Author: Torben G. Andersen Publisher: ISBN: Category : Languages : en Pages : 41
Book Description
We exploit direct model-free measures of daily equity return volatility and correlation obtained from high-frequency intraday transaction prices on individual stocks in the Dow Jones Industrial Average over a five-year period to confirm, solidify and extend existing characterizations of stock return volatility and correlation. We find that the unconditional distributions of the variances and covariances for all thirty stocks are leptokurtic and highly skewed to the right, while the logarithmic standard deviations and correlations all appear approximately Gaussian. Moreover, the distributions of the returns scaled by the realized standard deviations are also Gaussian. Consistent with our documentation of remarkably precise scaling laws under temporal aggregation, the realized logarithmic standard deviations and correlations all show strong temporal dependence and appear to be well described by long-memory processes. Positive returns have less impact on future variances and correlations than negative returns of the same absolute magnitude, although the economic importance of this asymmetry is minor. Finally, there is strong evidence that equity volatilities and correlations move together, possibly reducing the benefits to portfolio diversification when the market is most volatile. Our findings are broadly consistent with a latent volatility fact or structure, and they set the stage for improved high-dimensional volatility modeling and out-of-sample forecasting, which in turn hold promise for the development of better decision making in practical situations of risk management, portfolio allocation, and asset pricing.
Author: Rong Chen Publisher: ISBN: Category : Languages : en Pages :
Book Description
We examine the distribution of intradaily volatility of common stock returns of a portfolio (updated annually) of the 250 most actively traded stocks on the NYSE for the sample period 1983-92. Our results suggest that there was a shift in the distribution of return volatility around 1985-86: both the level and dispersion of volatility increased significantly after 1985. We find that the well known 'U'-shaped pattern of both intradaily volatility and volume shifted almost uniformly upwards following 1985; moreover, the U-shape is present not merely in the level of volatility and volume, but in the dispersion also. We examine intradaily volatility and volume on triple witching days, and find that volume is significantly higher at the open but not the close, while the opposite is true for volatility. Finally, we model the joint relationship of volatility and volume and find it be complex and non-linear.
Author: Frank K. Reilly Publisher: ISBN: Category : Stocks Languages : en Pages : 92
Book Description
General price studies on the level of volatility for aggregate stock market have derived conflicting results. Using daily stock price changes for the period 1926-1975, the paper examines the characteristics of the distribution of daily stock price changes. Subsequently we examined changes in several measures of stock price volatility. The results indicated significant changes over time and especially in 1973-1975.
Author: Steven L. Jones Publisher: ISBN: Category : Languages : en Pages :
Book Description
We find that for a sample of call options on stocks with low returns in the prior year, the implied volatilities increase as the year-end approaches. On the other hand, we do not detect an increase in the volatilities implied from the put options on the same stocks over the same dates. This is inconsistent with a hypothesis that the turn-of-the-year seasonal in stock returns is due to a seasonal increase in systematic risk. Instead, the results are consistent with price pressure from portfolio rebalancing at the turn-of- the-year. The implications are that the option market anticipates the return seasonal, but it survives in the stock market due to transaction costs.
Author: Robert Krimetz Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
The application of machine learning and probabilistic programming methods on stock return prediction has grown in tandem with the availability of high frequency stock data. With well recorded heteroskedasticity in historical stock returns, modeling attempts have evolved from making general assumptions about the underlying data generating distribution to predicting changes in the underlying distribution of returns. The increase in popularity of 'tradable volatility' through derivative contacts and VIX futures over the past three decades has motivated research efforts to model the variance of daily returns. Along this line of research, three schools of thought have emerged to model return volatility; Time Series Models, Stochastic Models, and Bayesian Models. Given that the preliminary assumptions underlying these models differ, the nature of their results and the varying metrics used to calculate their respective accuracy makes it difficult to directly compare them. Accordingly, the currently available pool of research has diverged along these three separate paths making it unclear the advantages of each. Notably, Bayesian models have largely been neglected in the current pool of research due to their computational intensity. In this paper I derive ten time series and Bayesian models then provide a comprehensive comparative study of the results on real stock data. I found that Bayesian models with intractable posterior distributions significantly outperform time series models at predicting directional change in future volatility, while the GARCH and FIGARCH time series models generate the most accurate point predictions for future volatility. I hope the results outlined in this paper better contextualize different volatility predictions and motivate the creation of more accurate tradeable volatility models.
Author: Torben G. Andersen
Author: Missaka Warusawitharana
Author: Rong Chen
Author: Frank K. Reilly
Author: Steven L. Jones
Author: 
Author: Robert Krimetz
Author: Prasad V. Bidarkota
Author: Richard Michael Osborne
Author: Bruno H. Solnik