Intraday volatility in the bond,foreign exchange,and stock index futures markets

Intraday volatility for the Eurodollar, the Euro/dollar foreign exchange rate, and the E‐mini S&P 500 futures contracts traded on a continuous 23‐hour schedule on the Chicago Mercantile Exchange Globex electronic platform is studied. Volatility transmission in a single market across different regions is mainly explained by intraregion volatility (heat waves); interregion volatility (meteor showers) plays a secondary role. The joint impact of liquidity variables such as volume and open interest on volatility is also analyzed. Volume tends to increase volatility, but open interest does not affect it. The results are explained by the type of trading venue. Unlike floor‐based trading systems, in electronic markets open interest does not seem to provide additional information on market liquidity and its relation to volatility beyond any information contributed by volume. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:313– 334, 2008     

我国股票市场收益、交易量、波动性动态关系的实证分析

本文对我国股票市场上证指数和深圳成指的收益、交易量、波动性之间的动态关系进行了实证研究,研究结果表明:收益和绝对收益与交易量之间均存在正相关关系;收益与交易量以及绝对收益与交易量之间存在双向Granger因果关系(线性或非线性);深圳成指收益的波动方差对收益具有正向作用,而上证指数收益的波动方差对收益没有直接的影响;上证指数和深圳成指的成交量对股指收益的波动方差不具有解释作用.     

中国开放式基金收益及其波动性的周内效应研究

了解基金收益及其波动性是否存在周内效应对投资者非常重要,投资者可以利用收益及其波动性的变动信息调整投资组合,增加投资收益。运用均值方程含有虚拟变量的GARCH(1,1)模型和条件方差方程含有虚拟变量的修正的GARCH(1,1)模型,我们分别对2003年6月1日至2005年8月18日期间中国开放式基金收益的周内效应和收益波动性的周内效应进行实证研究,结果显示,在研究期间内样本基金收益及收益的波动性在周三这一天显著不同于其他交易日,即存在“周三效应”。     

Return distributions and volatility forecasting in metal futures markets: Evidence from gold,silver, and copper

The characterization of return distributions and forecast of asset‐price variability play a critical role in the study of financial markets. This study estimates four measures of integrated volatility—daily absolute returns, realized volatility, realized bipower volatility, and integrated volatility via Fourier transformation (IVFT)—for gold, silver, and copper by using high‐frequency data for the period 1999 through 2008. The distributional properties are investigated by applying recently developed jump detection procedures and by constructing financial‐time return series. The predictive ability of a GARCH (1,1) forecasting model that uses various volatility measures is also examined. Three important findings are reported. First, the magnitude of the IVFT volatility estimate is the greatest among the four volatility measures. Second, the return distributions of the three markets are not normal. However, when returns are standardized by IVFT and realized volatility, the corresponding return distributions bear closer resemblance to a normal distribution. Notably, the application of financial‐time sampling technique is helpful in obtaining a normal distribution. Finally, the IVFT and realized volatility proxies produce the smallest forecasting errors, and increasing the time frequency of estimating integrated volatility does not necessarily improve forecast accuracy. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:55–80, 2011     

What Drives European Football Clubs’ Stock Returns and Volatility?

The article studies the main determinants of European football clubs’ stock returns and volatility. A panel-data analysis of a sample of 24 European football clubs was conducted to test the influence of several variables, based on a matrix of internal/external and real/financial dimensions, on both stock returns and their volatility. The results show that clubs’ stock returns are influenced by the real and financial context and by a set of internal variables such as profit considered as a reflection of accounting discipline, capitalization as an indicator of size and stadium attendance as a proxy indicator of reputation. The volatility of stock returns seems particularly vulnerable to the overall instability on stock markets and dependent on clubs’ profit and net players’ transfers and, to a lesser extent, on sporting outcomes.     

Realized volatility and correlation in energy futures markets

Using high‐frequency returns, realized volatility and correlation of the NYMEX light, sweet crude oil, and Henry‐Hub natural gas futures contracts are examined. The unconditional distributions of daily returns and daily realized variances are non‐Gaussian, whereas the distributions of the standardized returns (normalized by the realized standard deviation) and the (logarithms of) realized standard deviations appear approximately Gaussian. The (logarithms of) standard deviations exhibit long‐memory, but the realized correlation between the two futures does not, implying rather weak inter‐market linkage in the long run. There is evidence of asymmetric volatility for natural gas but not for crude oil futures. Finally, realized crude oil futures volatility responds with an increase in the weeks immediately before the OPEC events recommending price increases. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:993–1011, 2008     

The information content in implied idiosyncratic volatility and the cross‐section of stock returns: Evidence from the option markets

Current literature is inconclusive as to whether idiosyncratic risk influences future stock returns and the direction of the impact. Earlier studies are based on historical realized volatility. Implied volatilities from option prices represent the market's assessment of future risk and are likely a superior measure to historical realized volatility. Implied idiosyncratic volatilities on firms with traded options are used to examine the relationship between idiosyncratic volatility and future returns. A strong positive link was found between implied idiosyncratic risk and future returns. After considering the impact of implied idiosyncratic volatility, historical realized idiosyncratic volatility is unimportant. This performance is strongly tied to small size and high book‐to‐market equity firms. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28: 1013–1039, 2008     

