Stock Market Volatility

This study examines impact of the introduction of single stock futures contracts on the return volatility of the SSFs-listed underlying stocks. The study documents a significant decrease in return volatility for the SSFs-underlying stocks following the introduction of single stock futures contracts on the Karachi Stock Exchange. The multivariate analysis in which the spot trading volume, the futures trading volume and open interest were partitioned into news and informationless components, the estimated coefficient of expected futures volume component is statistically significant and negatively related to volatility, suggesting that equity volatility is mitigated when the expected level of futures activity is high. The findings of the decreased spot price volatility of the SSFs-underlying stocks associated with large expected futures activity is important to the debate of regarding the role of equity derivatives trading in stock market volatility. These empirical results for the Pak...

This paper examines the impact of political uncertainty (caused by the civil uprisings in the Arab World i.e., "Arab Spring") on the volatility of major stock markets in the MENA region. Our main findings are as follows. First, by distinguishing between conventional and Islamic stock market indices, we find that these two groups of investments react heterogeneously to the recent political turmoil. Specifically, we document a significant increase in the volatility of Islamic indices during the period of political unrests whereas the uprisings have had little or no significant effect on the volatility in conventional markets. Such difference is confirmed by further analysis in a multivariate GARCH model. Second, regardless of its impact on volatility, there is little evidence to suggest that MENA markets have become more integrated with international markets after the political revolution. Third, similar results are not found for the benchmark indices which indicate that the changes are the result of political tensions. In general, these results are robust to model specification and consistent with the notion that political uncertainty contributes to financial volatility. Overall, the findings are important in understanding the role of political uncertainty on stock market stability and are of great significance to investors and market regulators.

Small and Medium Enterprises (SMEs) drive India's economy, contributing one-third of its GDP. In 2023 alone, SMEs raised over ₹5,500 crore through more than 120 IPOs, fueling growth and competitiveness. Despite extensive research on SME IPO performance, ownership structures, and information asymmetry, researchers have largely overlooked the factors influencing retail subscription in SME IPOs, leaving a critical research gap. This study analyzes the factors that influence retail subscriptions in SME IPOs in India, focusing on issue attributes, subscription rates, and external influences. An analysis was conducted on 506 SME IPOs listed between 2021 and 2024. JMP software was used to evaluate the effects of issue attributes, QIB and NII subscription patterns, and external factors on retail subscription rates. The findings indicate that issue price and issue size negatively impact retail subscriptions, while GMP, NII subscriptions, and QIB subscriptions positively influence them. The model explains 66% of the variance in retail subscriptions, highlighting the importance of market sentiment and institutional participation. The study expands the literature by incorporating a wider range of factors to provide a more comprehensive understanding of SME IPO subscriptions. It emphasizes the need for strategies to attract retail investors by enhancing transparency, improving pricing approaches, and mitigating risks, offering valuable insights for policymakers and market participants.

This research aims to analyze the impact of oil price shocks on stock market indices using the VECM. The data spans from January 1, 2000, to December 31, 2023, capturing both short-term dynamics and long-term equilibrium relationships. Key findings indicate that oil price shocks significantly influence stock market indices, with varying impacts across different regions. For instance, Japan and Vietnam exhibit stronger negative effects compared to other regions. The results also reveal differences in the speed of adjustment towards long-term equilibrium, highlighting varying levels of market efficiency. The Johansen cointegration test results reveal significant long-term equilibrium relationships between oil prices and stock market indices, underscoring the interconnected nature of these variables. The study concludes that oil prices are a critical factor in stock market performance, underscoring the need for informed strategies by investors, corporate managers, and government agencies to mitigate risks and capitalize on opportunities. These insights are crucial for understanding the interconnected nature of global financial markets and developing effective risk management strategies.

This study investigates the relationship between economic growth and energy consumptionboth renewable and non-renewable-in European Union countries during the green transition. Using a panel dataset of 28 EU countries from 1995 to 2021, we employ econometric techniques-including the Westerlund cointegration test and a fixed-effect panel threshold model-to assess long-term equilibrium relationships. The results indicate that while both renewable and non-renewable energy consumption are associated with economic growth, their roles differ. Renewable energy consumption shows a positive but less robust relationship with economic growth. In contrast, non-renewable energy consumption demonstrates a more robust bidirectional causality with economic growth, indicating a more intertwined relationship with economic growth during the study period. Interestingly, in countries with high levels of non-renewable energy consumption-classified as regime 2 in the panel threshold model-increased non-renewable energy consumption is associated with a decrease in economic activity. Our results have significant policy recommendations, indicating that promoting renewable energy sources does not hinder economic growth. Moreover, such promotion has the potential to contribute substantially to economic growth in the future. Therefore, in addition to other crucial benefits, such as increased energy security, the development of renewable energy sources does not threaten the economy. This is particularly relevant as many EU countries, including Poland, Romania, Hungary, Bulgaria, Slovakia, and Lithuania, still have underdeveloped renewable energy sectors.

