Dissertations / Theses on the topic 'Stock market- India'

This thesis adopts an empirical approach to examine various market microstructure issues, using data from the National Stock Exchange of India (NSE). Whilst the respective empirical analyses may be considered as self-contained investigations, they are primarily linked through the common objective of understanding the mechanics of the pricing process as it occurs on actual markets, using the NSE as exemplar. The first major focus of the dissertation is non-synchronous trading: empirical evidence of nonsynchronicity is obtained by testing for predictability as between indices of different levels of liquidity. A simple test of the analysis of trading-break returns is proposed to infer whether predictability may be mainly attributable to non-synchronous trading or whether it constitutes a delayed adjustment of traders' expectations. The second question tackled in the thesis is whether volatility on the NSE may be considered as justified or excessive. Rathert han adopting the established methodology of comparing stock price changes to information about expected dividends, the research question is split up into two subsidiary ones. The first question is whether volatility is related to information flows, whilst the second related questionc oncernst he relationship betweenv olatility and returns. Three sources of excessive volatility are pin-pointed. Monday effects are found in index data but not in the underlying stocks-indicating index fluctuations which are not information-related. A second indicator of excessive price movements is the pronounced volatility which coincides with the fiscal year end of quoted companies but which is not accompanied by a similar increase in long-term returns. A third indication of unjustified price fluctuations is that volatility seems unrelated to returns when considering a long-term time series. The third topic of the thesis relates to the efficacy of opening and closing call auctions. This issue may be considered as the crux of the dissertation and it is tackled by analysing the effects of the suspension of a call auction system on NSE. Changes in volatility, efficiency and liquidity following the suspension are analysed, and an event study is presented. The relationship between call auctions and long-term volatility is also investigated. The findings suggest that the expected benefits of call auctions may not always materialise, possibly due to an inappropriately structured auction, or because a liquidity threshold for stocks must be surpassed for the expected benefits to accrue.

L'elaborato propone uno studio del comportamento degli investitori attivi sul mercato azionario e su quello dei futures della borsa indiana NSE National Stock Exchange, una delle principali piattaforme di e-trading del mondo per numero di scambi. Attraverso l'analisi delle scelte di investimento dei traders nel periodo compreso tra l'inizio di aprile 2006 e la fine del mese di giugno dello stesso anno, la tesi intende approfondire le dinamiche relative alla liquidità e il ruolo dei grandi investitori nel condizionare l'andamento delle transazioni. L'analisi derivante dall'applicazione di misure di liquidità del mercato e lo studio delle frequenze di trading, dei volumi e dei profitti ottenuti dagli investori permettono di evidenziare alcune delle dinamiche che governano i mercati finanziari. Lo studio è stato condotto sull'andamento del trading su un singolo asset scambiato presso NSE National Stock Exchange of India.

This thesis investigates the weak-form of the Efficient Market Hypothesis (EMH) in the South Asian region. In particular, the emerging market countries of Bangladesh, India, Pakistan and Sri Lanka are considered. According to the weak-form of the EMH, current share prices reflect all available historical information such that investors should not be able to outperform the market on a consistent basis by trading on past information. It is an important topic for investigation given the economic growth as well as the financial development which have taken place in the region over the last two decades (South Asian Financial Markets Review, 2010). Moreover, most previous studies have investigated the topic for developed or other emerging markets; the South Asian region has largely been ignored. Prior studies which have investigated the South Asian markets have either focused on each country separately, or included one or two countries from the region as part of a broader sample. This thesis tries to fill this gap in the literature by investigating market efficiency in the South Asian markets as a regional grouping. In the first part of the analysis the long- and short-run relationships among the four stock markets are examined by employing a multivariate cointegration framework, the Vector Error Correction Model (VECM) approach, the Granger Causality test, Impulse Response Function analysis and Variance Decomposition analysis. A large sample of weekly stock index data is used in the analysis covering the 18-year period January 1993 - December, 2010. To analyse the effect of important global events on market integration, the data are split into the two sub-periods of pre- and post-September 11, 2001. The results suggest that linkages exist among the markets in both the long- as well as in the short-run. These findings imply that share price changes may be predicted from historical information not only in the market itself but from the changes in the other three markets as well. In addition, international portfolio diversification into the region may have limited benefits in the long-run as equity prices in all four countries move together in an equilibrium fashion over the longer run.In the second empirical analysis, relationship between the equity returns and macroeconomic variables is investigated. The research examines the EMH by investigating whether lagged shocks to macroeconomic variables are important in explaining equity returns. Both local and global macroeconomic variables are used and their importance in predicting the equity returns for each of the region’s markets is analysed. In particular, 12 macroeconomic variables were investigated, including seven local and five global measures being employed. Principal Components Analysis (PCA) is used to narrow down the most relevant factors. Principal Components (PCs) are then extracted and used as inputs into regressions explaining future returns. The resulting findings show that local economic factors are important in explaining share returns in the South Asian emerging stock markets. The findings support the notion that historical macroeconomic information may be used to predict share price changes in the regional markets. Finally, to investigate market linkages in greater depth, the thesis studies volatility and return interactions among the markets simultaneously. A multivariate GARCH-BEKK model is used to investigate return and volatility spillovers in own as well as in cross-markets. Results from the analysis indicated that the four markets of Bangladesh, India, Pakistan and Sri Lanka are linked not only by the news transmission about the share returns but also by the transmission of volatility. The evidence supports the notion that ‘news’ in one market influences not only the returns in that market but also the variance of price changes in other markets. These findings imply that equity returns in the South Asian stock markets are predictable from historical share price changes in their own, as well as from the other markets of the region; this result calls the weak form of the EMH into question since it suggests that an investor could outperform by studying historic return and volatility data in the region.

