ATTENTION:
BEFORE YOU READ THE ABSTRACT OR CHAPTER ONE OF THE PROJECT TOPICS BELOW, PLEASE READ THE INFORMATION BELOW.THANK YOU!
INFORMATION:
YOU CAN GET THE COMPLETE PROJECT OF THE TOPIC BELOW. THE FULL PROJECT COST N5,000 ONLY. THE FULL INFORMATION ON HOW TO PAY AND GET THE COMPLETE PROJECT IS AT THE BOTTOM OF THIS PAGE. OR
YOU CAN CALL: 08068231953, 08137701720, 09070569307, 08154275408
WHATSAPP US ON: 08137701720
THE ROLE OF TECHNOLOGY IN ASSET MANAGEMENT AND THE RELATIONSHIP BETWEEN STYLISED FACTS AND THE NATURE OF FINANCIAL MARKETS
ABSTRACT
This work discusses the role of technology in asset management.
Technology is used by both internal and external asset managers to manage data, assess risk, verify compliance, and take care of various operational requirements. Investment professionals make the actual investment decisions, even when technology aids in decision-making.
the so-called leverage effect(a negative correlation between past returns and future volatility), and the increased downside correlations. For individual stocks, the leverage correlation can be rationalized in terms of a new ‘retarded’ model which interpolates between a purely additive and a purely multiplicative stochastic process
The purpose of technology asset management is to ensure that every IT asset is properly used, maintained, upgraded and disposed of at the end of its lifecycle. IT asset management (ITAM) involves using financial, contractual and inventory data to track and make strategic decisions about IT assets. ITAM is the practice of managing and optimizing information technology (IT) assets, such as computers, databases, systems, applications, and networks across an organization3. ITAM is used by both internal and external asset managers to manage data, assess risk, verify compliance, and take care of various operational requirements.
A stylized fact is a term used in economics to refer to empirical findings that are so consistent (for example, across a wide range of instruments, markets and time periods) that they are accepted as truth. Due to their generality, they are often qualitative.
Sewell (2006)
“In social sciences, especially economics, a stylized fact is a simplified presentation of an empirical finding. While results in statistics can only be shown to be highly probable, in a stylized fact, they are presented as true. They are a means to represent complicated statistical findings in an easy way. A stylized fact is often a broad generalisation, which although essentially true may have inaccuracies in the detail.”Wikipedia (2006)
“Definition: Stylized facts are observations that have been made in so many contexts that they are widely understood to be empirical truths, to which theories must fit. Used especially in macroeconomic theory. Considered unhelpful in economic history where context is central. “
About.com (2006)
“Nevertheless, the result of more than half a century of empirical studies on financial time series indicates that this is the case if one examines their properties from a statistical point of view: the seemingly random variations of asset prices do share some quite nontrivial statistical properties. Such properties, common across a wide range of instruments, markets and time periods are called stylized empirical facts.
Stylized facts are thus obtained by taking a common denominator among the properties observed in studies of different markets and instruments. Obviously by doing so one gains in generality but tends to lose in precision of the statements one can make about asset returns. Indeed, stylized facts are usually formulated in terms of qualitative properties of asset returns and may not be precise enough to distinguish among different parametric models. Nevertheless, we will see that, albeit qualitative, these stylized facts are so constraining that it is not easy to exhibit even an (ad hoc) stochastic process which possesses the same set of properties and one has to go to great lengths to reproduce them with a model.”Cont (2001)
“An important part of the research is the analysis of financial data [1-3], which has led to the characterization of some empirical statistical regularities, known as “stylized facts”.”
“The one common observation across these dimensions is that “market activity” is strongly correlated with price variability. Trading volume, return volatility, and bid-ask spreads are highest around the open and close of trading; return variability per unit of time is higher over trading than nontrading periods; trading volume and spreads are particularly high on days with large return innovations; and public information releases—which theoretically may induce price jumps without any trading—are typically associated with extrememely heavy volume.”Andersen and Bollerslev (1998)
.A hundred and twenty questionnaires were distributed among students and teachers from selected secondary schools in Nigeria. Interviews and surveys were also conducted.