The paper examined the relationship between oil price shocks and stock market volatilities in Nigeria, Egypt, South Africa, Kenya, Ivory Coast, and Ghana using a structural Vector Autoregressive model. The data used for the study spanned from January 2000 to December 2019. Findings from the study showed homogeneity in the response of stock market volatility to oil shocks for both oil importing and oil exporting countries, with slight variances in the timing of pass-through and speed of adjustment. Supply shocks had no significant impact on stock market volatility in all countries considered. In making stock market-related decisions, investors and even policy makers should consider the source and pass-through mechanism of oil price shock in their specific countries.

We present a theory of excess stock market volatility, in which market movements are due to trades by very large institutional investors in relatively illiquid markets. Such trades generate significant spikes in returns and volume, even in the absence of important news about fundamentals. We derive the optimal trading behavior of these investors, which allows us to provide a unified explanation for apparently disconnected empirical regularities in returns, trading volume and investor size.

The Nifty 100 Index, part of the National Stock Exchange of India (NSE), represents the top 100 large capitalization companies listed on the exchange. It includes the Nifty 50 stocks and another 50 large cap stocks, covering various sectors of the Indian economy. The index aims to provide investors with a benchmark that measures the market performance within the economic environment in India and the investment opportunities that exists. The 2024 Lok Sabha election results created significant turbulence in the Indian stock market, deviating sharply from exit poll predictions of a landslide victory for the NDA. However, preliminary trends suggested a very stiff competition between the NDA and the I. N. D. I. A. alliance. The study seeks to investigate the impact of the election outcomes on the performance of the Nifty 100 index in the period of the election. This paper helps to fill the existing gap in the literature regarding the relationship between political events and stock market activity in India, and the implications of electoral uncertainty for stock market performance. The findings of the study reveal enhanced volatility and significant fluctuations in the Nifty 100 index both before and after the election, enhancing the market sensitivity to political developments. The study contributes to the understanding of the interplay between political events and stock market dynamics in India, offering insights for investors and policymakers regarding market behaviour in response to electoral uncertainty. The study highlights the necessity for strategic planning and risk management for market participants in politically volatile periods.

The problem in this study is whether the Indonesian Capital Market is coupled with the ASEAN Capital Market and East Asia Capital Market. And the interest rate of the US Federal Reserve (Fed Rate) and Central Bank of Indonesia's Interest Rates (BI Rate) against the decision of domestic investors and foreign sellingbuying of shares. The research model using EViews-9 for stationary testing (ADF-Test), cointegration (Johansen-Test), and t-test and f-test automatically calculated by EViews-9. The study found their cointegration between Capital Markets in ASEAN (JCI, STI, KLSE, SET, PSE), cointegration in Capital Markets in EAST ASIA (NIKKEI, HSI, KOSPI, SHCOMP, TWSE), and cointegration Interest Rates (The Federal Fund Rate, Bank Indonesia Rate). The results of this study also showed that domestic investors in the Central Bank of Indonesia (BI Rate) are deciding to buy shares with the movement. Meanwhile, local investors in the decision to sell stocks saw the index movement of the KLSE and STI index (ASEAN Capital Markets), as well as the TWSE index (East ASIA Capital Market). Foreign Investors buy stocks to decide on the movement of the US Federal Reserve (the Fed) and the Bank Indonesia interest rate (BI Rate).

Quantile regression provides a method of estimating quantiles from a conditional distribution density. It is achieves this by minimizing asymmetrically weighted sum of absolute errors thus partitioning the conditional distribution into quantiles. Lower conditional quantiles are of interest in estimation of Value-at-Risk because they indicate downward movement of financial returns. Current risk measurement methods do not effectively estimate the VaR since they make assumptions in the distribution tails. Financial data is sampled frequently leading to a heavier tailed distribution compared to a normal and student t distribution. A remedy to this is to use a method that does not make assumptions in the tail distribution of financial returns. Little research has been done on the usage of quantile regression in the estimation of portfolio risk in the Nairobi Securities Exchange. The main aim of this study was to model the portfolio risk as a lower conditional quantile, compare the performance of this model to the existing risk measurement methods and to predict the Value-at-Risk. This study presents summary of key findings and conclusion drawn from the study. From the fitted conditional quantile GARCH model 62.4% of VaR can be explained by past standard deviation and absolute residual of NSE 20 share index optimal portfolio returns. The fitted model had less proportion of failure of 7.65% compared to commonly used VaR models.