Problem background and discussion: Emerging countries economies are growing substantially; one of these is India which stock market has been one of the best performing in the world in recent years. Analysts are forecasting further development and some claims that India has the most business- and investment-stimulating political leaders in the world. However, stock markets in emerging countries are highly volatile and normally more risky than in developed economies. One approach to emphasise the more common risks in emerging countries are by including Environmental, Social and Governance (ESG) rating into the fundamental investment model. However, there is a conflict of what previous studies suggest regarding ESG investments. Some argues there is a positive relation and others a negative relation between ESG factors and risk- adjusted return. Research question: “Is there a relation between risk-adjusted return and ESG performance at the Indian stock market?” Objective: The objective is to determine if there is a relationship between ESG performance and risk-adjusted return in India. Another objective is to determine if there is a relationship between ESG performance and risk-adjusted return among companies with high Total ESG rating as well as for companies with low Total ESG rating. Theoretical framework: ESG is an established approach to describe sustainability issues, where screening is a process designed to select those companies that meet ESG criteria. A basic description of Capital Asset Pricing Model CAPM, which calculates an asset's expected return, has been used to calculate risk-adjusted return. Efficient Market Hypothesis EMH is the basic theory of market efficiency and is used to explain any non-linear relationship between ESG factors and risk-adjusted returns. Adaptive Market Hypothesis AMH has been taken into account as it deals with financial behaviour. Method: A quantitative study using a deductive approach has been selected to perform this study. The practical approach is a cross sectional study where the relationship in the Indian market has been analysed and significance-tested during 2014. ESG information for 126 companies listed on the Bombay Stock Exchange (BSE) has been purchased from Sustainalytics, a global leader in research for responsible investment. Empirical findings and analysis: The results of the study demonstrate no significant relationship between Total ESG rating and risk-adjusted return during 2014. In the examination of individual categories, Environmental and Social rating does not have a significant association with the risk-adjusted Return. Though, the results display a negative relationship between Governance rating and risk-adjusted return. This relationship is also obtained among companies in with low Total ESG rating but not companies with high ESG rating. Conclusion: Results implies that investors have not been able to use the information of Total ESG performance to obtain a better risk-adjusted return on the Indian stock market in 2014. However, this can be achieved by using Governance rating.

