What Can Explain the Price, Volatility and Trading Volume of Bitcoin?

2018 ◽  
Author(s):  
Halvor Aalborg ◽  
Peter Molnár ◽  
Jon Erik de Vries
Keyword(s):  
Trading Volume ◽  
Price Volatility

Asset price volatility and trading volume with rational beliefs

2004 ◽  
Vol 23 (4) ◽  
pp. 795-829 ◽  
Author(s):  
Ho-Mou Wu ◽  
Wen-Chung Guo
Keyword(s):  
Trading Volume ◽  
Asset Price ◽  
Price Volatility ◽  
Rational Beliefs

Returns and volume

2020 ◽  
Vol 37 (3) ◽  
pp. 457-473
Author(s):  
Panos Fousekis
Keyword(s):  
Trading Volume ◽  
Time Series Data ◽  
Price Volatility ◽  
Statistical Testing ◽  
Model Specification ◽  
Series Data ◽  
Causal Order ◽  
Content Type ◽  
Daily Data ◽  
The Usa

Purpose The relationship between returns and trading volume is central in financial economics because it has both a theoretical interest and important practical implications with regard to the structure of financial markets and the level of speculation activity. The aim of this study is to provide new insights into the association between returns and trading volume by investigating their kernel (instantaneous) causality. The empirical analysis relies on time series data from 22 commodities futures markets (agricultural, energy and metals) in the USA. Design/methodology/approach Non-parametric (local linear) regressions are applied to daily data on returns and on trading activity; generalized correlation measures are computed and their differences are subjected to formal statistical testing. Findings The results suggest that raw returns are likely to kernel-cause volume and volume is likely to kernel-cause price volatility. The patterns of causal order are generally in line with what is stipulated by the relevant theory, they provide guidance for model specification and they appear to explain the empirical evidence on temporal (lag-lead) causality between the same pairs of variables obtained in earlier works. Originality/value The concept of kernel causality has very recently become a part of the toolkit for econometric/statistical analysis. To the best of the author’s knowledge, this is the first study that relies on the notion of kernel (instantaneous) causality to provide new evidence on a relationship that is of keen interest to investors, professional economists and policymakers.

What can explain the price, volatility and trading volume of Bitcoin?

2019 ◽  
Vol 29 ◽  
pp. 255-265 ◽  
Author(s):  
Halvor Aarhus Aalborg ◽  
Peter Molnár ◽  
Jon Erik de Vries
Keyword(s):  
Trading Volume ◽  
Price Volatility

Monotonicity, linearity and symmetry in the price volatility–volume relationship

2020 ◽  
Vol 37 (1) ◽  
pp. 110-133 ◽  
Author(s):  
Panos Fousekis ◽  
Dimitra Tzaferi
Keyword(s):  
Trading Volume ◽  
Futures Markets ◽  
Statistical Tests ◽  
Price Volatility ◽  
Local Linear Regression ◽  
Linear Regression Models ◽  
Content Type ◽  
Nonparametric Approach ◽  
Volume Relationship ◽  
The Usa

Purpose This paper aims to investigate the contemporaneous link between price volatility and trading volume in the futures markets of energy. Design/methodology/approach Non-parametric (local linear) regression models and formal statistical tests are used to assess monotonicity, linearity and symmetry. The data are daily price and volumes from five futures markets (West Texas Intermediate, Brent, gasoline, heating oil and natural gas) in the USA. Findings Trading volume and price volatility have, in all markets, a strong nonlinear relation to each other. There are violations of monotonicity locally but not globally. The qualitative nature of the price shocks may have implications for the trading activity locally. Originality/value To the authors’ best knowledge, this is the first manuscript that investigates simultaneously and formally all the three important issues (i.e. monotonicity, linearity and asymmetry) for the price volatility–volume relationship using a highly flexible nonparametric approach.

The impact of order imbalance on returns, liquidity, and volatility in agricultural commodity markets

2018 ◽  
Vol 78 (5) ◽  
pp. 571-591 ◽  
Author(s):  
Steffen Volkenand ◽  
Guenther Filler ◽  
Martin Odening
Keyword(s):  
Trading Volume ◽  
High Speed ◽  
Price Volatility ◽  
Agricultural Commodity ◽  
Content Type ◽  
Order Imbalance ◽  
New Information ◽  
Agricultural Futures ◽  
Best Offer ◽  
The Impact

PurposeThe purpose of this paper is to investigate and compare the impact of order imbalance on returns, liquidity and price volatility in agricultural futures markets on an intraday basis. The authors examine whether order imbalance is more powerful to explain variations in asset prices compared to other indicators of trading activity, particularly trading volume.Design/methodology/approachUsing Chicago Mercantile Exchange best bid best offer data, the impact of order imbalance is analyzed via regression analyses. The analyses are carried out for corn, wheat, soy, live cattle and lean hogs in March 2008 and March 2016.FindingsResults confirm the positive relation between order imbalance and returns as well as between order imbalance and price volatility as suggested by market microstructure models. Order imbalance, however, does not generally outperform trading volume as an explanatory variable.Practical implicationsFor some contracts, returns can be predicted using lagged order imbalance. This offers the opportunity to derive profitable trading strategies.Originality/valueThis paper is one of the first attempts to explore the relationship between order imbalance and returns, liquidity and volatility for agricultural commodity futures on an intraday basis, accounting for the increased trading volume and for the high speed at which new information enters the market in an electronic trading environment.

Volatility persistence and trading volume in an emerging futures market

2010 ◽  
Vol 11 (3) ◽  
pp. 296-309 ◽  
Author(s):  
Pratap Chandra Pati ◽  
Prabina Rajib
Keyword(s):  
Capital Market ◽  
Trading Volume ◽  
Price Volatility ◽  
Futures Market ◽  
Market Volatility ◽  
Leverage Effect ◽  
Time Varying ◽  
Conditional Volatility ◽  
Content Type ◽  
Futures Contract

PurposeThe purpose of this paper is to estimate time‐varying conditional volatility, and examine the extent to which trading volume, as a proxy for information arrival, explain the persistence of futures market volatility using National Stock Exchange S&P CRISIL NSE Index Nifty index futures.Design/methodology/approachTo estimate the volatility and capture the stylized facts of fat‐tail distribution, volatility clustering, leverage effect, and mean‐reversion in futures returns, appropriate ARMA‐generalized autoregressive conditional heteroscedastic (GARCH) and ARMA‐EGARCH models with generalized error distribution have been used. The ARMA‐EGARCH model is augmented by including contemporaneous and lagged trading volume to determine their contribution to time‐varying conditional volatility.FindingsThe paper finds evidence of leverage effect, which indicates that negative shocks increase the futures market volatility more than positive shocks of the same magnitude. In addition, the results indicate that inclusion of both contemporaneous and lagged trading volume in the GARCH model reduces the persistence in volatility, but contemporaneous volume provides a greater reduction than lagged volume. Nevertheless, the GARCH effect does not completely vanish.Practical implicationsResearch findings have important implications for the traders, regulatory bodies, and practitioners. A positive volume‐price volatility relationship implies that a new futures contract will be successful only to the extent that there is enough price uncertainty associated with the underlying asset. Higher trading volume causes higher volatility; so, it suggests the need for greater regulatory restrictions.Originality/valueEquity derivatives are relatively new phenomena in Indian capital market. This paper extends and updates the existing empirical research on the relationship between futures price volatility and volume in the emerging Indian capital market using improved methodology and recent data set.

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