PurposeThe purpose of the study is to examine the dynamics in the troika of asset pricing, volatility, and the business cycle in the US and Japan.Design/methodology/approachThe study uses a six-factor asset pricing model to derive the realized volatility measure for the GARCH-type models.FindingsThe comprehensive empirical investigation led to the following conclusion. First, the results infer that the market portfolio and human capital are the primary discounting factors in asset return predictability during various phases of the subprime crisis phenomenon for the US and Japan. Second, the empirical estimates neither show any significant impact of past conditional volatility on the current conditional volatility nor any significant effect of subprime crisis episodes on the current conditional volatility in the US and Japan. Third, there is no asymmetric volatility effect during the subprime crisis phenomenon in the US and Japan except the asymmetric volatility effect during the pos...

L'analisi mira ad analizzare e prevedere l'andamento del prezzo del rame, con particolare riferimento ai futures quotati in dollari statunitensi per libbra, attraverso l’applicazione di tecniche di analisi statistica classica e moderna relative alla serie storica. Il rame rappresenta una delle materie prime più rilevanti per l'economia globale, grazie al suo ampio impiego industriale – in settori come l’edilizia, gli impianti elettrici e i trasporti - e al ruolo sempre più centrale nella transizione energetica, in particolare nella produzione di veicoli elettrici e nelle infrastrutture per le energie rinnovabili.
Nel contesto geopolitico ed economico attuale, il prezzo del rame risente di una molteplicità di fattori: dalle tensioni commerciali tra Stati Uniti e Cina all’evoluzione della domanda nei mercati emergenti, dalle politiche monetarie delle principali banche centrali fino a variabili esogene come scioperi nei paesi produttori e cambiamenti climatici. Queste dinamiche generano una struttura temporale complessa del prezzo, rendendolo un oggetto d’indagine particolarmente adatto all’analisi quantitativa e alla modellizzazione previsionale.

Industrialization being an engine of growth in modern economies and the primary roles energy plays in driving industrialization, stability of oil prices has become a crucial factor with spatial and temporally implications for the performance of the Nigerian industrial subsector. In this regard, this study examined the symmetric and asymmetric effects (if any) of the oil price dynamic on the Nigerian industrial subsector using ARDL and NARDL frame works based on quarterly time series data spanning 1970Q 1 to 2018Q 4. The result of the short-run linear ARDL model reveals that oil price stimulates marginally the performance of the building and construction industry as well as aggregated industrial output but unfortunately dampened the performance of the manufacturing subsector. Similar to the short-run result, the long-run non-linear shows that oil price shocks (increase and decrease) have mixed and variegated effects on the industrial subsector and its constituents as confirmed by the probability values of the Wald"s test. The results show that increase in the price of oil dampened aggregate industrial output and manufacturing index with marginal increase in building and construction output. Conversely, a decrease in oil price stimulates industrial manufacturing, building and construction indexes in the long-run though short-lived compare to the negative effects that propagate for a longer period. In light of the empirical findings and the asymmetric nature of oil price shocks on Nigerian industrial subsector, Nigerian government and the industrialists should formulate appropriate trade policy and develop sound industrial policy management mechanism to effectively mitigate the negative effects of oil price shocks on Nigerian industrial subsector. These include inward looking industrial policy, establishment of light industries, promotion of small and medium scales enterprise, Small and Medium Industry Equity Investment Scheme (SMIEIS), National Integrated Industrial Development (NIID), Industrial Park Development strategy (IPDs) and proper funding of Bank of Industry (BOI) during period of oil boom and consequently smoothen Nigerian industrial performance at all times irrespective of the magnitude of oil price variation.

Unprecedented difficulties brought on by the COVID-19 pandemic have accelerated the pace of digital disruption in a number of industries, requiring organizations to swiftly adjust and come up with fresh, creative solutions. This study looks at how resilience-building in post-pandemic businesses is impacted by organizational readiness for digital transformation, leadership adaptability, and strategic agility. 150 respondents from a variety of industries participated in a quantitative survey, and data was analyzed using multiple regression, partial least squares structural equation modeling (PLS-SEM), correlation analysis, and reliability tests. Research shows that organizational resilience is significantly and favorably impacted by strategic agility, leadership adaptability, and digital readiness. This highlights the critical role that flexible strategies and proactive leadership play in preserving competitive advantage in the face of ongoing digital disruption.