Asymmetric pricing of implied systematic volatility in the cross‐section of expected returns

Assuming a symmetric relation between returns and innovations in implied market volatility, Ang, A., Hodrick, R., Xing, Y., and Zhang, X. (2006) find that sensitivities to changes in implied market volatility have a cross‐sectional effect on firm returns. Dennis, P., Mayhew, S., and Stivers, C. (2006), however, find an asymmetric relation between firm‐level returns and implied market volatility innovations. We incorporate this asymmetry into the cross‐sectional relation between sensitivity to volatility innovations and returns. Using both portfolio sorting and firm‐level regressions, we find that sensitivity to VIX innovations is negatively related to returns when volatility is rising, but is unrelated when it is falling. The negative relation is robust to controls for other variables, suggesting only the increase in implied market volatility is a priced risk factor. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:34–54, 2011     

Is volatility risk priced in the securities market? Evidence from S&P 500 index options

The authors examine whether volatility risk is a priced risk factor in securities returns. Zero‐beta at‐the‐money straddle returns of the S&P 500 index are used to measure volatility risk. It is demonstrated that volatility risk captures time variation in the stochastic discount factor. The results suggest that straddle returns are important conditioning variables in asset pricing, and investors use straddle returns when forming their expectations about securities returns. One interesting finding is that different classes of firms react differently to volatility risk. For example, small firms and value firms have negative and significant volatility coefficients, whereas big firms and growth firms have positive and significant volatility coefficients during high‐volatility periods, indicating that investors see these latter firms as hedges against volatile states of the economy. Overall, these findings have important implications for portfolio formation, risk management, and hedging strategies. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27:617–642, 2007     

An examination of the complementary volume–volatility information theories

The volume–volatility relationship during the dissemination stages of information flow is examined by analyzing various theories relating volume and volatility as complementary rather than competing models. The mixture of distributions hypothesis, sequential arrival of information hypothesis, the dispersion of beliefs hypothesis, and the noise trader hypothesis all add to the understanding of how volume and volatility interact for different types of futures traders. An integrated picture of the volume–volatility relationship is provided by investigating the dynamic linear and nonlinear associations between volatility and the volume of informed (institutional) and uninformed (the general public) traders. In particular, the trading behavior explanation for the persistence of futures volatility, the effect of the timing of private information arrival, and the response of institutional traders to excess noise trading risk is examined. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:963–992, 2008     

Factors explaining movements in the implied volatility surface

This article explores the relationship of changes in the S&P 500 index implied volatility surface to economic state variables. Observable variables can explain some of the variation in implied volatility, with the majority of explanatory power from index returns. Although the contemporaneous return is most important for explaining changes in short dated volatility, the path of the index is important for explaining changes in long dated volatility. Other variables also display statistically significant relations to volatility changes. Shocks to the Nikkei 225, short‐term interest rates, and the corporate/government bond yield spread are correlated with small, systematic changes in implied volatility. The results suggest a multifactor model for market volatility, with factors other than index returns adding negligible explanatory ability. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:915–937, 2002     

Volatility,global information,and market conditions: a study in futures markets

This study examined the behavior of return volatility in relation to the timing of information flow under different market conditions influenced by trading volume and market depth. We emphasized information flow during trading and nontrading periods that may represent domestic and offshore information in the global trading of currencies. Test results show that volatility was negatively related to market depth; that is, deeper markets had relatively less return volatility. Additionally, the effect that market depth had on volatility was superseded by information within trading volume. Test results focusing on the timing of information flow reveal that in low‐volume markets, the volatility of nontrading‐period returns exceeded the volatility of trading‐period returns. However, when trading volume was high, this pattern was reversed and conformed to the observations of earlier articles. Our findings proved to be robust across time, different currency markets, and different measures of return volatility. We also observed a trend toward greater integration between foreign and U.S. financial markets; the U.S. market increasingly emphasized information from nontrading periods to supplement information arriving during trading periods. © 2001 John Wiley & Sons, Inc. Jrl Fut Mark 21:173–196, 2001     

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     

FX Market Returns and Their Relationship to Investor Fear

This note examines the relationship between changes in levels of investor fear (measured by the implied volatility index) and foreign exchange market returns. Our empirical results indicate a negative relationship between daily returns on high‐interest rate (investing) currencies and changes in the implied volatility index, while the association is positive for low‐interest rate (funding) currencies. That is, investing (funding) currencies tends to depreciate (appreciate) when investor fear increases. A sequential breakpoint test identifies a significant change in this relationship in the period following the 2008 collapse of Lehman Brothers, and another in 2012 following the resolution of the European sovereign debt crisis, which suggests that the relationship is linked to financial system liquidity. During the crisis period, currency returns are much more sensitive to changes in investor fear, and this is particularly so for funding currencies that are perceived to present a safe‐haven. The results have important implications for international finance, and those looking to speculate via the carry trade.     

