市场波动性与中小上市公司成长性评价

本文在已有的较为成熟的指标体系中尝试加入市场波动性风险因素，对中小企业板上市公司的成长性进行评价。对考虑市场波动性因素与未考虑市场波动性因素两个排名作有效性检验及比较分析发现，加入市场波动性风险因素使得排名结果更为有效；同时对成长性排名得分与市场波动性风险作回归分析，发现其成长性排名得分与市场波动性风险呈显著负相关关系。     

Market distraction and near-zero daily volatility persistence

This study shows that during the FIFA World Cups, the Olympic Games, and Christmas and New Year, the average daily volatility persistence is near zero across 17 equity indices in 14 developed economies. The evidence indicates low information production by distracted financial analysts and journalists. Volatility persistence has seasonal variation that is high in January and October and low in June, consistent with seasonality in market attention. When attention seasonality is disrupted by unprecedented events in 2020–21, seasonality in volatility persistence is reversed. The seasonal variations in volatility persistence explain an average 8.7% of daily variations in volatility level across global markets.     

市场波动促使Libor走向完善

Libor一直是全球最为重要的利率基准之一。针对年初以来市场对于Libor可能偏离实际交易利率的担忧,英国银行家协会（BBA）迅速展开了调查和讨论,并公布了调查结果。从目前看,Libor在精确、透明和客观性上依然具有主导性和市场地位,市场尽管也提出了一些替代方案,但从实际看,继续完善Libor形成机制并加强监管也许是市场更好的选择。     

Market heterogeneities and the causal structure of volatility

Abstract

The correlation between historical and realized volatilities is studied empirically for a large range of time intervals. Similarly, the correlation between the volatility changes and the realized volatilities is studied. Both quantities measure the response functions of the market participants. These correlations show explicitly the heterogeneous structure of the market according to the characteristic time horizons of the different agents. It reveals a volatility cascade from long to short time horizons, with a structure different from the one observed in turbulence. A comparison is made with several theoretical processes used in finance, allowing a better understanding of the role and interactions of the market participants (intra-day trader, portfolio manager, central banks, pension funds, …). Moreover, we have developed a new ARCH-type process that incorporates the different groups of agents, with their characteristic memories. This process reproduces well the empirical response function, and allows us to quantify the importance of each group.     

Market imperfections and the information content of implied and realized volatility

The information content of option implied volatility and realized volatility under market imperfections are studied in the context of GARCH modeling and volatility forecasts of Taiwan stock market (TAIEX) returns. Consistent with most studies, we find that the Taiwan implied volatility index (TVIX) calculated from the TAIEX option prices contains most of the information, and that White's [White, H., 2000. A reality check for data snooping. Econometrica 68, 1097–1126] reality check test cannot reject the null hypothesis that the TVIX provides the best forecast. Possibly due to market imperfections, however, the incremental information content of realized volatility as well as daily returns cannot be ruled out. Finally, we also find that the information is found only in the most recent TVIX, indicating information is being efficiently impounded on the TAIEX option prices. This finding suggests that appropriately designed derivative products can alleviate the problems caused by market imperfections.     

Market co-movement between credit default swap curves and option volatility surfaces

We analyze the co-movement between the Credit Default Index (CDX) curve and the S&P 500 index's option volatility surface. We connect the reduced-form no-arbitrage model with the Nelson-Siegel (N-S) model on hazard rate implied from the CDX curve, and identify the levels, slopes, and curvatures from these two markets via the Unscented Kalman Filter (UKF). We find that the changes in the level, slope, and curvature in the CDX curve and those in the volatility surface are correlated due to the bridge of the S&P 500 index return. Finally, the co-movement between the CDX curve and S&P 500 index's volatility surface become stronger after the late 2000s global financial crisis.     

Risk-managed industry momentum and momentum crashes

This paper investigates Barroso and Santa-Clara’s [J. Financ. Econ., 2008, 116, 111–120] risk-managed momentum strategy in an industry momentum setting. We investigate several traditional momentum strategies including that recently proposed by Novy-Marx [J. Financ. Econ., 2012, 103, 429–453]. We moreover examine the impact of different variance forecast horizons on average pay-offs and also Daniel and Moskowitz’s [J. Financ. Econ., 2016, 122, 221–247] optionality effects. Our results show in general that neither plain industry momentum strategies nor the risk-managed industry momentum strategies are subject to optionality effects, implying that these strategies have no time-varying beta. Moreover, the benefits of risk management are robust across volatility estimators, momentum strategies and subsamples. Finally, the ‘echo effect’ in industries is not robust in subsamples as the strategy works only during the most recent subsample.     