Stock market efficiency is an important concept, especially in a growing economy like the Kenyan one. This study empirically determines the form of market efficiency, time-varying volatility effect, and asymmetric or leverage effect of the Nairobi Stock Exchange (NSE) market. Secondary data at the daily NSE 20-share index for the period spanning from January 2001 to December 2010 was used. The market efficiency was determined basing on unit root tests, ADF test, PP test and the non-parametric Runs test. The results indicated that stock returns follow an ARMA (2,1) stochastic process with significant positive serial correlation. The ADF test and PP test clearly gave evidence that the NSE index were non-stationary (random) at level and stationary (non-random) for the first and second differences. This implies that the NSE market is informationally efficient at the weak-form level. Using the non-parametric run test, results clearly displayed NSE market is weak-form efficient. EGARCH and TGARCH model with student's t-distribution were the best models to capture asymmetric effect of stock returns. Since (γ1) was positive, it indicated significance evidence for asymmetry in stock returns thus presence of leverage effect (good news had a higher impact on volatility than bad news).

This paper presents a quantitative analysis of how tariff policy shocks influence financial markets, with a particular focus on stock market responses. Using a multivariate Ordinary Least Squares (OLS) regression model, we examine the impact of variables such as tariff magnitude, country scope, macroeconomic indicators, and market volatility (VIX) on short-term equity returns. Motivated by the recent 2025 market collapse following President Trump's tariff announcement, we simulate scenarios of varying economic intensity to forecast investor reactions. Model validation demonstrates strong predictive accuracy and diagnostic reliability. The results provide actionable insights for policymakers, investors, and risk managers, offering a framework for forecasting and mitigating the financial repercussions of trade protectionism. By incorporating sensitivity analyses, strategic forecasting, and policy recommendations, this research contributes to the development of evidence-based economic governance in an increasingly volatile global economy.

This chapter aims to explore the different facets of the collective memory of the 1987 crash in the US, which represented an unprecedented collapse of prices on the global stock markets. The 22.3% fall of the Dow Jones on Black Monday (October 19 th 1987) represents the biggest single-day stock market collapse in history-even greater than that of October 24 th 1929. Over 500 billion US dollars in share value was wiped out. The crash spread to other major financial markets over the world, but was quickly resolved thanks to the central banks' intervention on the capital markets.

This article investigates the key determinants of corporate bond performance in Ghana, distilling a comprehensive thesis into a concise analysis. Using an explanatory research design with panel data from 12 listed firms (2015-2019), the work examines how macroeconomic conditions and firm-specific factors influence corporate bond outcomes. Regression and cointegration techniques reveal that inflation rate, interest rate, exchange rate, government expenditure, firm size, corporate tax rate, and total debt levels all significantly shape corporate bond performance. Notably, stable inflation and interest rates, a favorable tax environment, and larger firm size are associated with improved corporate bond metrics, explaining about 88% of the variation in performance. These findings underscore the importance of macroeconomic stability and prudent corporate financial management in developing Ghana's corporate bond market. The article offers fresh, policy-relevant insights: regulators should ensure a stable economic environment (particularly price and exchange-rate stability) and strengthen market infrastructure, while firms and investors can leverage these drivers for better financing outcomes. We conclude with recommendations for policymakers, market participants, and academics to foster a more vibrant corporate bond market in Ghana.

The efficiency in emerging markets is becoming more important as the trend of investment in these markets is accelerating nowadays. The level of market efficiency influences an investor's investment strategy because of its improper valuation. The improper valuation of securities may lead to abnormal gains for the investor. On the other hand, abnormal gain in a perfectly efficient market is impossible as the share price absorbs all the information available in the public domain. However, efficiency in its finest sense is a distant reality. Many researchers have pointed out different levels of efficiencies, including the presence of calendar anomalies. The purpose of the study is to capture the effect of the day of the week on the stock return and volatility in India. Daily time series data of Sensex and Nifty were collected from the two prominent exchanges of India, i.e. Bombay Stock Exchange and National Stock Exchange websites, for a period of four years. After validating stationarity with ADF and Phillip-Perron tests, the study used GARCH, EGARCH and TGARCH models to capture day impacts on stock return and volatility. The study reveals that the Tuesday effect persisted in the Indian stock market during the study period. Moreover, the TGARCH model was found to be the most suitable model for describing volatility behaviour in the Indian stock market. As other days in the week were less significant, the study's findings suggest that the gradual disappearance of day affects the Indian capital market. Another study finding is the significance of the leverage effect, which implies the increased role of bad news over good news while capturing the day-of-the-week effect. Considering the above facts, the present study's findings contribute to a deeper understanding of stock market dynamics and the challenge of the day-of-the-week effect on market efficiency.