Over the Counter Exchange of India (OTCEI) was established in 1992 mainly to provide a platform for small and medium sized companies to raise money for their capital requirements. It is a well defined dealer market with market makers giving bid and ask quotes. It was established with state-of-the art technology with ringless, scripless trading. In this study, we develop a theoretical model to decompose spread into its three components in a dealer market. This model is further empirically examined by using OTCEI data. We find that Inventory holding cost to be the highest on OTCEI followed by Adverse Information cost and Order Processing cost. The result reflects market microstructure which is peculiar to OTCEI. The methodology developed in this study is basically a generalization of S toll's (1989) methodology. . Roll(1984) shows that in a pure order processing world, spread equals the square root of negative of serial covariances of successive differences of transaction prices. Stoll (1989) relates spread to the covariance of successive difference of transaction prices and that of the quotes. Stoll introduces two parameters, 5, which is a measure of magnitude of price change and JI, the probability of reversal of type of transaction, that is, from Bid to Ask or vice-versa, to model the Bid/Ask price movements from one transaction to the next. Thus Stoll, from this model, establishes a theoretical relationship between serial covariances of successive differences of transaction and quote returns and spread. 5 and n are estimated via regression of serial covariances of transaction and quote, returns on average proportional spread square. With these two parameters, Stoll finally decomposes spread into three components. δ, is the amount of price change between transactions for two reasons- Inventory holding reason and adverse information reason. Stoll explains these price changes due to two reasons with just one parameter, 5. This forms the main motivation of this study. In our study, we let 8 assume two different values, 5i and 82 which attempts to capture the price changes due to the two different causes viz inventory holding and adverse information. It is convenient to think of these two S's being associated with two different states of transactions. However, these states themselves are indeterminate . In other words, the price change could be due to inventory reasons, or due to trading with an informed trader, or due to both. Thus, while Stoll assumes only one 8, in our study, we have two different values of 8. Thus, with three parameters, 81, 82 and n, this study attempts to estimate the relevant parameters and realistically decompose the three components of spread in a dealer market. Just like Stoll, the developed theoretical model also relates serial covariances of transaction price changes and quoted price changes to spread square. However, unlike Stoll, now there are 3 parameters, namely, 5j, 82 and n. As it is impossible to solve three unknowns with just two equations, it becomes necessary to introduce one more equation relating the three parameters to the spread. It is here that we introduce, for the first time, the serial covariance of the second order differences of the transaction price changes, which is related to spread via an equation. Intuitively, we can explain this relationship using Roll's result. Roll(1984) has shown that spread equals square root of the negative serial covariances of transaction price changes in a pure order processing world. Since the second order difference is nothing but the rate of price changes, it also must be related to spread, since the price change themselves are related to it, empirically, we find that spread square significantly affects the serial covariance of second order difference of price changes as well. Besides explaining the price changes with just one 5, Stoll's method of decomposition is not realistic. Though his method of decomposition does yield three components of spread, in reality, it lumps Adverse information cost and Inventory holding cost together. In our study, we make use of the state-of-the art Huang and Stoll's (1997) methodology of decomposition of spread. We first embed the developed theoretical price-movement model into that of Huang and S toll's this yields a functional relationship between 5i and 52 and a and |3 of Huang and Stoll, which directly refers to the adverse information and inventory holding components respectively. Thus, in our study, we realistically decompose the components of spread and OTCEI and empirically too, we find that the components estimated from our methodology does reflect the market microstructure of OTCEI. Apart from developing and empirically testing the theoretical model, we also see if it fits the observed data on OTCEI. We find that the theoretical model does not exactly conform to the observed data in OTCEI, necessitating some empirical fine-tuning. We build an empirical model which is again used to get the three components of spread. We also estimate components of spread in OTCEI using Stoll's and Huang and Stoll's methodology and we compare them with the estimates obtained using our methodology. We find that Stoll's methodology overstates the Adverse information component of spread and understates the inventory holding component of spread and Huang and Stoll's methodology and Our methodology and model yields estimates of components of spread which is more in tune with the market micros tructure of OTCEI. The estimates obtained from empirical model too conforms to the market microstructure of OTCEI.