An empirical investigation of trading volume and return volatility of the Taiwan Stock Market

This study examines the Mixed Distribution Hypothesis (MDH) using 5-min interval stock returns of the Taiwan Stock Index (TSI). Startlingly enough, the persistence of stock volatility remains dominant after the stochastic mixing variable was included in the variance equation. It implies that the MDH cannot explain away the ARCH phenomenon. We have found that the composition of participants (approximately 92% of participants are individual investors) in TSI is a major contributing factor to the persistent volatility. In addition, the existence of limits on price changes, to some extent, accounts for the persistence phenomenon. Similar results are also found for individual stocks in the sample. Interestingly enough, the explanatory power of trading volume exhibits a U-shaped pattern in explaining return volatility in Taiwan Stock Market.     

Determinants of periodic volatility of intraday exchange rates in the Taipei FX Market

Using the periodic GARCH (P-GARCH) model, this paper investigates the cause of the volatility seasonality of intraday Taiwan dollar/U.S. dollar (NTD/USD) exchange rate. We study the intraday volatility of NTD/USD exchange rate by considering impacts from public news arrivals, inventory risk and central bank interventions. The estimation results indicate that news arrivals at the market open may induce traders to adjust their inventory position and result in higher NTD/USD volatility on days with reported central bank interventions.     

Market uncertainty,expected volatility and the mispricing of S&P 500 index futures

An association between increased index futures mispricing and concurrent index volatility has been reported within several prior studies; in the present study, we argue that expected volatility over an arbitrage horizon also has an adverse effect on the ability and willingness of traders to engage in arbitrage, leading to greater and more persistent futures mispricing. Using the CBOE VIX and its innovation on the concurrent spot volatility as proxies for expected volatility, we present evidence of an increase in S&P 500 index futures mispricing with expected volatility. The impact of the VIX grows exponentially across the distribution of conditional mispricing levels, which suggests that the expectations of heightened future volatility become increasingly detrimental to arbitrage activities when the futures price deviations are enlarged; however, the influence of expected volatility is found to have been reduced during the global financial crisis period, a period during which concurrent volatility overwhelmingly dominated the magnitude of mispricing.     

Macroeconomic determinants of stock volatility and volatility premiums

How does stock market volatility relate to the business cycle? We develop, and estimate, a no-arbitrage model, and find that (i) the level and fluctuations of stock volatility are largely explained by business cycle factors and (ii) some unobserved factor contributes to nearly 20% to the overall variation in volatility, although not to its ups and downs. Instead, this “volatility of volatility” relates to the business cycle. Finally, volatility risk-premiums are strongly countercyclical, even more than stock volatility, and partially explain the large swings of the VIX index during the 2007–2009 subprime crisis, which our model captures in out-of-sample experiments.     

Growth and volatility

Growth and volatility correlate negatively across countries, but positively across sectors. Analytically, whether or not sectoral growth and volatility are correlated positively is irrelevant in the aggregate. Cross-country estimates identify the detrimental effects of macroeconomic volatility on growth, but they cannot be used to dismiss theories implying a positive growth-volatility coefficient, which appear to hold in sectoral data. In particular, volatile sectors command high investment rates, as they would in a mean-variance framework.     

Realized volatility and transactions

This paper re-examines the impact of number of trades, trade size and order imbalance on daily stock returns volatility. In contrast to prior studies, we estimate daily volatility using realized volatility obtained by summing up intraday squared returns. Consistent with the theory of quadratic variation, realized volatility estimates are shown to be less noisy than standard volatility measures such as absolute returns used in previous studies. In general, our results confirm [Jones, C.M., Kaul, G., Lipson, M.L., 1994. Transactions, volume, and volatility. Review of Financial Studies 7, 631–651] that number of trades is the dominant factor behind the volume–volatility relation. Neither trade size nor order imbalance adds significantly more explanatory power to realized volatility beyond number of trades. This finding is robust to different time periods, firm sizes and regression specifications. The implications of our results for microstructure theory are discussed.     

