Trading Volume, Volatility and Leverage
Trading Volume, Volatility and Leverage

Author: Ayesha Rawoo

Publisher: LAP Lambert Academic Publishing

Published: 2011-08

Total Pages: 64

ISBN-13: 9783845438023

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Knowledge of volatility is of crucial importance in many areas. The Stock Market of Mauritius did not remain untouched. This paper explores the relationship between trading volume, volatility and leverage in the Stock Market of Mauritius. In contrast to other studies which examine the SEMDEX or the SEM by sector, we examine the relationship for 35 listed stocks on the SEM. Daily return, volume and the SEMDEX is used for the period 2005 to 2009. We also emphasize on the impact of trading volume. The analysis shows that there exist substantial ARCH effect and volatility shocks are quite persistent in the market. The impact of both recent and old news can be found. The study also finds evidence of leverage and asymmetric effect on the Stock Market. Consistent with the results of Lamoureux and Lastrapes (1990), our results shows that the persistence of volatility decreases after volume is included in the model.

Volume, Volatility, and Leverage
Volume, Volatility, and Leverage

Author: George Tauchen

Publisher:

Published: 2009

Total Pages: 46

ISBN-13:

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This paper uses dynamic impulse response analysis to investigate the interrelationships among stock price volatility, trading volume, and the leverage effect. Dynamic impulse response analysis is a technique for analyzing the multistep ahead characteristics of a non-parametric estimate of the one-step conditional density of a strictly stationary process. The technique is the generalization to a nonlinear process of Sims-style impulse response analysis for linear models. In this paper, we refine the technique and apply it to a long panel of daily observations on the price and trading volume of four stocks actively traded on the NYSE: Boeing, Coca-Cola, IBM, and MMM.

Stochastic Volatility and Time Deformation
Stochastic Volatility and Time Deformation

Author: Joann Jasiak

Publisher:

Published: 2012

Total Pages:

ISBN-13:

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In this paper, we study stochastic volatility models with time deformation. Such processes relate to the early work by Mandelbrot and Taylor (1967), Clark (1973), Tauchen and Pitts (1983), among others. In our setup, the latent process of stochastic volatility evolves in an operational time which differs from calendar time. The time deformation can be determined by past volume of trade, past returns, possibly with an asymmetric leverage effect, and other variables setting the pace of information arrival. The econometric specification exploits the state-space approach for stochastic volatility models proposed by Harvey, Ruiz and Shephard (1994) as well as the matching moment estimation procedure using SNP densities of stock returns and trading volume estimated by Gallant, Rossi and Tauchen (1992). Daily data on returns and trading volume of the NYSE are used in the empirical application. Supporting evidence for a time deformation representation is found and its impact on the behavior of returns and volume is analyzed. We find that increases in volume accelerate operational time, resulting in volatility being less persistent and subject to shocks with a higher innovation variance. Downward price movements have similar effects while upward price movements increase the persistence in volatility and decrease the dispersion of shocks by slowing down market time. We present the basic model as well as several extensions; in particular, we formulate and estimate a bivariate return-volume stochastic volatility model with time deformation. The latter is examined through bivariate impulse response profiles following the example of Gallant, Rossi and Tauchen (1993).

Leveraged Exchange-Traded Funds
Leveraged Exchange-Traded Funds

Author: Peter Miu

Publisher: Springer

Published: 2016-04-29

Total Pages: 174

ISBN-13: 1137478217

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Leveraged Exchange-Traded Funds (LETFs) are publicly-traded funds that promise to provide daily returns that are in a multiple (positive or negative) of the returns on an index. To meet that promise, the funds use leverage, which is typically obtained through derivatives such as futures contracts, forward contracts, and total-return swaps. As of the end of 2012, there were over 250 LETFs in North America with total assets of approximately $32.24 billion. While the amount of assets held by these funds is still small, their popularity continues to grow as their trading volume is significantly larger and much more dynamic than traditional, non-leveraged ETFs. This comprehensive guide to LETFs provides high-level practitioners and researchers with a detailed reference tool for navigating the market and making informed investment decisions. Written from a measured analytical perspective, Miu and Charupat use clear and concise explanations of all important aspects of LETFs, focusing on such key elements as structure, pricing, performance, regulations, taxation, and trading strategies. The first two chapters set the stage for the book by identifying exactly what LETFs are and how they are regulated. The following chapters then look to bridge theory with practice to dive deep into the mechanics, portfolio rebalancing techniques, and daily compounding effects that make investing in these funds so lucrative.

