Market capitalisation,cross-correlations,the lead/lag structure and microstructure effects in the Indian stock market

The lead/lag relationship between portfolios comprising large and small cap firms is analytically derived in terms of their speeds of adjustment and degrees of thin trading. Using a number of Indian equity index series that differ in their market capitalization characteristics, large cap indices are found to lead small cap indices and to have higher speeds of adjustment towards intrinsic values. However, pure thin trading effects and an interaction effect between thin trading and speeds of adjustment are found to make significant contributions to the lead/lag effect. An empirical analysis of the underlying intrinsic value process using a reduced form model developed in the paper indicates that a small degree of overreaction is present in intrinsic values series.     

The Determinants of Conditional Autocorrelation in Stock Returns

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day‐of‐the‐week are potential determinants of conditional autocorrelation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M‐GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time‐varying patterns of return autocorrelation.     

Asymmetric information,volatility components and the volume–volatility relationship for the CAC40 stocks

This paper investigates the relationship between trading volume components and various realized volatility measures for the CAC40 index constituents. A mixture-of-distribution model is used to decompose trading volume into informed and liquidity components. Realized volatility is broken down into continuous volatility and jumps. Our findings confirm the strong positive contemporaneous relationship between total trading volume and volatility when realized volatility and its continuous component are considered. A limited evidence of the effect of total trading volume on discontinuous volatility is found. The positive volume–volatility relationship is mainly driven by the informed component of trading volume. Conversely, liquidity volume is negatively related to realized volatility lending some support to the view that liquidity trading dampens the volatility of stock returns. A stronger negative relationship between liquidity volume and volatility jump is uncovered.     

Price formation on stock exchanges: the evolution of trading within the day

Prior analyses of prices of the NYSE and other exchanges findthat transitory price volatility is greater at the open of tradingthan at the close. We extend this line of research by using40 years of hourly Dow Jones 65 composite price index data toestimate transitory volatility throughout the trading day. Ourresults indicate that transitory volatility steadily declinesduring the trading day. We find a similar intraday decline intransitory volatility for a 2-year sample of the individualfirms in the Dow Jones 30 Industrials Index. The results areconsistent with the hypothesis that trading aids price formationand do not support the argument that particular trading mechanismsare the source of greater volatility at the open of trading.     

Forecasting Intraday Volatility and Value-at-Risk with High-Frequency Data

In this paper, we develop modeling tools to forecast Value-at-Risk and volatility with investment horizons of less than one day. We quantify the market risk based on the study at a 30-min time horizon using modified GARCH models. The evaluation of intraday market risk can be useful to market participants (day traders and market makers) involved in frequent trading. As expected, the volatility features a significant intraday seasonality, which motivates us to include the intraday seasonal indexes in the GARCH models. We also incorporate realized variance (RV) and time-varying degrees of freedom in the GARCH models to capture more intraday information on the volatile market. The intrinsic tail risk index is introduced to assist with understanding the inherent risk level in each trading time interval. The proposed models are evaluated based on their forecasting performance of one-period-ahead volatility and Intraday Value-at-Risk (IVaR) with application to the 30 constituent stocks. We find that models with seasonal indexes generally outperform those without; RV can improve the out-of-sample forecasts of IVaR; student GARCH models with time-varying degrees of freedom perform best at 0.5 and 1 % IVaR, while normal GARCH models excel for 2.5 and 5 % IVaR. The results show that RV and seasonal indexes are useful to forecasting intraday volatility and Intraday VaR.     

The price discovery of day trading activities in futures market

Access to information is necessary for market transparency. However, contrary to trading volume and open interest, information related to day trading activities is rarely available. By incorporating unexplored day trading volume in the literature, this paper demonstrates that both the expected open interest and expected day trading volume are consistently and positively correlated with returns, but that one-lagged day trading volume is negatively correlated with futures returns. Meanwhile, both expected and unexpected day trading volume are negatively correlated with volatility, suggesting that arbitrage activities related to unexpected day trading volume may accelerate the movement of futures prices to a new equilibrium. Moreover, open interest provides liquidity but increases volatility. Finally, we strongly suggest that day trading transaction information be released by futures exchanges to achieve greater transparency.     

When Uncertainty Blows in the Orchard: Comovement and Equilibrium Volatility Risk Premia

We provide novel evidence for an equilibrium link between investors' disagreement, the market price of volatility and correlation, and the differential pricing of index and individual equity options. We show that belief disagreement is positively related to (i) the wedge between index and individual volatility risk premia, (ii) the different slope of the smile of index and individual options, and (iii) the correlation risk premium. Priced disagreement risk also explains returns of option volatility and correlation trading strategies in a way that is robust to the inclusion of other risk factors and different market conditions.     