This research meticulously analyses the performance dynamics of two paramount cryptocurrencies, Bitcoin and Ethereum, over 2,682 observations. Preliminary findings indicate a near alignment in the mean returns of both assets, with Ethereum marginally outperforming Bitcoin. Interestingly, Ethereum’s superior returns are accompanied by heightened volatility, underlined by its more significant standard deviation. Both cryptocurrencies manifest negative skewness, hinting at a proclivity for negative returns, with Bitcoin showing a sharper skew. Their pronounced kurtosis values attest to the potential for extreme price swings. Regarding forecasting efficacy, the Weighted Moving Average (WMA) method emerges as superior for both assets, yielding the most accurate predictions. At the same time, the Exponential Moving Average (EMA) demonstrates the highest forecast errors. Further, the Relative Strength Index (RSI) evaluation suggests Ethereum may be oversold, alluding to potential investment opportunities. In contrast, Bitcoin, with its mid-range RSI, resides in a neutral zone devoid of clear market signals. The findings shed light on the nuanced performance and forecasting landscape of these leading cryptocurrencies, offering pivotal insights for potential investors.

This study investigates the volatility and return characteristics of the Nifty 50 Index over the five-year period from April 01, 2019 to March 31, 2024. It also studies the market pre-COVID and post-COVID using the daily high and low prices of the two years from 2019-2021. The main objective is to study the performance and growth of Nifty 50 over the last five years in India. Utilizing daily closing prices sourced from NSE website we calculate the average daily return, standard deviation of daily returns and their annualized equivalents to provide a comprehensive analysis of the index's performance and risk profile. The findings reveal an average daily return of 0.053% indicating a steady growth in the index while the standard deviation of daily returns at 1.21% reflects the short-term volatility experienced by investors. When annualized, the average return of 14.26% and median return of 11.82% underscores strong market performance suggesting healthy growth in the Indian stock market over the analysed period. The annualized standard deviation of 19.03% highlights the associated risk reflecting significant deviation from the annual returns. This study provides valuable insights for investors, portfolio managers, and policymakers seeking to understand the dynamics, risk and return of the Nifty 50 Index.

This paper provides a unique approach with AI algorithms to predict emerging stock markets volatility. Traditionally, stock volatility is derived from historical volatility, Monte Carlo simulation and implied volatility as well. In this paper, the writer designs a consolidated model with back-propagation neural network and genetic algorithm to predict future volatility of emerging stock markets and found that the results are quite accurate with low errors.

Using 11 OECD countries data, this study employs a Markov Switching unit root regression to investigate the issue of the non-stationarity and non-linearity of stock prices. The results convincingly support the view that the stock prices in the OECD countries are characterized by a two-regime Markov Switching unit root process. For Australia,

Recently, the Johannesburg Stock Exchange (JSE) launched a new range of exotic products, namely the Can-Do. The main idea behind the Can-Do products is to help financial institutions list some of their exotic options on the exchange. Cando’s are market to model. These products are all modelled with the celebrated Black-Scholes (Black & Scholes, 1973) partial differential equation (PDE) but with exotic features. Due to the inherent complexity of these features in the modeling equations, one can rarely find closed-form analytical solutions to these options. Therefore one has to resort to numerical methods. In the literature, four main families of methods where developed and are used extensively to solve option pricing problems. These include lattice methods (Breen, 1991), (Cox, et al., 1979), Monte Carlo simulations (Barraquand & Martineau, 1995), (Broadie & Glasserman, 1997), finite difference (FD) methods (Brennan & Schwartz, 1977), (Brennan & Schwartz, 1978) and analytical approximations (Carr & Faguet, 1994), (Geske & Johnson, 1984).

The use of finite difference methods for solving PDEs on a computer goes back almost to the 1950s since its invention. In finance, these methods were introduced in the 1970’s after the derivation of the Black-Scholes model. The sophistication of these methods in finance has become a field of extensive research. These include alternating finite difference (ADI) methods (Hout & Foulon, 2010), adaptive grids (Persson & Sydow, 2010), grids stretching (Oosterlee et al., 2005) and compact finite differences (Tangman et al., 2008), to name but a few. The interested reader is referred to two important textbooks (Daniel J, 2006), (Tavella & Randall, 2000) in the literature that cover financial derivatives pricing with finite difference methods exclusively.

This document provides a base for the valuation methods used in the Can-Do product space. Thus, we will not re-document already well-documented models and methodologies. This document will rather focus on the application of these methods to the JSE Can-Do products.

This study aims to examine the weak form efficiency of Dhaka Stock Exchange (DSE) using random walk model of EMH based on daily return series. The study applies both non-parametric [Kolmogorov-Smirnov test with Lilliefors coefficient, run test] and parametric test [autocorrelation test, unit root test and variance ratio test] on DSE general index, DSE broad index and DSE30 index ranging from June 1, 2004, to March 18, 2018. The results of the tests reject the null hypothesis of randomness. On the other hand, the result of the run test shows that share prices of DSE30 index follow the random walk. The return series of DSE broad index show some signs in favour of randomness by autocorrelation test while the returns of DSE general index support the efficiency concerning variance ratio test under both homoscedastic and heteroskedastic assumptions. The overall results of the study show inefficiency of DSE in the weak form which means the investor has a chance to make an abnormal profit p...