Over the Counter Exchange of India (OTCEI) was established in 1992 mainly to provide a platform for small and medium sized companies to raise money for their capital requirements. It is a well defined dealer market with market makers giving bid and ask quotes. It was established with state-of-the art technology with ringless, scripless trading. In this study, we develop a theoretical model to decompose spread into its three components in a dealer market. This model is further empirically examined by using OTCEI data. We find that Inventory holding cost to be the highest on OTCEI followed by Adverse Information cost and Order Processing cost. The result reflects market microstructure which is peculiar to OTCEI. The methodology developed in this study is basically a generalization of S toll's (1989) methodology. . Roll(1984) shows that in a pure order processing world, spread equals the square root of negative of serial covariances of successive differences of transaction prices. Stoll (1989) relates spread to the covariance of successive difference of transaction prices and that of the quotes. Stoll introduces two parameters, 5, which is a measure of magnitude of price change and JI, the probability of reversal of type of transaction, that is, from Bid to Ask or vice-versa, to model the Bid/Ask price movements from one transaction to the next. Thus Stoll, from this model, establishes a theoretical relationship between serial covariances of successive differences of transaction and quote returns and spread. 5 and n are estimated via regression of serial covariances of transaction and quote, returns on average proportional spread square. With these two parameters, Stoll finally decomposes spread into three components. δ, is the amount of price change between transactions for two reasons- Inventory holding reason and adverse information reason. Stoll explains these price changes due to two reasons with just one parameter, 5. This forms the main motivation of this study. In our study, we let 8 assume two different values, 5i and 82 which attempts to capture the price changes due to the two different causes viz inventory holding and adverse information. It is convenient to think of these two S's being associated with two different states of transactions. However, these states themselves are indeterminate . In other words, the price change could be due to inventory reasons, or due to trading with an informed trader, or due to both. Thus, while Stoll assumes only one 8, in our study, we have two different values of 8. Thus, with three parameters, 81, 82 and n, this study attempts to estimate the relevant parameters and realistically decompose the three components of spread in a dealer market. Just like Stoll, the developed theoretical model also relates serial covariances of transaction price changes and quoted price changes to spread square. However, unlike Stoll, now there are 3 parameters, namely, 5j, 82 and n. As it is impossible to solve three unknowns with just two equations, it becomes necessary to introduce one more equation relating the three parameters to the spread. It is here that we introduce, for the first time, the serial covariance of the second order differences of the transaction price changes, which is related to spread via an equation. Intuitively, we can explain this relationship using Roll's result. Roll(1984) has shown that spread equals square root of the negative serial covariances of transaction price changes in a pure order processing world. Since the second order difference is nothing but the rate of price changes, it also must be related to spread, since the price change themselves are related to it, empirically, we find that spread square significantly affects the serial covariance of second order difference of price changes as well. Besides explaining the price changes with just one 5, Stoll's method of decomposition is not realistic. Though his method of decomposition does yield three components of spread, in reality, it lumps Adverse information cost and Inventory holding cost together. In our study, we make use of the state-of-the art Huang and Stoll's (1997) methodology of decomposition of spread. We first embed the developed theoretical price-movement model into that of Huang and S toll's this yields a functional relationship between 5i and 52 and a and |3 of Huang and Stoll, which directly refers to the adverse information and inventory holding components respectively. Thus, in our study, we realistically decompose the components of spread and OTCEI and empirically too, we find that the components estimated from our methodology does reflect the market microstructure of OTCEI. Apart from developing and empirically testing the theoretical model, we also see if it fits the observed data on OTCEI. We find that the theoretical model does not exactly conform to the observed data in OTCEI, necessitating some empirical fine-tuning. We build an empirical model which is again used to get the three components of spread. We also estimate components of spread in OTCEI using Stoll's and Huang and Stoll's methodology and we compare them with the estimates obtained using our methodology. We find that Stoll's methodology overstates the Adverse information component of spread and understates the inventory holding component of spread and Huang and Stoll's methodology and Our methodology and model yields estimates of components of spread which is more in tune with the market micros tructure of OTCEI. The estimates obtained from empirical model too conforms to the market microstructure of OTCEI.

We study some aspects of liquidity of stocks traded through the National Stock Exchange (NSE) of India. Initially we examine the multi-dimensional nature of liquidity by conducting day-wise factor analysis of eleven liquidity proxies across a cross-section of stocks, using data from two periods reflecting different market conditions. Five factors emerge consistently, interpretable as depth, spread, volume, price elasticity and relative activity. Subsequently, we study execution of limit orders in the NSE from three angles. First we consider order execution probability, using 106 stock-specific logistic models. Important predictors of order execution probability are price premium followed by volatility, relative activity, bid ask spread and order imbalance. Some differences are noted when comparing companies of different sizes and between buy and sell orders. Second, we study order execution times using survival analysis. Several diagnostic tests indicate that parametric Accelerated Failure Time models using the log-logistic distribution for the survival time S(t) are suitable for current data. 100 stock-specific models are built; results are consistent with the logistic models. Additionally depth is also found to be important. Finally we build 4 combined models across stocks for both execution probabilities as well as times. These models perform well on out of sample data, suggesting their predictive utility.