An empirical investigation of the factors that determine the pricing of Dutch index warrants

This paper investigates the pricing of Dutch index warrants. It is found that when using the historical standard deviation as an estimate for the volatility, the Black and Scholes model underprices all put warrants and call warrants on the FT-SE 100 and the CAC 40, while it overprices the call warrants on the DAX. When the implied volatility of the previous day is used the model prices the index warrants fairly well. When the historical standard deviation is used the mispricing of the call and the put warrants depends in a strong way on the mispricing of the previous trading day, and on the moneyness (in a non-linear way), the volatility, and the dividend yield. When the implied standard deviation of the previous trading day is used the mispricing of the call warrants is only related to the moneyness and to the estimated volatility, while the mispricing of put index warrants depends in a strong way on the moneyness, the volatility, the dividend yield and the remaining time to maturity.     

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.     

From pit to electronic trading: Impact on price volatility of U.S. Treasury futures

This paper investigates the dynamics of price volatility and trading volume of 10-year U.S. Treasury note futures within the context of transition from pit to electronic trading. The analysis is conducted over four discernible phases of futures trading evolution: the pit-only phase, the leap to electronic trading, and the electronic trading dominant phase, which is divided further into two periods, the before and after the financial crisis of 2007/2009. Generalized autoregressive conditional heteroskedasticity with in-mean conditional variance and generalized error distribution parameterization (GARCH-M-GED) tests are conducted to examine the conditional volatility of total returns index as a function of trading volume. The empirical results show a consistently negative relationship between the trading volume and price volatility for all four analyzed phases. They also show decreasing leptokurtosis (except for the direct effects of the recent crisis), continuously high persistency in volatility, as well as a weakening impact of unexpected ARCH-type shocks during the most recent analyzed period. Overall, the shift to electronic trading entails a substantial increase in trading volume, but not in price volatility of Treasury futures.     

THE EMPIRICAL RELATIONSHIP BETWEEN TRADING VOLUME,RETURNS AND VOLATILITY

This paper presents an empirical analysis of the relationship between trading volume, returns and volatility in the Australian stock market. The initial analysis centres upon the volume-price change relationship. The relationship between trading volume and returns, irrespective of the direction of the price change, is significant across three alternative measures of daily trading volume for the aggregate market. This finding also provides basic support for a positive relationship between trading volume and volatility. Furthermore, evidence is found supporting the hypothesis that the volume-price change slope for negative returns is smaller than the slope for non-negative returns, thereby supporting an asymmetric relationship which is hypothesised to exist because of differential costs of taking long and short positions. Analysis at the individual stock level shows weaker support for the relationship. A second related hypothesis is tested in which the formation of returns is conditional upon information arrival which similarly affects trading volume. The hypothesis is tested by using the US overnight return to proxy for expected “news” and trading volume to proxy for news arrival during the day. The results show a reduction in the significance and magnitude of persistence in volatility and hence are consistent with explaining non-normality in returns (and ARCH effects) through the rate of arrival of information. The findings in this paper help explain how returns are generated and have implications for inferring return behaviour from trading volume data.     

THE SPEED OF ADJUSTMENT OF PRICES TO PRIVATE INFORMATION: EMPIRICAL TESTS

We estimate speeds of adjustment of individual stock prices to private information using daily data. We use a model in which private information gives rise to return variance and private information decays linearly over time. We find that, on average, about 85 percent to 88 percent of private information is incorporated into prices within one trading day, with variation depending upon the stock's trading volume and whether the stock is listed on an exchange. The findings support strong form market efficiency.     

The Informational Role of Option Trading Volume in Equity Index Options Markets

This paper examines the dynamic relations between future price volatility of the S&P 500 index and trading volume of S&P 500 options to explore the informational role of option volume in predicting the price volatility. The future volatility of the index is approximated alternatively by implied volatility and by EGARCH volatility. Using a simultaneous equation model to capture the volume-volatility relations, the paper finds that strong contemporaneous feedbacks exist between the future price volatility and the trading volume of call and put options. Previous option volumes have a strong predictive ability with respect to the future price volatility. Similarly, lagged changes in volatility have a significant predictive power for option volume. Although the volume-volatility relations for individual volatility and volume terms are somewhat different under the two volatility measures, the results on the predictive ability of volume (volatility) for volatility (volume) are broadly similar between the implied and EGARCH volatilities. These findings support the hypothesis that both the information- and hedge-related trading explain most of the trading volume of equity index options.     

