Every modern economy is based on a sound financial system and acts as a monetary channel for productive purpose with effecting economic growth. It encourages saving habit by throwing open and plethora of instrument avenues suiting to the individuals requirements, mobilizing savings from households and other segments and allocating savings into productive usage such as trade, commerce, manufacture etc.
Thus a financial system can also be understood as institutional arrangements, through which financial surpluses are mobilized from the units generating surplus income and transferring them to the others in need of them. In nutshell, financial market, financial assets, financial services and financial institutions constitute the financial system. The activities include exchange and holding of financial assets or instruments of different kinds of financial institutions, banks and other intermediaries of the market.
Financial markets provide channels for allocation of savings to investment and provide variety of assets to savers in various forms in which the investors can park their funds. At the same time, financial market is one that integral part of the financial system which makes significant contribution to the countries’ economic development. It establishes a link between the demand and supply of long-term capital funds. The economic strength of a country depends squarely on the state of financial market, apart from the productive potential of the country. The efficient allocation of fund by the capital market depends on the state of capital market. All the countries therefore focus more on the functioning of the capital market. Indian financial market has faced many challenges in the process of effecting more efficient allocation and mobilization of capital.
It has attained a remarkable degree of growth in the last decade and in continuing to achieve the same in current decade also. Opening up of the economy and adoption of the liberalized economic policies have driven our economy more towards the free market. Over the last few years, financial markets, more specifically the security market were experiencing a lot of structural and regulatory changes. The major constituents of financial market are money market and the capital market catering to the type of capital requirements.
Table of Contents
Chapter - I
Introduction and Research Design
Chapter - II
Review of Literature
Chapter - III
Theoretical Overview of Futures and Option Market
Chapter - IV
4.1 Introduction
4.2 Econometric Methodology
4.2.1 Unit Root Test
4.2.1.1 Augmented Dickey Fuller Test
4.2.1.2 Phillips-Peron (PP) test
4.2.2 GARCH Model
4.2.3 Augmented GARCH Model
4.2.3.1 Expected Components of Trading Volume & Open Interest
4.2.3.2 Unexpected Components of Trading Volume & Open Interest
4.3 Results and Discussion
4.4 Conclusion
Chapter - V
5.1 Introduction
5.2 Econometric Methodology - Linear and Non- Linear Models
5.3 Results & Discussion
5.4 Forecast Evaluation
5.5 Summary and Conclusion
Chapter - VI
Summary, Conclusion and Policy Implications
Research Objectives and Focus
The primary goal of this research is to analyze the dynamic interdependencies between price volatility, trading volume, and market depth within the Indian stock futures market. It aims to determine whether information arrival patterns follow a sequential or mixture-of-distributions hypothesis, while identifying the most accurate econometric models for forecasting stock futures volatility in a developing market context.
- Conceptual framework and development of the Indian derivatives market.
- Empirical relationship analysis between price volatility, trading volume, and open interest.
- Comparative performance evaluation of linear and non-linear forecasting models.
- Policy implications for regulators and market participants regarding risk management.
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CHAPTER - IV THE EMPIRICAL RELATIONSHIP BETWEEN PRICE VOLATILITY, TRADING VOLUME AND MARKET DEPTH OF STOCK FUTURES
Financial media regularly reports daily trading activities to the stock markets. The information content of this data in terms of volatilities of price, trading volume and market depth has long attracted the attention of many researchers, policy makers and investors, to examine if there is any relationship between these variables and the types of relationship that exist between these variables. Trading volume offers useful information for practitioners and investors in investment decisions, as well as for researchers and policy makers in testing the theories of financial economics. The contemporaneous relation between price movements, trading volume and open interest on financial markets keeps attracting the attention of many financial economists. Karpoffs (1987) seminal paper summarizes the importance of this research area by presenting the following argument. First, the returns or trading volume relation provides insight into the structure of financial markets. Second, the returns or trading volume relation is important for event studies that use a combination of stock returns and trading volume data to draw inferences. Third, the returns or trading volume relation is critical to the debate over the empirical distribution of speculative prices. As far as relationship between volume and price changes relative is concerned, a positive relationship was documented first by Ying (1966).