This paper analyzes the effect of international financial integration on the equity risk premium using a panel dataset of sixty emerging and developing countries over the period 2000-2010. We also use equity risk premium determinants as independent variables in the model. The results show that there is a negative relationship between international financial integration and equity risk premium. We add the interaction term between international financial integration and crisis as an independent variable in the model. We find out that our results are not confirmed in crisis situation.

This paper analyzes the effect of international financial integration on the equity risk premium using a panel dataset of sixty emerging and developing countries over the period 2000-2010. We also use equity risk premium determinants as independent variables in the model. The results show that there is a negative relationship between international financial integration and equity risk premium. We add the interaction term between international financial integration and crisis as an independent variable in the model. We find out that our results are not confirmed in crisis situation.

This study seeks to apply the generalized autoregressive conditional heteroskedasticity (GARCH) model to assess the impact of inflation on stock market returns and volatility using monthly time series data from two West African countries, that is, Nigeria and Ghana. In addition, the impact of asymmetric shocks was investigated using the quadratic GARCH model developed by Sentana (1995), in both countries. Results for Nigeria show weak support for the hypothesis which states that bad news exert more adverse effect on stock market volatility than good news of the same magnitude; while a strong opposite case holds for Ghana. Furthermore, inflation rate and its three month average were found to have significant effect on stock market volatility in the two countries. Measures employed towards restraining inflation in the two countries, therefore, would certainly reduce stock market volatility, improve stock market returns and boost investor confidence.

In this paper, we compare the statistical properties of some of the most popular GARCH models with leverage e¤ect when their parameters satisfy the positivity, stationarity and nite fourth order moment restrictions. We show that the EGARCH speci cation is the most exible while the GJR model may have important limitations when restricted to have nite kurtosis. On the other hand, we show empirically that the conditional standard deviations estimated by the TGARCH and EGARCH models are almost identical and very similar to those estimated by the APARCH model. However, the estimates of the QGARCH and GJR models di¤er among them and with respect to the other three speci cations.

The paper attempts to answer an interesting question of whether green and socially responsible companies are well rewarded in terms of better price discovery and higher returns in the Indian stock market during the COVID’19 pandemic. The paper makes use of a semi-log regression equation, Paired Samples t-test and various risk-adjusted techniques of performance evaluation to achieve the objectives of the study. We find that the impact of COVID’19 was positive and significant for green and socially responsible companies. Further, these companies outperformed
their counterparts by showing better price discovery, significantly higher returns and risk premiums using various risk-adjusted measures. Our findings have paved the way for green investing or socially responsible investing in India and are relevant for banks, mutual fund or portfolio managers, policymakers and investors. Offering green or socially conscious products like Green Fixed Deposit Options, Clean Technology Ventures, etc., should become a priority for banks and mutual funds. Regulators and policymakers should ensure that environmentally friendly and socially conscious businesses receive enough funding for the
best possible use while causing the least amount of environmental damage. Since the securities of these companies are performing well during the pandemic period, there is a distinct possibility that investors are putting more faith in these companies.

Stock exchange is one of the foremost versatile sectors within the economic system, and stock market plays an important role in economic development. stock market could also be a hub where facilities are provided to the investors to urge and sell their Shares, Bonds and Debenture etc. In other words, stock market could also be a platform for trading various securities and derivatives with none barriers. available Market various companies are listed to their business venture through public issues. within the present scenario, future investors are investing within the businesses through stock market to realize profit. In India listed stock exchange are Bombay stock market (BSE), the National stock exchange (NSE) and thus the Calcutta stock exchange (CSE). These three are largest Indian stock market. Volatility could also be a statistical measure of the dispersion of returns for a given security or Market Index. Commonly, the upper the volatility greater the danger associated with the security. Volatility estimation is significant for several reasons associated with different people within the market. Developed markets still provide over long period of some time with higher returns constituting low volatility. "Indian market has started ending up being instructive more efficient diverged from made countries."The examination would encourage the peruse to know the past, current and future parts of Indian stock trade.

The purpose of this study is to identify the behavior of returns and volatility with the attributes of non-linearities and asymmetric patterns in the returns series of KSE and modeling of volatility for asset pricing with macroeconomic, value at risk and semi variance in GARCH specification. Daily data is used for stock returns for the period of Jan 2000 to Dec 2015 and the data for macroeconomic variables is taken for the period Jan 2000 to Dec 2015 on monthly basis. GARCH and GARCH-in-mean model is used for modeling the volatility in this study. GARCH-in-mean model is extended with macroeconomic variables in mean as well as with variance equation and findings indicate that the change in interest rate has significant negative effect on returns and increase in change in interest rate will decrease stock returns. The changes in oil prices have also significant positive impact on KSE returns. The results indicate that interest rate is significantly positively related to volatility. Ho...

