymmetric GARCH model and the traditional GARCH specification, it is found that the option values obtained from the different processes are very different. This clearly shows that the Grunbichler‐Longstaff model is too stylized to be used in pricing derivatives on a volatility index. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:251–282, 2004     

Value at risk and conditional extreme value theory via markov regime switching models

This study develops a new conditional extreme value theory‐based (EVT) model that incorporates the Markov regime switching process to forecast extreme risks in the stock markets. The study combines the Markov switching ARCH (SWARCH) model (which uses different sets of parameters for various states to cope with the structural changes for measuring the time‐varying volatility of the return distribution) with the EVT to model the tail distribution of the SWARCH processed residuals. The model is compared with unconditional EVT and conditional EVT‐GARCH models to estimate the extreme losses in three leading stock indices: S&P 500 Index, Hang Seng Index and Hang Seng China Enterprise Index. The study found that the EVT‐SWARCH model outperformed both the GARCH and SWARCH models in capturing the non‐normality and in providing accurate value‐at‐risk forecasts in the in‐sample and out‐sample tests. The EVTSWARCH model, which exhibits the features of measuring the volatility of a heteroscedastic financial return series and coping with the non‐normality owing to structural changes, can be an alternative measure of the tail risk. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:155–181, 2008     

中国股市波动的CARR模型分析

ARCH/GARCH模型在波动性的预测已被学者广泛使用并在实证上得到良好的效果。本文以上海股市为研究对象,分别运用CARR模型和GARCH模型进行波动性预测,进而对两种方法的预测能力进行比较,实证结果表明CARR模型在拟合波动性方面优于GARCH模型。     

The impact of volatility derivatives on S&P500 volatility

This study investigates whether the newly cultivated platform of volatility derivatives has altered the volatility of the underlying S&P500 index. The findings suggest that the onset of the volatility derivatives trading has lowered the volatility of both the cash market volatility and the cash market index, and significantly reduced the impact of shocks to volatility. When big sudden events hit financial markets, however, the volatility of volatility seems to elevate in the U.S. equity market as a result of increased global correlations. Regardless of the period under examination and the estimator employed, long‐run volatility persistence is present. The latter drops significantly when the credit crunch period is excluded from the post‐event date sample period. The correlation between the broad equity index and the return volatility remains low, which in turn strengthens the role of volatility derivatives to facilitate portfolio diversification. The analysis also shows that volatility is mean reverting, whereas market data support the impact of information asymmetries on conditional volatility. In the post‐event date phase, no asymmetries are found when the recent crisis is not accounted for. Finally, comparisons with other international equity indices, with no volatility derivatives listed, unveil that these indices exhibit higher volatility and slower recovery from shocks than the S&P500 index. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 29:1190–1213, 2009     

Bid and ask prices of index put options: Which predicts the underlying stock returns?

In this study, we separately estimate the implied volatility from the bid and ask prices of deep out-of-the-money put options on the S&P500 index. We find that the implied volatility of ask prices has stronger predictive power for stock returns than does the implied volatility of bid prices. We identify two sources of the better performance of the ask price implied volatility: one is its stronger predictive power during economic recessions and in the presence of increasing intermediary capital risk, and the other is its richer information about the future market variance risk premium.     

中国线材期货价格指数波动率预测能力分析

利用上海期货交易所线材期货15分钟高频价格数据构造已实现波动率估计序列,并以此作为参考标准,运用6种损失函数以及Diebold-Mariano检验法检验4类不同波动率模型对线材期货价格波动的样本外预测能力,显示,中国线材期货市场,基于高频数据的GJR(1,1)模型具有最为出色的波动率预测能力,而在某些损失函数标准下,HYGARCH(1,d,1)与GARCH(1,1)模型也体现出了较好的波动率预测能力。     

Intraday price discovery and volatility transmission in stock index and stock index futures markets: Evidence from China

Using high‐frequency data, this study investigates intraday price discovery and volatility transmission between the Chinese stock index and the newly established stock index futures markets in China. Although the Chinese stock index started a sharp decline immediately after the stock index futures were introduced, the cash market is found to play a more dominant role in the price discovery process. The new stock index futures market does not function well in its price discovery performance at its infancy stage, apparently due to high barriers to entry into this emerging futures market. Based on a newly proposed theoretically consistent asymmetric GARCH model, the results uncover strong bidirectional dependence in the intraday volatility of both markets. © 2011 Wiley Periodicals, Inc. Jrl Fut Mark     

Intradaily periodicity and volatility spillovers between international stock index futures markets