Does futures trading increase stock market volatility? The case of the Nikkei stock index futures markets

We propose new tests to examine whether stock index futures affect stock market volatility. These tests decompose spot portfolio volatility into the cross-sectional dispersion and the average volatility of returns on the portfolio's constituent securities. Our tests show that for Nikkei stocks spot portfolio volatility increased and cross-sectional dispersion decreased compared with average volatility when Nikkei futures began trading on the Osaka Securities Exchange, but not on the Singapore International Monetary Exchange. For non-Nikkei stocks, no shift occurred when futures trading began on either exchange. These findings are consistent with the hypotheses that futures trading increases spot portfolio volatility but that there is no volatility “spillover” to stocks against which futures are not traded. However, the increase in volatility attributable to futures trading is small compared with volatility shifts induced by changes in broad economic factors.     

Stock repurchases and treasury share sales: Do they stabilize price and enhance liquidity?

Can companies reduce the volatility and increase the liquidity of their stocks by trading them? In the context of the Italian stock market, where companies have far more leeway to sell as well as buy their own stocks than in the U.S., the answer is yes. We examine the effects of trading (open-market share repurchases and treasury shares sales) on liquidity (bid-ask spread) and volatility (return variance). Further, we examine the impact of shareholder approvals of repurchase programs on liquidity and volatility. We find clear evidence that trading increases liquidity and reduces volatility. These results are consistent with our analysis of the motives Italian companies give for making share repurchases.     

融资融券能否提高交易所自律监管效率?

本文利用沪深300指数日收益率为样本,采用GARCH-M模型、EGARCH-M模型分析融资融券业务推出对我国股市波动性的影响,并以波动性为交易所自律监管效率的代理变量来进一步说明融资融券能否提高我国交易所自律监管的效率。结果表明:融资融券业务从试点推出到转为常规业务的一年半以来,我国股市的波动性有所减小,波动的杠杆效应减弱,表明融资融券能起到稳定市场的作用。同时也说明了金融创新工具推出和应用的市场化改革能够促进我国交易所自律监管功能的发挥。     

Dispersion trading: Empirical evidence from U.S. options markets

This paper develops empirical evidence on the viability of a form of volatility trading known as “dispersion trading.” The results shed light on the efficiency with which U.S. options markets price volatility.Using end-of-day implied volatilities extracted from equity option prices for the stocks that comprise the S&P 500, the implied volatility of the S&P 500 is computed using a modification of the Markowitz variance equation. This Markowitz-implied volatility is then compared to the implied volatility of the S&P 500 extracted directly from index options on the S&P 500. These contemporaneous measures of implied volatility are then examined for exploitable discrepancies both with and without transaction costs. The study covers the period October 31, 2005 through November 1, 2007.It is shown that, from a trader's perspective, index option implied volatility tended to be more often “rich” and component volatilities tended to be more often “cheap.” Nevertheless, there were times when the opposite was true; suggesting that potential dispersion trades can run in either direction.     

Circuit Breakers,Trading Collars,and Volatility Transmission Across Markets: Evidence from NYSE Rule 80A

The NYSE's Rule 80A attempted to delink the futures and equity markets by limiting index arbitrage trades in the same direction as the last trade to reduce stock market volatility. Rule 80A leads to a small but statistically significant decline in intraday U.S. equity market volatility. In addition, the results are asymmetric: volatility is dampened more in a rising market than in a declining one. These results suggest that, to a limited extent, rule restrictions on trading can sufficiently delink the futures and equity markets enough to reduce the transmission of volatility.     

Volatility and trading activity following changes in the size of futures contracts

This paper has two purposes. First, we examine the relationship between daily price volatility and trading activity one year before and after a change in contract size by examining the results of contract splits in the Australian share price index futures and the U.K. FTSE-100 futures contracts and a reverse contract split in the Australian Bank Bill Acceptance futures contract. Second, we evaluate the effect of the change in contract size on the use of the particular futures market. We find that after a contract size change, the change in total trading frequency has the power to explain the change in daily price volatility. Specifically, after a contract split, trading frequency increased, resulting in increased daily price volatility, and vice versa after a reverse contract split. Most of the average trade size variable has an immaterial impact on price volatility. However, decomposing the total trading frequency into four trade size classes, we find that the trading frequency for small and large trade size categories are highly significant in explaining changes in daily price volatility after the contract splits. Finally, we find the change in contract size for each futures market was successful because within three years following the change, the adjusted trading volume and open interest surpassed the levels prior to the change and have continued to increase thereafter.     

Volume,Volatility, and New York Stock Exchange Trading Halts

Trading halts increase, rather than reduce, both volume and volatility. Volume (volatility) in the first full trading day after a trading halt is 230 percent (50 to 115 percent) higher than following “pseudohalts”: nonhalt control periods matched on time of day, duration, and absolute net-of-market returns. These results are robust over different halt types and news categories. Higher posthalt volume is observed into the third day while higher posthalt volatility decays within hours. The extent of media coverage is a partial determinant of volume and volatility following both halts and pseudohalts, but a separate halt effect remains after controlling for the media effect.