Later, empirical research of Granger and Morgenstern (1963) also focused only on positive contemporaneous relationship between asset price volatility and trading volume. This dissertation adds to the growing literature by examining the relationship between stock returns and trading volume data, with two different dimensions. First, on the prices changes and trading volume, if moves together with the market, which is called Sequential Information Arrival hypothesis (SEQ). Second, the dimensions from the information may be considered as mixing variables under the Mixture of Distribution hypothesis (MDH). It is really intricate; to test the informational content of futures market by using futures market returns series. There is an old Wall Street adage that “It takes trading volume to make prices move”. Hence, the basic logic in using trading activity variable is due to the explanatory power in predicting future price changes.
Summary of Chapters
Chapter - I: Provides an overview of the financial system in India, the role of derivatives, and the research design for the study.
Chapter - II: Reviews existing empirical and theoretical literature regarding the relationship between futures returns, trading volume, and volatility.
Chapter - III: Discusses the conceptual framework of derivatives, including market mechanisms, product types, and the economic functions of futures markets.
Chapter - IV: Empirically examines the dynamic relationship between price volatility, trading volume, and market depth using GARCH models.
Chapter - V: Evaluates various linear and non-linear models for forecasting stock futures volatility and assesses their predictive performance.
Chapter - VI: Summarizes the findings, draws conclusions, and provides policy implications for market participants and regulators.
Keywords
Stock Futures, Price Volatility, Trading Volume, Market Depth, GARCH Models, Econometric Methodology, Financial Derivatives, Information Arrival, Volatility Forecasting, Market Efficiency, Open Interest, Risk Management, Indian Stock Market, Sequential Information Arrival Hypothesis, Mixture of Distributions Hypothesis
Frequently Asked Questions
What is the core focus of this research?
The work investigates the complex interplay between price volatility, trading volume, and market depth within the Indian stock futures market, seeking to understand how these factors affect market dynamics.
What are the primary topics covered?
The study covers the conceptual framework of derivatives, the empirical relationship between volatility and trading variables, and the comparative efficacy of linear and non-linear forecasting models.
What is the ultimate objective of the thesis?
The primary objective is to identify a suitable model to accurately forecast volatility for select stock futures contracts in the Indian market to assist policymakers and the research community.
Which scientific methods are employed?
The researcher utilizes econometric techniques, specifically unit root tests (ADF and PP) and various GARCH-family models (including GARCH, TGARCH, EGARCH, and IGARCH) to analyze time-series data.
What does the main body of the work address?
The main body examines the statistical distributional properties of return, volume, and open interest, and rigorously tests different forecasting models to determine their reliability in predicting market fluctuations.
Which keywords best characterize this work?
Key terms include Stock Futures, Price Volatility, Trading Volume, GARCH Models, Volatility Forecasting, and Market Depth.
How do unexpected trading volume and open interest affect volatility?
The study finds that unexpected components of trading volume and open interest generally have a greater impact on volatility than expected components, highlighting their importance for market information.
Does the study find support for the Samuelson Hypothesis in India?
The study concludes that the Samuelson Hypothesis does not provide strong support for the Indian futures market, as the market demonstrates different behavioral characteristics regarding maturity effects.
What is the role of market depth in the author's findings?
Interestingly, the research indicates that market depth (as represented by open interest) does not have a significant effect on price volatility in the analyzed stock futures contracts.
What policy recommendations emerge from the study?
The author suggests that regulators should strengthen risk management practices, reconsider margin requirements for members based on capital adequacy, and ensure efficient settlement mechanisms to handle market volatility.
- Quote paper
- Srinivasan Kaliyaperumal (Author), 2010, An Analysis of Price Volatility, Trading Volume and Market Depth of Stock Futures Market in India, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/415947