This paper examines short‐run information transmission between the U.S. and U.K. markets using the S&P 500 and FTSE 100 index futures. Ultrahighfrequency futures data are employed—which have a number of advantages over the low‐frequency spot data commonly used in previous studies—in establishing that volatility spillovers are in fact bidirectional. The generalized autoregressive conditionally heteroskedastic model (GARCH) is employed to estimate the mean and volatility spillovers of intraday returns. A Fourier flexible function is utilized to filter the intradaily periodic patterns that induce serial correlation in return volatility. It was found that estimates of volatility persistence and speed of information transmission are seriously affected by intradaily periodicity. The bias in parameter estimation is removed by filtering out the intradaily periodic component of the transaction data. Contrary to previous findings, there is evidence of spillovers in volatility between the U.S. and U.K. markets. Results indicate that the volatility of the U.S. market is affected by the most recent volatility surprise in the U.K. market. © 2005 Wiley Periodicals, Inc. Jrl Fut Mark 25:553–585, 2005     

The bias in time series volatility forecasts

By Jensen's inequality, a model's forecasts of the variance and standard deviation of returns cannot both be unbiased. This study explores the bias in GARCH type model forecasts of the standard deviation of returns, which we argue is the more appropriate volatility measure for most financial applications. For a wide variety of markets, the GARCH, EGARCH, and GJR (or TGARCH) models tend to persistently over‐estimate the standard deviation of returns, whereas the ARLS model of L. Ederington and W. Guan (2005a) does not. Furthermore, the GARCH and GJR forecasts are especially biased following high volatility days, which cause a large jump in forecast volatility, which is rarely fully realized. © 2009 Wiley Periodicals, Inc. Jrl Fut Mark 30:305–323, 2010     

The information content of implied volatility in light of the jump/continuous decomposition of realized volatility

In the framework of encompassing regressions, the information content of the jump/continuous components of historical volatility is assessed when implied volatility is included as an additional regressor. The authors' empirical application focuses on daily and intradaily data for the S&P100 and S&P500 indexes, and daily data for the associated VXO and VIX implied volatility indexes. The results show that the total explanatory power of the encompassing regressions barely changes when the jump/continuous components are included, although the weekly and monthly continuous components are usually significant. This evidence supports the view that implied volatility has very high information content, even when extended decompositions of past realized volatility are used. Moreover, adding GARCH‐type volatility forecasts in the regressions confirms these results. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:337–359, 2007     

Conditional Volatility and the GARCH Option Pricing Model with Non‐Normal Innovations

On the basis of the theory of a wedge between the physical and risk‐neutral conditional volatilities in Christoffersen, P., Elkamhi, R., Feunou, B., & Jacobs, K. (2010), we develop a modification of the GARCH option pricing model with the filtered historical simulation proposed in Barone‐Adesi, G., Engle, R. F., & Mancini, L. (2008). The one‐day‐ahead conditional volatilities under physical and risk‐neutral measures are the same in the previous model, but should have been allowed to be different. Using extensive data on S&P 500 index options, our approach, which employs one‐day‐ahead risk‐neutral conditional volatility estimated from the cross‐section of the option prices (in contrast to the existing GARCH option pricing models), maintains theoretical consistency under conditional non‐normality, and improves the empirical performances. Remarkably, the risk‐neutral volatility dynamics are stable over time in this model. In addition, the comparison between the VIX index and the risk‐neutral integrated volatility economically validates our approach. © 2011 Wiley Periodicals, Inc. Jrl Fut Mark 33:1–28, 2013     

Cash trading and index futures price volatility

This study examines the effect of cash market liquidity on the volatility of stock index futures. Two facets of cash market liquidity are considered: (1) the level of liquidity trading proxied by the expected New York Stock Exchange (NYSE) trading volume and (2) the noise composition of trading proxied by the average NYSE trading commission cost. Under the framework of spline–GARCH with a liquidity component, both the quarterly average commission cost and the quarterly expected NYSE volume are negatively associated with the ex ante daily volatility of S&P 500 and NYSE composite index futures. Conversely, liquidity and noise trading in the cash market both dampen futures price volatility, ceteris paribus. This negative association between secular cash trading liquidity and daily futures price volatility is amplified during times of market crisis. These results retain statistical significance and materiality after controlling for bid–ask bounce of futures prices and volume of traded futures contracts. This study establishes empirical evidence to affirm the conventional prediction of a liquidity–volatility relationship: the liquidity effect is secular and persistent across markets. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:465–486, 2011     

A Markov regime switching approach for hedging stock indices

In this paper we describe a new approach for determining time‐varying minimum variance hedge ratio in stock index futures markets by using Markov Regime Switching (MRS) models. The rationale behind the use of these models stems from the fact that the dynamic relationship between spot and futures returns may be characterized by regime shifts, which, in turn, suggests that by allowing the hedge ratio to be dependent upon the “state of the market,” one may obtain more efficient hedge ratios and hence, superior hedging performance compared to other methods in the literature. The performance of the MRS hedge ratios is compared to that of alternative models such as GARCH, Error Correction and OLS in the FTSE 100 and S&P 500 markets. In and out‐of‐sample tests indicate that MRS hedge ratios outperform the other models in reducing portfolio risk in the FTSE 100 market. In the S&P 500 market the MRS model outperforms the other hedging strategies only within sample. Overall, the results indicate that by using MRS models market agents may be able to increase the performance of their hedges, measured in terms of variance reduction and increase in their utility. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:649–674, 2004