Academic journal article IUP Journal of Applied Finance

Intraday Trading Activity and Volatility: Evidence from Energy and Metal Futures

Academic journal article IUP Journal of Applied Finance

Intraday Trading Activity and Volatility: Evidence from Energy and Metal Futures

Article excerpt

(ProQuest: ... denotes formulae omitted.)

Introduction

The purpose of this study is to investigate and test theoretical models that explain the volume-volatility relationship. Studying the volume-volatility relationship is important as it provides more insights into the structure of the financial markets, which has implications for the market participants (Nguyen and Daigler, 2006). Empirical evidence suggests that information that arrives in the market affects both volume and price volatility simultaneously. For example, Schwert (1989), Gallant et al. (1992), and Daigler and Wiley (1999) find support for the contemporaneous positive relationship between volume and volatility. We can classify models into two categories, which explain the volume-volatility relationship: (1) Information theories associate information with the volume and volatility, and (2) Dispersion of belief theories argue that traders have heterogeneous beliefs about the information and act accordingly, which in turn affects volume and volatility in the market.

In this study, we test both information-based theories and dispersed belief models by using tick-by-tick data for the five commodity futures (crude oil, gold, silver, copper, and zinc) traded at Multi-Commodity Exchange India Ltd. (MCX) for the period January 1, 2009 to December 31, 2012. The use of tick-by-tick data allows us to gain more insights into the trading activities in these markets and to explain the volume-volatility relationship. We use trading number and order imbalance (as a proxy for the information variable in these markets) in order to test the theoretical models developed to explain volume-volatility dynamics.

Informed and uninformed traders play an important role in explaining information- based and dispersed belief models. Black (1986) argues that noise traders induce more risk by increasing the volatility through their noisy information-based trades. If this is the case, then trading activity as the proxy for information may be noisy too. Kyle (1985), Admati and Pfleiderer (1988) demonstrate that the order imbalance can provide better information than trading number about the informed trades. As the informed traders have access to the trading sources, they have superior information to that of noisy traders and they cluster their trades on one side of the market. We can capture this information through order imbalance. In other words, order imbalance is a suitable proxy for the quality of information possessed by the informed traders (Fung and Kao, 2012). Chan and Fong (2000) find that order imbalance has the ability to explain the volume-volatility relationship. To summarize, trade number and order imbalance are the main factors in explaining volume-volatility dynamics.

We examined the role of trade number and order imbalance in explaining the relationship between trading volume and return volatility. We contribute to the extant literature in several ways. First, we have used intraday data for commodity futures, whereas most previous studies on volume-volatility have used daily data for the exchange rate, stocks, and indices. Studies on emerging markets using high frequency data on commodities are rare. We have used trading number and order imbalance as a proxy for the uninformed and informed traders, respectively. The intraday data provides us with more insights into the role of these proxies when attempting to explain the volume-volatility relationship.

Second, previous studies have separately tested theories explaining the volume-volatility relationship. Therefore, it is difficult to conclude whether these theories are complementary or competitive. In this study, we have tested all four hypotheses: the Mixture of Distribution Hypothesis (MDH), the Sequential Information Arrival Hypothesis (SIAH), the dispersed belief hypothesis, and the asymmetric information hypothesis. Finally, to the best of our knowledge, this is the first study that has used high frequency data to explain the volume- volatility relationship in the context of the emerging Indian commodity market. …

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