Primary and secondary data will be used in the analysis. Tables and percentages will also be used as the instrument of analysis
It will be observed therefore that technology have a strong and significant positive impact in asset management. There is a strong positive relationship between Leverage of technology and asset management effectiveness.
TABLE OF CONTENT:
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
1.2 Statement of the Research Problem
1.3 Objectives of the Study
1.4 Significance of the Study
1.5 Research Questions
1.6 Ethical issues
1.7 Research Hypothesis
CHAPTER TWO
LITERATURE REVIEW
2.1 Stylised facts
2.2 Relationship between stylized facts and the nature of financial markets
2.3 Role of technology in asset management
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Research Method
3.2 Research Design
3.3 Research Sample
3.4 Measuring Instrument
3.5 Data Collection
3.6 Data Analysis
3.7 Expected Result
CHAPTER FOUR
DATA ANALYSIS AND RESULTS
4.1 Data Analysis
4.2 Results
4.3 Discussion
CHAPTER FIVE
Summary And Recommendations
List of references
5.1 Summary
5.2 Recommendations for Further Study
References
CHAPTER ONE
INTRODUCTION
BACKGROUND OF THE STUDY
Volatility modeling of financial time series data such as asset returns has become an interesting area of research among researchers and practitioners. This is because volatility is an important concept for many economic and financial applications such as portfolio optimization, risk management, options trading and asset pricing. According to Tsay (2002), a special feature of volatility which is the conditional variance of the underlying asset returns, is that it is not directly observable. Thus, financial analysts have keen interest in obtaining good estimates of this conditional variance in order to improve portfolio allocation, risk management and valuation of financial derivatives. Various types of models such as autoregressive conditional heteroskedasticity and stochastic volatility models have been applied in modeling volatility.
In modeling the volatility of financial time series data researchers have revealed a wealth of interesting statistical properties called “stylized facts”. Stylized facts are empirical findings that are so consistent and believed to hold for a diverse collection of instruments, markets and time periods. A number of researchers have studied stylized facts of asset returns including studies by Cont (2001); Ding et al. (1983); Guillaume et al. (1997) and Pagan (1986) who summarized the most important stylized facts of assets returns as: Absence of autocorrelations, non Gaussianity, heavy/fat tails, aggregational Gausianity, gain/loss asymmetry, leverage effect, volatility clustering, and volatility mean reversion among others. A good volatility model must then be able to capture and reflect these stylized facts.
The first documented evidence of volatility clustering, leptokurtosis and leverage effects was observed by Mandelbrot (1963) who found evidence of the tendency of large changes in asset prices (either positive or negative) to be followed by large changes in asset prices and small changes in asset prices to be followed by small changes. Similar results were found in studies conducted independently by Fama (1965) and Black (1976). A vast of documented evidence on the subject matter both for developed and emerging stock markets are found in the literature. See for example; Harris (1986), Fama (1970), Du and Ning (2008), Fama and French (1988), Ding and Granger (1996), Granger and Ding (1996), Gibbons and Hess (1981), Granger and Hyung (2004), Greene & Fielitz (1977), Hamao & Hasbrouck (1995), Hansen & Lunde (2006) among others for more surveys. In Nigeria, several studies have been conducted on volatility modelling which provide more insights on the subject matter. For instance, Olowe (2009) investigated the relationship between stock returns and volatility in Nigeria using EGARCH-in-mean model in the light of banking reforms, insurance reform, stock market crash and the global financial crisis. He used daily returns for the period 4 January 2004 to January 9, 2009. The result shows persistence of volatility and presence of leverage effects. In a related development Okpara (2011) conducted a study to investigate the relationship between the stock market returns and volatility in Nigerian stock market using the same EGARCH– in–mean framework. He used monthly stock price data from Nigerian stock Exchange. The study found the Nigerian stock market to be volatile with high level of risk in stock trading with leverage asymmetric effects. The study also found low persistence of volatility clustering which suggest that increase in volatility is not likely to remain high over several periods. Awogbemi & Alagbe (2011) examined the volatility of Naira/US Dollar and Naira/UK Pound Sterling exchange rates in Nigeria using GARCH model. They used data on monthly exchange rates for the period 2007-2010 and found evidence of volatility persistence and clustering in Nigeria.
