"Volatility clustering and thick tailed returns are intimately related."
Bollersvlev, Engle and Nelson (1994)
"Volatility and Serial Correlation LeBaron (1992) finds a strong inverse relation between volatility and serial correlation for U.S. stock indices.
This finding appears remarkably robust to the choice of sample period, market index, measurement interval, and volatility measure. Kim (1989) documents a similar relationship in foreign exchange rate data."
Bollersvlev, Engle and Nelson (1994)
Returns and Volatility
"Leverage Effects The so-called "leverage effect," first noted by Black (1976), refers to the tendency for changes in stock prices to be negatively correlated with changes in stock volatility. Fixed costs such as financial and operating leverage provide a partial explanation for this phenomenon. A firm with debt and equity outstanding typically becomes more highly leveraged when the value of the firm falls. This raises equity returns volatility if the returns on the firm as a whole is constant. Black (1976), however, argued that the response of stock volatility to the direction of returns is too large to be explained by leverage alone. This conclusion is also supported by the empirical work of Christie (1982) and Schwert (1989b)."
Bollersvlev, Engle and Nelson (1994)
"Leverage effect: most measures of volatility of an asset are negatively correlated with the returns of that asset."
Cont (2001)
Returns and Volume
"Trading volume plays a key role in rational models describing the information flow, see e.g. Admati and Pfleiderer (1988). Campbell et al. (1993) develop a model in which serial correlation is due to changing risk-aversion. They argue that in days of high trading volume, serial correlation should be larger. Safvenvblad (2000) confirms empirically this prediction on Swedish stocks, but does not for the Swedish stock index. Since he finds that in his sample period high volume days are more often high return days, his explanation is profit taking. Conrad et al. (1994) find that high volume is associated with negative autocorrelation, while low volume is associated with positive autocorrelation. See also Chordia and Swaminathan (2000), who link the trading volume to the speed of adjustment of information, showing that high volume portfolios lead low volume portfolios."
Bianco and Renò (2006)
"(ii) large price movements are followed by high volume;
(iii) conditioning on lagged volume substantially attenuates the “leverage” effect; and
(iv) after conditioning on lagged volume, there is a positive risk-return relation."
Gallant, Rossi and Tauchen (1992)
Abstract: "This paper reviews previous and current research on the relation between price changes and trading volume in financial markets, and makes four contributions. First, two empirical relations are established: volume is positively related to the magnitude of the price change and, in equity markets, to the price change per se. Second, previous theoretical research on the price-volume relation is summarized and critiqued, and major insights are emphasized. Third, a simple model of the price-volume relation is proposed that is consistent with several seemingly unrelated or contradictory observations. And fourth, several directions for future research are identified."
Karpoff (1987)
Wang (1994) found that volume is positively correlated with absolute changes in prices and dividends.
Volatility and Volume
"However, it is important to stress that it is very difficult to disentangle the volume effect from the volatility effect, since these two quantities are strongly positively correlated."
Bianco and Renò (2006)
"Volume/volatility correlation: trading volume is correlated with all measures of volatility."
Cont (2001)
"(i) positive correlation between conditional volatility and volume;
(iii) conditioning on lagged volume substantially attenuates the “leverage” effect; and
(iv) after conditioning on lagged volume, there is a positive risk-return relation."
Gallant, Rossi and Tauchen (1992)
Serial Correlation and Volume
Abstract: "This paper investigates the relationship between aggregate stock market trading volume and the serial correlation of daily stock returns. For both stock indexes and individual large stocks, the first-order daily return autocorrelation tends to decline with volume. The paper explains this phenomenon using a model in which risk-averse 'market makers'accommodate buying or selling pressure from 'liquidity'or 'noninformational' traders. Changing expected stock returns reward market makers for playing this role. The model implies that a stock price decline on a high-volume day is more likely than a stock price decline on a low-volume day to be associated with an increase in the expected stock return."
Campbell, Grossman and Wang (1992)
LEE, B.S. and O.M. RUI, 2002. The dynamic relationship between stock returns and trading volume: Domestic and cross-country …. Journal of Banking and Finance. [Cited by 25] (6.42/year)
Abstract: "This paper examines the dynamic relations – causal relations and the sign and magnitude of dynamic effects – between stock market trading volume and returns (and volatility) for both domestic and cross-country markets by using the daily data of the three largest stock markets: New York, Tokyo, and London. Major findings are as follows: First, trading volume does not Granger-cause stock market returns on each of three stock markets. Second, there exists a positive feedback relationship between trading volume and return volatility in all three markets. Third, regarding the cross-country relationships, US financial market variables, in particular US trading volume, contains an extensive predictive power for UK and Japanese financial market variables. Fourth, sub-sample analyses show evidence of stronger spillover effects after the 1987 market crash and an increased importance of trading volume as an information variable after the introduction of options in the US and Japan."
Lee and Rui (2002)
DOWNING, C. and F. ZHANG, 2004. Trading Activity and Price Volatility in the Municipal Bond Market, The Journal of Finance. [Cited by 9] (4.74/year)
Abstract: "Utilizing a comprehensive database of transactions in municipal bonds, we investigate the volume-volatility relation in the municipal bond market. We find a positive relation between the number of transactions and a bond's price volatility. In contrast to previous studies, we find a "negative" relation between average deal size and price volatility. These results are found to be robust throughout the sample. Our results are inconsistent with current theoretical models of the volume-volatility relation. These inconsistencies may arise because current models fail to account for the effects of overall market liquidity on the costs of large transactions."