We study some aspects of liquidity of stocks traded through the National Stock Exchange (NSE) of India. Initially we examine the multi-dimensional nature of liquidity by conducting day-wise factor analysis of eleven liquidity proxies across a cross-section of stocks, using data from two periods reflecting different market conditions. Five factors emerge consistently, interpretable as depth, spread, volume, price elasticity and relative activity. Subsequently, we study execution of limit orders in the NSE from three angles. First we consider order execution probability, using 106 stock-specific logistic models. Important predictors of order execution probability are price premium followed by volatility, relative activity, bid ask spread and order imbalance. Some differences are noted when comparing companies of different sizes and between buy and sell orders. Second, we study order execution times using survival analysis. Several diagnostic tests indicate that parametric Accelerated Failure Time models using the log-logistic distribution for the survival time S(t) are suitable for current data. 100 stock-specific models are built; results are consistent with the logistic models. Additionally depth is also found to be important. Finally we build 4 combined models across stocks for both execution probabilities as well as times. These models perform well on out of sample data, suggesting their predictive utility.

碩士
國立成功大學
國際管理碩士在職專班
96
In order to investigate directionality between stock market and foreign investment, a comprehensive study is applied including two facets of developing stock market visualized stock market growth and 30 day market volatility of Indian stock exchanges – Bombay Stock Exchange and National Stock Exchange. For foreign investment, three types of foreign investment – direct (FDI), indirect or institutional (FII) and depository receipt (such as ADR) are included in the study. Along with foreign investment variables, economic growth is also included. Through multiple regression, a strong link is obtained for investment and economic growth with the stock market growth. In terms of volatility, foreign speculators are found risk aversive. After obtaining strong relation, Granger causality is applied on individual relationship, followed by test of exogeneity. Through these two tests, it is proved that stock market growth is playing lead role with respect to economic growth as well as foreign investment; whereas 30 days volatility has no causal relationship either with foreign investment or with economic growth.

Financial Derivatives are one of the most popular and emerging innovations in the field of financial engineering. Since their inception, there has been a phenomenal growth in the volumes of derivatives traded all over the world. Financial markets are known to be extremely volatile and derivatives provide a way of eliminating or reducing the risks involved in these markets. Since these instruments derive their value from some underlying asset, trading in these instruments is bound to affect the underlying assets. Thus it becomes important to examine what these effects are and whether they have been favourable or detrimental to the underlying stock markets specially when there has been an explosive growth of these financial derivatives all over the world. This issue gains more importance in the case of emerging markets like India as they try to be more competitive and efficient as the developed Western markets. This thesis mainly deals with looking at this impact on the Indian stock markets. The Indian markets still being very new in this area, not many studies have been reported here related to this issue. The main focus of this thesis is to provide some more evidence on the impact of one kind of derivative instrument, namely options on different characteristics of underlying stocks in the Indian stock market. The thesis has the following objectives: • To examine the impact of option introduction on the price of underlying stocks in National Stock Exchange (NSE). • To examine the impact of option introduction on the volatility of underlying stocks in NSE • To examine the impact of option introduction on liquidity of underlying stocks in NSE NSE introduced derivatives beginning with index futures on June 12, 2000, followed by index options on June 4, 2001, options on individual securities on July 2, 2001 and finally futures on individual securities on November 9, 2001. Due to the temporal proximity of the introduction of index options and individual options, there exists a possibility of an interaction of these two effects. This problem is solved by a judiciously chosen sampling design. In particular, three groups of stocks are considered. The first group consists of stocks on which options were first introduced on 2nd July 2001 and thus would exhibit a combined effect of the two events if any. The second group consists of stocks on which options were introduced much later and therefore would show effects of individual option introduction if any. The third group comprises of nonoptioned stocks whose returns are considered around the date of index option introduction and thus would show effects of index option alone if any. To separate the two effects an ANOVA/ Logistic Regression model is used. An objective selection of the event and estimation windows is done using a Bayesian Change Point Analysis. The first part of the thesis looks at the effect of option introduction on the price of underlying stocks. A standard event study methodology as has been used in the literature is employed for this purpose. The study does not find any significant effect of option introduction on the prices. The second part of the thesis deals with the effect on volatility. Volatility is measured as the risk of a stock and as is done in the literature, three kinds of risk are looked at: total risk, systematic risk and the unsystematic risk. In case of the total risk, an F-test and an Ansari