Onakoya (2013) conducted a study to examine the relative contributions of stock market volatility on economic growth in Nigeria for the period of 1980 to 2010 using Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model. The study found volatility shocks to be quite persistent in Nigeria and remarked that this might distort growth of the economy. Adesina (2013) used symmetric and asymmetric GARCH models to estimate stock return volatility in Nigerian Stock Exchange (NSE). The study used 324 monthly data from January 1985 to December 2011 of the NSE all share-index and found enough evidence of volatility clustering and high persistence of volatility for the NSE return series with no asymmetric shock phenomenon (leverage effects) for the return series. Olusola & Opeyemi (2013) used a parametric measure to study the trend and possible causes of exchange rate volatility in Nigeria for the period 1986:1 to 2009:4. The study revealed that exchange rate has been volatile in Nigeria which portrays higher risk to a risk-averse economic agent. On investigating the nature of volatility clustering, persistence and leptokurtic nature of asset returns in Nigeria using individual bank indices and the All-share Index of the Nigerian Stock Exchange, Emenike & Ani (2014) found evidence of volatility clustering, persistence and fat tail distribution which have insignificant influence on the volatility of stock returns of the banks. Osazevbaru (2014a) empirically examined the presence or otherwise of volatility clustering in Nigerian stock market using time series data of daily share prices for the period 1995 to 2009. He employed the Autoregressive Conditional Heteroscedasticity (ARCH) Model and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. The estimates indicate that the market exhibits volatility clustering and the rate at which the response function decays was found to be very high. He suggested that aggressive trading on a wide range of securities be encouraged as this will increase market depth and hence reduce volatility.
Osazevbaru (2014b) investigated the hypothesized relationship between market news and volatility using daily and monthly stock data of the Nigerian stock market for the period 1995 to 2011. He used Threshold Generalized Autoregressive Conditional Heteroscedasticity, TGARCH (1,1) model and found no asymmetries in the market news and the impact of bad news was not larger on volatility than good news. He also found the Nigerian market to be such that old information wields more importance than recent information. Uwubanmwen & Omorokunwa (2015) also found evidence of volatility clustering and persistence in Nigeria by showing that oil price volatility generates and stimulates stock prices volatility in Nigeria.
STATEMENT OF THE PROBLEM
Different volatility models across different economies to investigate stylized facts of financial returns all agreed that some of these empirical properties exist. Although in Nigeria, most researchers are interested in examining volatility clustering, persistence, leptokurtosis and leverage effects. This study investigates the stylized facts characterized by developed markets in emerging stock markets like Nigeria using GARCH invariants and more recent data. The parameter estimation procedure adopted in this study assumes Gaussian error structure typical of GARCH-type models unlike the ones found in the literature which assumed non-Gaussian errors.
OBJECTIVES OF THE STUDY
- To understand the impact of technology on asset management
- To understand the relationship between stylized facts and the nature of financial markets
- To understand the relationship between technology and asset management.
RESEARCH QUESTIONS
- What is the impact of technology on asset management
- What is the relationship between stylized facts and the nature of financial markets
- What is the relationship between technology and asset management.
RESEARCH HYPOTHESIS
H0: There is no relationship between technology and asset management.
H1: There is a relationship between technology and asset management
HOW TO RECEIVE PROJECT MATERIAL (S)
After paying the appropriate amount (#5,000) into our bank Account below, send the following information to any of the numbers below
08068231953, 08137701720, 09070569307, 08154275408 (1) Your project topics
(2) Email Address
(3) Payment Name
OR you drop them on our WhatsApp, 08137701720
We will send your material(s) after we receive bank alert
BANK ACCOUNTS
Account Name: AMUTAH DANIEL CHUKWUDI
Account Number: 0046579864
Bank: GTBank.
OR
Account Name: AMUTAH DANIEL CHUKWUDI
Account Number: 3139283609
Bank: FIRST BANK
FOR MORE INFORMATION, CALL:
08068231953, 08137701720, 09070569307, 08154275408
http://graduateprojects.com.ng