Asset volatility

We examine whether fundamental measures of volatility are incremental to market-based measures of volatility in (i) predicting bankruptcies (out of sample), (ii) explaining cross-sectional variation in credit spreads, and (iii) explaining future credit excess returns. Our fundamental measures of volatility include (i) historical volatility in profitability, margins, turnover, operating income growth, and sales growth; (ii) dispersion in analyst forecasts of future earnings; and (iii) quantile regression forecasts of the interquartile range of the distribution of profitability. We find robust evidence that these fundamental measures of volatility improve out-of-sample forecasts of bankruptcy and help explain cross-sectional variation in credit spreads. This suggests that an analysis of credit risk can be enhanced with a detailed analysis of fundamental information. As a test case of the benefit of volatility forecasting, we document an improved ability to forecast future credit excess returns, particularly when using fundamental measures of volatility.     

Herding and market volatility

In this paper, we explore the impact of investor herding behavior on stock market volatility. We adopt a direct herding measure based on the variation of cross-sectional stock betas. The measure can be readily separated into positive and adverse components, whereby investors herd towards and away from the market portfolio, respectively. Using A-shares listed in the Chinese equity market from August 2005 to March 2021, we show that the market volatility is Granger caused by the measure, and that there exists an asymmetric effect between positive and adverse herding on volatility. Furthermore, we provide robust evidence that the information contained in the herding measure helps generate significantly improved volatility forecasts and add economic value to investors. Our paper not only contributes to the volatility forecasting literature but also advances our understanding of herding in the equity market.     

Conditional volatility persistence and volatility spillovers in the foreign exchange market

I investigate the magnitudes and determinants of volatility spillovers in the foreign exchange (FX) market, using realized measures of volatility and heterogeneous autoregressive (HAR) models. I confirm both meteor shower effects (i.e., inter-regional volatility spillovers) and heat wave effects (i.e., intra-regional volatility spillovers) in the FX market. Furthermore, I find that conditional volatility persistence is the dominant channel linking the changing market states of each region to future volatility and its spillovers. Market state variables contribute to more than half of the explanatory power in predicting conditional volatility persistence, with the model that calibrates volatility persistence and spillovers conditionally on market states performing statistically and economically better. The utilization of market state variables significantly extends our understanding of the economic mechanisms of volatility persistence and spillovers and sheds new light on econometric techniques for volatility modeling and forecasting.     

Margin regulation and volatility

An infinite-horizon asset-pricing model with heterogeneous agents and collateral constraints can explain why adjustments in stock market margins under US Regulation T had an economically insignificant impact on market volatility. In the model, raising the margin requirement for one asset class may barely affect its volatility if investors have access to another, unregulated class of collateralizable assets. Through spillovers, however, the volatility of the other asset class may substantially decrease. A very strong dampening effect on all assets׳ return volatilities can be achieved by a countercyclical regulation of all markets.     

Intradaily volatility and adjustment

Intradaily volatility is related to the speed of adjustment of prices towards their intrinsic values. The decomposition of volatility into intrinsic and noise related components is demonstrated to be impacted by speeds of adjustment. Intradaily speeds of adjustment are estimated for U.K. index data and some empirical evidence of overreaction at the open and underreaction at the close of the trading day found for the FTSE100 index. The major result that we report in this paper is that differential intradaily volatilities at the index level are related to differential speeds of adjustment, thus providing insights into the similar results reported in U.S. index studies, such as [Gerety, M., Mulherin, J., 1994. Price formation on stock exchanges: the evolution of trading within the day. The Review of Financial Studies 7, 609–629].     

Local volatility of volatility for the VIX market

Following a trend of sustained and accelerated growth, the VIX futures and options market has become a closely followed, active and liquid market. The standard stochastic volatility models—which focus on the modeling of instantaneous variance—are unable to fit the entire term structure of VIX futures as well as the entire VIX options surface. In contrast, we propose to model directly the VIX index, in a mean-reverting local volatility-of-volatility model, which will provide a global fit to the VIX market. We then show how to construct the local volatility-of-volatility surface by adapting the ideas in Carr (Local variance gamma. Bloomberg Quant Research, New York, 2008) and Andreasen and Huge (Risk Mag 76–79, 2011) to a mean-reverting process.     

Market closure effects on return, volatility, and turnover patterns in the Hong Kong index futures market

This paper examines the market closure effect of the Stock Exchange of Hong Kong (SEHK) on the intraday behaviour of the index futures contract which continues to trade for 5–15 min after the close of the SEHK. The behaviour of the index futures market in Hong Kong is consistent with the contagion model of King and Wadhwani (1990) in that the close of the SEHK leads to an immediate downturn in the return, volatility, and turnover in the index futures market. The long period of nontrading before the morning also leads to a higher morning volatility and turnover.