Bradley test is used to check for changes in the variance and scale parameters of market-adjusted continuously compounded returns of the stocks before and after option introduction. The results of these tests are recorded as a categorical variable taking on the value 0 for no change and 1 for a change and a Binomial Logistic Regression is used to separate the effects of the two events. Furthermore, after recording the results of the above mentioned tests as a categorical variable with three categories (0, 1, -1), a Multinomial Logistic Regression is also used in order to estimate the direction of the change (increase, decrease or no change). The ratios of after to before total risks are also analyzed using an ANOVA model. The systematic risk is measured using three kinds of betas – OLS betas, Scholes-Williams betas and Fowler-Rorke betas. The differences in the before and after betas of every stock are modelled using an ANOVA model in order to separate the two effects as well as the interaction effect. The unsystematic risk is estimated by the conditional variances and the unconditional variances of ARMA and ARMA-GARCH models fitted to market model excess returns. The ANOVA model is used here as well. In addition to this, the before and after ARCH and GARCH coefficients of GARCH (1, 1) models fitted to the excess returns are also compared using the ANOVA model. The results indicate that individual options are leading to a decline in total risk however index options are causing an increase in total risk. The interaction effect is significant in this case thereby causing an increase in total risk in the Group I stocks. The OLS betas indicate that individual option introduction seems to have increased the systematic risk. The Scholes-Williams betas indicate that index option introduction seems to have increased the systematic risk. The Fowler Rorke betas on the other hand, do not show any significant impact of individual option or index option introduction. For all the three betas index options introduction seems to have no effect on the systematic risk. Though the interaction effect seems to be significant in all the three cases, it however does not significantly affect the systematic risk in Group I stocks. As regards the unsystematic risk, both the conditional and unconditional variances of ARMA models show a significant reduction for individual option introduction but index options do not have any significant impact on either one of these measures. In case of unconditional variances of ARMA-GARCH models, none of the effects come out as significant. While comparing the news and persistence coefficients of GARCH (1, 1) models, the news coefficients indicate that the due to index option introduction, stocks are becoming more efficient in terms of absorbing the news more rapidly. No significant effect of either event is found on the persistence coefficients. The last part of the thesis deals with the liquidity issue. Liquidity has been measured using two measures – relative volume (based on daily data) and implicit bid-ask spread given by Roll (1984) (calculated from intra-day data). In case of the liquidity measures, the Logistic Regression models are used i.e. a categorical variable with two or three categories obtained from the results of a Wilcoxon Rank Sum test for comparing the median volume and spread before and after option introduction, is used. It is found that for the relative volume, individual option introduction has led to a favourable effect in terms of increasing the volume post introduction of options; however index options seem to have had a negative effect. As for the spread, index options seem to have had a stabilizing influence on the underlying stocks than the individual options.

Financial Derivatives are one of the most popular and emerging innovations in the field of financial engineering. Since their inception, there has been a phenomenal growth in the volumes of derivatives traded all over the world. Financial markets are known to be extremely volatile and derivatives provide a way of eliminating or reducing the risks involved in these markets. Since these instruments derive their value from some underlying asset, trading in these instruments is bound to affect the underlying assets. Thus it becomes important to examine what these effects are and whether they have been favourable or detrimental to the underlying stock markets specially when there has been an explosive growth of these financial derivatives all over the world. This issue gains more importance in the case of emerging markets like India as they try to be more competitive and efficient as the developed Western markets. This thesis mainly deals with looking at this impact on the Indian stock markets. The Indian markets still being very new in this area, not many studies have been reported here related to this issue. The main focus of this thesis is to provide some more evidence on the impact of one kind of derivative instrument, namely options on different characteristics of underlying stocks in the Indian stock market. The thesis has the following objectives: • To examine the impact of option introduction on the price of underlying stocks in National Stock Exchange (NSE). • To examine the impact of option introduction on the volatility of underlying stocks in NSE • To examine the impact of option introduction on liquidity of underlying stocks in NSE NSE introduced derivatives beginning with index futures on June 12, 2000, followed by index options on June 4, 2001, options on individual securities on July 2, 2001 and finally futures on individual securities on November 9, 2001. Due to the temporal proximity of the introduction of index options and individual options, there exists a possibility of an interaction of these two effects. This problem is solved by a judiciously chosen sampling design. In particular, three groups of stocks are considered. The first group consists of stocks on which options were