Various volatility estimators and models have been proposed in the literature to measure volatility of asset returns. In this paper, we compare empirical performance of various unconditional volatility estimators and conditional volatility models (GARCH and ...

This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory through a new class of semiparametric ARFIMA models with HYGARCH errors (SEMIFARMA-HYGARCH); this class includes nonparametric deterministic trend, stochastic trend, short-range and long-range dependence and long memory heteroscedastic errors. We study the daily returns of the Dow Jones from 1896 to 2006. We estimate several models and we find that the coefficients of the SEMIFARMA-HYGARCH model, including long memory coefficients for the equations of the mean and the conditional variance, are highly significant. The forecasting results show that the informational shocks have permanent effects on volatility and the SEMIFARMA-HYGARCH model has better performance over some other models for long and/or short horizons. The predictions from this model are also better than the predictions of the random walk model; accordingly, the weak efficiency assumption of financial markets seems violated fo...

The purpose of this research is to analyze effect of macroeconomic , commodity market, and asian stock index on composite stock price index, 2018 to 2022. This type of research is descriptive quantitative. The type of data in this study is time series data taken from January 2018 to December 2022 so that a target population of 60 (5 years x 12) monthly report data is obtained for the sample data in this study. This is due to research that the sample used uses census research techniques. Data collection techniques through library research and field survey. The data analysis used in the study uses error correction model (ECM). The results show that in long and short term, inflation has a negative and significant effect on composite stock price index. Exchange rate has a negative and insignificant effect on composite stock price index. Interest rate has a negative and significant effect on composite stock price index. World oil price has a negative and significant effect on composite stock price index. Gold price has a negative and significant effect on composite stock price index. Nikkei 225 has a positive and insignificant effect on composite stock price index. KLCI has a negative and insignificant effect on composite stock price index. KOSPI has a negative and insignificant effect on composite stock price index. Hang seng in long term has a negative and insignificant effect on composite stock price index, but in short term has a positive and insignificant effect on composite stock price index.

Global financial integration and increasing trade have facilitated the free movement of capital among countries, especially in financial markets. As a result, the complexities of the transactions and extremely large increase in the volume of transactions have contributed to exposure to external shock by these markets. For this paper, we used the BEKK-GARCH model to determine the direction of volatility spillover between the USD/KRW and USD/PHP foreign exchange markets and the stock markets of South Korea and the Philippines to better understand how financial shocks between markets can affect the stability and competitiveness of their markets and help regulators and policy makers correct these imbalances. The paper is broken down into four periods

Advantages and limitations of the existing volatility models for forecasting foreign-exchange and stock market volatility from multiscale and high-dimensional data have been identiÿed. Support vector machines (SVM) have been proposed as a complimentary volatility model that is capable of e ectively extracting information from multiscale and high-dimensional market data. SVM-based models can handle both long memory and multiscale e ects of inhomogeneous markets without restrictive assumptions and approximations required by other models. Preliminary results with foreign-exchange data suggest that SVM can e ectively work with high-dimensional inputs to account for volatility long-memory and multiscale e ects. Advantages of the SVM-based models are expected to be of the utmost importance in the emerging ÿeld of high-frequency ÿnance and in multivariate models for portfolio risk management.

We use the economic policy uncertainty indices of Baker, Bloom, and Davis (2016) in combination with the mixed data sampling (MIDAS) approach to investigate the US and UK stock market movements. The long-run US-UK stock market correlation depends positively on US economic policy uncertainty shocks. The US long-run stock market volatility depends significantly on the US economic policy uncertainty shocks but not on UK shocks while the UK depends significantly on both.

We use Baker, Bloom, and Davis's (2016) economic policy uncertainty indices in combination with the mixed data sampling (MIDAS) approach to investigate long-run stock market volatility and correlation, primarily for the US and UK. Long-run US-UK stock market correlation depends positively on US economic policy uncertainty shocks. The dependence is asymmetric, with only positive shocks-increasing uncertainty-being of importance. The US long-run stock market volatility depends significantly on US economic policy uncertainty shocks but not on UK shocks, while the UK long-run stock market volatility depends significantly on both. Allowing for US economic policy uncertainty shocks improves the outof-sample forecasting of US-UK stock market correlation and enhances portfolio performance. Similar results apply to the long-run correlation between the US and Canada, China, and Germany.