Variance and skew risk premiums for the volatility market: The VIX evidence

We extract variance and skew risk premiums from volatility derivatives in a model-free way and analyze their relationships along with volatility index and equity index returns. These risk premiums can be synthesized through option trading strategies. Using a time series of option prices on the VIX, we find that variance swap excess return can be partially explained by volatility index and equity index excess returns while these latter variables carry little information for the skew swap excess return. The results sharply contrast with those obtained for the equity index option market underlining very specific characteristics of the volatility derivative market.     

Can exchange seat prices predict financial market volatility?

There is considerable evidence that trading volume and volatility are positively related and that exchange seat prices are largely a function of trading volume. This article examines whether changes in seat prices at the Chicago Board of Trade (where stock index and interest rate futures account for the vast majority of trading volume) are useful in predicting changes in interest rate and stock market volatility. Exponential GARCH and transfer function models are used to demonstrate the power of changes in CBOT seat prices in predicting changes in interest rate and stock market volatility. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1206–1221, 2008     

The limits to stock index arbitrage: Examining S&P 500 futures and SPDRS

This study examines factors affecting stock index spot versus futures pricing and arbitrage opportunities by using the S&P 500 cash index and the S&P 500 Standard and Poor's Depository Receipt (SPDR) Exchange‐Traded Fund (ETF) as “underlying cash assets.” Potential limits to arbitrage when using the cash index are the staleness of the underlying cash index, trading costs, liquidity (volume) issues of the underlying assets, the existence of sufficient time to execute profitable arbitrage transactions, short sale restrictions, and the extent to which volatility affects mispricing. Alternatively, using the SPDR ETF as the underlying asset mitigates staleness and trading cost problems as well as the effects of volatility associated with the staleness of the cash index. Minute‐by‐minute prices are compared over different volatility levels to determine how these factors affect the limits of S&P 500 futures arbitrage. Employing the SPDR as the cash asset examines whether a liquid tradable single asset with low trading costs can be used for pricing and arbitrage purposes. The analysis examines how long mispricing lasts, the impact of volatility on mispricing, and whether sufficient volume exists to implement arbitrage. The minute‐by‐minute liquidity of the futures market is examined using a new transaction volume futures database. The results show that mispricings exist regardless of the choice of the underlying cash asset, with more negative mispricings for the SPDR relative to the S&P 500 cash index. Furthermore, mispricings are more frequent in high‐ and mid‐volatility months than in low‐volatility months and are associated with higher volume during high‐volatility months. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:1182–1205, 2008     

Market Timing With Moving Averages

I present evidence that a moving average (MA) trading strategy has a greater average return and skewness as well as a lower variance compared to buying and holding the underlying asset using monthly returns of value‐weighted US decile portfolios sorted by market size, book‐to‐market, and momentum, and seven international markets as well as 18,000 individual US stocks. The MA strategy generates risk‐adjusted returns of 3–7% per year after transaction costs. The performance of the MA strategy is driven largely by the volatility of stock returns and resembles the payoffs of an at‐the‐money protective put on the underlying buy‐and‐hold return. Conditional factor models with macroeconomic variables, especially the default premium, can explain some of the abnormal returns. Standard market timing tests reveal ample evidence regarding the timing ability of the MA strategy.     

Emerging Equity Market Volatility

Abstract

This paper considers two emerging markets that are under-researched, Kenya and Nigeria. It offers a comprehensive view of four time properties that emerged from the empirical time series literature on asset returns: (1) the predictability of returns from past observations; (2) the auto-regressive behavior of conditional volatility; (3) the asymmetric response of conditional volatility to innovations; and (4) the conditional variance risk premium. Results of the exponential GARCH (EGARCH) model indicate that asymmetric volatility found in the U.S. and other developed markets also characterized the Nigerian stock exchange. In Kenya, however, the asymmetric volatility coefficient is significant and positive, suggesting that positive shocks increase volatility more than negative shocks of an equal magnitude. The Nairobi Stock Exchange (KSE) returns series report negative but insignificant risk-premium parameters. In Nigeria (NSE), return series exhibit a significant and positive time-varying risk premium. The results also show that expected returns are predictable, that the auto-regressive return parameters (? ₁ ) are significant in both Kenya and Nigeria. Finally, the GARCH parameter (b) is statistically significant, indicating that volatility persistence is present in the two emerging markets studied.     

Did Investors Herd during the Financial Crisis? Evidence from the US Financial Industry

We examine the herding behavior of investors in the US financial industry, especially commercial banks, S&Ls, investment and insurance firms during global financial crisis of 2008 towards own sub‐sector and market consensus using augmented cross sectional absolute deviation of returns (CSAD) model. After distinguishing between fundamental and non‐fundamental information, we find a greater influence of global financial crisis on spurious herding for commercial and investment banks, and such herding increases in the down market and with conditional volatility of returns, but adverse herding is prevalent among investors during normal period in response to fundamental information. We also find that herding intensity on fundamental information is relatively high with market consensus for all financial institutions except insurance firms in high volatility regime, and intentional herding is only significant and limited to S&Ls and investment banks in high volatility regime. Our findings suggest limited spillover effects of herding when investors face non‐fundamental information.