first introduced on 2nd July 2001 and thus would exhibit a combined effect of the two events if any. The second group consists of stocks on which options were introduced much later and therefore would show effects of individual option introduction if any. The third group comprises of nonoptioned stocks whose returns are considered around the date of index option introduction and thus would show effects of index option alone if any. To separate the two effects an ANOVA/ Logistic Regression model is used. An objective selection of the event and estimation windows is done using a Bayesian Change Point Analysis. The first part of the thesis looks at the effect of option introduction on the price of underlying stocks. A standard event study methodology as has been used in the literature is employed for this purpose. The study does not find any significant effect of option introduction on the prices. The second part of the thesis deals with the effect on volatility. Volatility is measured as the risk of a stock and as is done in the literature, three kinds of risk are looked at: total risk, systematic risk and the unsystematic risk. In case of the total risk, an F-test and an Ansari Bradley test is used to check for changes in the variance and scale parameters of market-adjusted continuously compounded returns of the stocks before and after option introduction. The results of these tests are recorded as a categorical variable taking on the value 0 for no change and 1 for a change and a Binomial Logistic Regression is used to separate the effects of the two events. Furthermore, after recording the results of the above mentioned tests as a categorical variable with three categories (0, 1, -1), a Multinomial Logistic Regression is also used in order to estimate the direction of the change (increase, decrease or no change). The ratios of after to before total risks are also analyzed using an ANOVA model. The systematic risk is measured using three kinds of betas – OLS betas, Scholes-Williams betas and Fowler-Rorke betas. The differences in the before and after betas of every stock are modelled using an ANOVA model in order to separate the two effects as well as the interaction effect. The unsystematic risk is estimated by the conditional variances and the unconditional variances of ARMA and ARMA-GARCH models fitted to market model excess returns. The ANOVA model is used here as well. In addition to this, the before and after ARCH and GARCH coefficients of GARCH (1, 1) models fitted to the excess returns are also compared using the ANOVA model. The results indicate that individual options are leading to a decline in total risk however index options are causing an increase in total risk. The interaction effect is significant in this case thereby causing an increase in total risk in the Group I stocks. The OLS betas indicate that individual option introduction seems to have increased the systematic risk. The Scholes-Williams betas indicate that index option introduction seems to have increased the systematic risk. The Fowler Rorke betas on the other hand, do not show any significant impact of individual option or index option introduction. For all the three betas index options introduction seems to have no effect on the systematic risk. Though the interaction effect seems to be significant in all the three cases, it however does not significantly affect the systematic risk in Group I stocks. As regards the unsystematic risk, both the conditional and unconditional variances of ARMA models show a significant reduction for individual option introduction but index options do not have any significant impact on either one of these measures. In case of unconditional variances of ARMA-GARCH models, none of the effects come out as significant. While comparing the news and persistence coefficients of GARCH (1, 1) models, the news coefficients indicate that the due to index option introduction, stocks are becoming more efficient in terms of absorbing the news more rapidly. No significant effect of either event is found on the persistence coefficients. The last part of the thesis deals with the liquidity issue. Liquidity has been measured using two measures – relative volume (based on daily data) and implicit bid-ask spread given by Roll (1984) (calculated from intra-day data). In case of the liquidity measures, the Logistic Regression models are used i.e. a categorical variable with two or three categories obtained from the results of a Wilcoxon Rank Sum test for comparing the median volume and spread before and after option introduction, is used. It is found that for the relative volume, individual option introduction has led to a favourable effect in terms of increasing the volume post introduction of options; however index options seem to have had a negative effect. As for the spread, index options seem to have had a stabilizing influence on the underlying stocks than the individual options.

碩士
國立高雄應用科技大學
金融系金融資訊碩士班
102
The aim of this paper is to discuss the integration of stock markets among India and some important East Asian countries, Japan, China, Singapore, and HongKong, using tests allowing for endogenously determined breaks in cointegrating relationships and recursive cointegration analysis. The empirical evidence show: First, according to the empirical results of Gregory and Hansen(1996), it is inconsistent whether there is a cointegration among all variables being based on stock price with local currency, while there is a cointegration among all variables being based on stock price with U.S. dollars. Besides, the empirical evidence show that there are three timings with the structural breaks, around Oct. in 2003, Sep. to Nov. in 2005, and from 2008 to 2009. Second, the results of recursive cointegration analysis confirms exiting of convergence among stock markets of India and other six economics. Which implies integration is existing among stock markets of India and other six economies in the long run.