Trading Volume, Volatility and Return Dynamics
Trading Volume, Volatility and Return Dynamics

Author: Leon Zolotoy

Publisher:

Published: 2007

Total Pages: 36

ISBN-13:

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In this paper we study the dynamic relationship between trading volume, volatility, and stock returns at the international stock markets. First, we examine the role of volume and volatility in the individual stock market dynamics using a sample of ten major developed stock markets. Next, we extend our analysis to a multiple market framework, based on a large sample of cross-listed firms. Our analysis is based on both semi-nonparametric (Flexible Fourier Form) and parametric techniques. Our major findings are as follows. First, we find no evidence of the trading volume affecting the serial correlation of stock market returns, as predicted by Campbell et.al (1993) and Wang (1994). Second, the stock market volatility has a negative and statistically significant impact on the serial correlation of the stock market returns, consistent with the positive feedback trading model of Sentana and Wadhwani (1992). Third, the lagged trading volume is positively related to the stock market volatility, supporting the information flow theory. Fourth, we find the trading volume to have both an economically and statistically significant impact on the price discovery process and the co-movement between the international stock markets. Overall, these findings suggest the importance of the trading volume as an information variable.

Dynamic Trading Volume
Dynamic Trading Volume

Author: Paolo Guasoni

Publisher:

Published: 2014

Total Pages: 36

ISBN-13:

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We derive the process followed by trading volume, in a market with finite depth and constant investment opportunities, where a representative investor, with a long horizon and constant relative risk aversion, trades a safe and a risky asset. Trading volume approximately follows a Gaussian, mean-reverting diffusion, and increases with depth, volatility, and risk aversion. The model generates an endogenous ban on leverage and short-selling.

Volume, Volatility and Momentum in Financial Markets
Volume, Volatility and Momentum in Financial Markets

Author: Marcus Davidsson

Publisher:

Published: 2014

Total Pages: 13

ISBN-13:

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In this paper we will discuss the relationship among volume, volatility and return momentum in global financial markets. It turns out that when the volatility is large i.e. the difference between the daily high price and the daily low price is large then the trading volume is also large. We also found that a momentum strategy on volume perform on par with a momentum return investment strategy. A significant amount of positive serial correlation was also found in the volatility and volume.

Dynamic Volume-Volatility Relation
Dynamic Volume-Volatility Relation

Author: Hanfeng Wang

Publisher:

Published: 2005

Total Pages: 39

ISBN-13:

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We find that trading volume not only contributes positively to the contemporaneous volatility, as indicated in previous literature, but also contributes negatively to the subsequent volatility. And this pattern between trading volume and volatility is consistently held among individual stocks, volume-based portfolios, size-based portfolios, and market index, and among daily data and weekly data. These empirical findings tend to support that the Information-Driven-Trade (IDT) hypothesis is more pervasive and powerful in explaining trading activities in the stock market than the Liquidity-Driven-Trade (LDT) hypothesis. Our additional tests obtain three interesting findings, 1) liquidity and the degree of information asymmetry influence the relation between volume and subsequent volatility, 2) the effect of volume on subsequent volatility and volume size have a non-linear relationship, which is consistent with Barclay and Warner (1993, JFE)'s finding, 3) the effect of volume on subsequent volatility is asymmetry when the stock price moves up and when the stock price moves down, and we attribute this asymmetry to the short-selling constraints.