During the 1990s, the world economy had witnessed many ups and downs of capital inflows and outflows due to financial crisis and economic turmoil. The rising international capital flows were very attractive from the year 2000, until the global financial crisis 2008. The changing pattern of capital flows does not depend only on external but also internal country characteristics and fundamentals. Since the global financial crisis, wide-ranging cross-border capital flows into G20 nations, including inflows from both G20 countries and non-G20 countries. But they have partly returned to pre-crisis of high tide-lines. They are main below the average level, on a percentage to GDP basis, for the G20, over the past decade. This is nothing but the dilemma of risk. So, the investors’ always treat the U.S and some developed market as a safe bucket for investment. Due to lack of understanding regarding emerging markets opportunities or inadequate ability to efficient investment, it is a greater task to quantify the share of both developed and developing countries out of G20 countries. International capital flows have remained a controversy and puzzle among the existing variety of flows. Both theoretical and the empirical literature on international capital flows have been a topic of argument among the researchers and policy makers. After the liberalization episode, international portfolio capital flows were introduced in the Indian financial market. The existing literature gives a mixed result for international capital flows and its impact on financial market development including macroeconomic situation. In the recent scenario, international capital flows pass through different phases due to financial crisis and many ups and downs in the world economy. It is very important to study the liquidity situation of financial market, international capital flows into G20 countries and its contagious interaction between liquidity, efficiency and returns across the global financial market. Existing literature discusses the total flow from U.S to G20 countries including India, but very few studies focus on gross flows, net flows from U.S to India and its impact on liquidity and returns on Indian stock market. However, U.S is treated as a dominating country due to its monopoly policies and regulation towards the global financial market integration. But the question arises, how far U.S policy affects emerging country’s financial markets like India? This gives space for a study. The Impact of U.S policies on global financial market efficiency is also a threat in the present situation. The existing economic theory talks about the Push and Pull theory in an economy. The previous studies specifically emphasize on the impact of different types of flows on financial market efficiency and returns. But the relation between push approach and pull approach and its role in financial market efficiency and returns are missing in prior studies. The assessment of capital flows to exchange rate and current account performance is rarely studied in the context of global monetary policies. The present study uses variety of econometric tools for the empirical analysis. For the first objective, Pedroni and Kao’s cointegration test are used to identify the existing cointegrating vector among variables. Fully Modified Ordinary Least Square Method (FMOLS) and Dynamic Ordinary Least Square (DOLS) were used to find out the elasticity estimation of the variables. To find out the cross-country specification result, ARDL/PMG model is used. For second objective, a Vector Error Correction Model (VECM) has been x chosen for this study as it allows identification of long and short term relationships between variables. In estimating the cointegration, first we have checked whether each of the series is integrated of the same order. Integration of a time series can be confirmed by the standard Augmented Dickey-Fuller test and Phillips-Perrons unit root tests. The number of cointegration ranks ‘r’ is tested with the maximum eigen value and trace test. The maximum eigen value statistics tests the null hypothesis that there are ‘r’ co-integrating vectors against the alternative of ‘r+1’ co-integrating vectors. The trace statistics tests the null hypothesis of no co-integrating vector against the alternative of at least one co-integrating vector. The asymptotic critical values are given in Johansen (1991) and MacKinnon et al. (1999). For third objective, Vector Autoregressive (VAR) method, impulse response function and variance decomposition technique are employed to examine the short-term dynamics and casual relationship between variables. Before estimating the VAR model, the unit root test was used to examine the stationary properties of the variables. In this study two unit roottests, viz. Augmented Dickey Fuller (ADF) tests and Phillip Perron’s (PP) test have been conducted to examine the stationarity properties of the variables. Finally, for the fourth objective, again VECM framework is used to analyze the relationship between the log of stock prices and the log of output. From all the above analysis, it is very clear that foreign investors tend to channelize rather than dry-out liquidity from domestic market. Hence, our analysis finds little support from the correlation that, at the time of adverse environment, foreign investors can destabilize the domestic market. As the cross-country specification result indicates, India, one of the emerging countries among the total 18 high market capitalization countries, is having positive causality from flows to both domestic market liquidity and returns with significant coefficient. Also we establish that domestic market efficiency is having long-run association between foreign capital flows from U.S to India. Both the variables, foreign net flows and the “liftoff” episode (quantitative easing episode), have direct influence on Indian domestic financial market. The volatility pattern of U.S Fed rate and foreign capital flows interaction with domestic financial market, presents statistically significant result with netflows but not with Fed rate. Our results significantly reciprocate the present scenario of tremendous increase in capital out flows due to taper talk and QE phase 4. So the empirical result signifies that in Indian capital market both pull factor and push factor works for capital flight. But real financial situation statistically justifies that “push factor approach” (declaration phase of U.S fed rate) has greater impact than “pull factor approach” (REER, Inflation). We can conclude that outflows don’t cause depreciation of exchange rate. It implies that capital flows to a country does not enhance the capital account to full extent; rather it helps to maintain the reserve. This study has not found any huge contribution of foreign capital flows to output growth (IIP) but the contribution is positive so far as the fills up of the gap between savings and investment is concerned.