Objective: The purpose of this paper is to demonstrate the effectiveness of the nonparametric GARCH model for the prediction of future Bitcoin prices. Methodology: The use of parametric GARCH models to characterize the volatility of Bitcoin returns is widely used in the empirical literature. Alternatively, we consider a non-parametric approach to model and forecast the volatility of Bitcoin returns. Results: We show that the volatility forecast of the nonparametric GARCH model yields superior performance compared to an extended class of parametric GARCH models. Originality / relevance: The improved accuracy of forecasting the volatility of Bitcoin returns based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to commonly used GARCH parametric models..

Global financial integration and increasing trade have facilitated the free movement of capital among countries, especially in financial markets. As a result, the complexities of the transactions and extremely large increase in the volume of transactions have contributed to exposure to external shock by these markets.

For this paper, we used the BEKK-GARCH model to determine the direction of volatility spillover between the USD/KRW and USD/PHP foreign exchange markets and the stock markets of South Korea and the Philippines to better understand how financial shocks between markets can affect the stability and competitiveness of their markets and help regulators and policymakers
correct these imbalances. The paper is broken down into four periods namely 2004 to 2013, 2004 to 2008, inclusive of the 2008 Global Financial Crisis, and 2009 to 2013, and 2014 to 2018 for the two silent periods after the crisis. The results showed the South Korean markets had no evidence
of shock transmissions but had bidirectional volatility transmission in the period of 2004 to 2008, and unidirectional volatility transmission from their stock market to their USD/KRW foreign exchange market for the other three time periods. This proves that there is an interdependence and market integration between the foreign exchange market and stock market, which could change from one period to another, especially during a financial crisis. This could trigger policy changes by the regulators in the aforementioned markets through adjustments that will be made either in the stock prices or foreign exchange rates to protect the competitiveness of these markets.
Furthermore, this could guide investors by providing them with early warning signs related to external shocks that can adversely affect their investments by employing portfolio asset allocation and risk management tools to protect their investments.

This study examines the behavior of the Indian stock market during financial crises and analyzes how such economic
downturns impact market performance and investor responses. Financial crises often trigger significant market volatility and
uncertainty, posing challenges for investors and financial institutions. By investigating stock price movements, trading volumes, and
the reactions of various investor groups—such as retail, institutional, and foreign investors—this research aims to provide a
comprehensive understanding of market dynamics during crises. The study also explores the role of investor risk profiles in shaping
their investment decisions in times of financial stress. Insights from this research can help policymakers, investors, and financial advisors
develop effective strategies to manage risks and make informed investment choices during periods of economic instability. The findings
contribute to a better understanding of how financial crises influence the Indian stock market, offering valuable guidance for navigating
future financial downturns.

This paper investigates the impact of the 2024 Indian Lok Sabha elections on the stock market, a
relationship of significant interest to economists, investors, and policymakers. Given the pivotal
role of the Lok Sabha in shaping India's economic policies and legislative landscape, the outcomes
of these elections are expected to have substantial implications for market dynamics. Historical
data analysis reveals that past elections have consistently led to increased market volatility and
activity, reflecting investor sentiment towards political stability and economic policy continuity.
This study employs a combination of quantitative and qualitative approaches, including historical
data analysis, event studies, and predictive modeling, to forecast potential market reactions. Key
factors such as political stability, proposed economic reforms, global economic environment, and
investor sentiment are examined to understand their influence on market behavior. The 2024
elections are especially significant as they occur during a period of post-pandemic economic
recovery, technological advancements, and evolving geopolitical relations. By analyzing these
factors, the paper aims to provide insights into how different election outcomes might affect
various sectors, investment strategies, and overall market performance. The findings are intended
to aid investors in navigating the election period, making informed decisions, and understanding
the broader implications of political events on economic stability and growth. This comprehensive
analysis contributes to the existing literature on political economy and market behavior, offering
valuable perspectives for stakeholders involved in the Indian financial markets.

This paper investigates the impact of the 2024 Indian Lok Sabha elections on the stock market, a relationship of significant interest to economists, investors, and policymakers. Given the pivotal role of the Lok Sabha in shaping India's economic policies and legislative landscape, the outcomes of these elections are expected to have substantial implications for market dynamics. Historical data analysis reveals that past elections have consistently led to increased market volatility and activity, reflecting investor sentiment towards political stability and economic policy continuity. This study employs a combination of quantitative and qualitative approaches, including historical data analysis, event studies, and predictive modeling, to forecast potential market reactions. Key factors such as political stability, proposed economic reforms, global economic environment, and investor sentiment are examined to understand their influence on market behavior. The 2024 elections are especially significant as they occur during a period of post-pandemic economic recovery, technological advancements, and evolving geopolitical relations. By analyzing these factors, the paper aims to provide insights into how different election outcomes might affect various sectors, investment strategies, and overall market performance. The findings are intended to aid investors in navigating the election period, making informed decisions, and understanding the broader implications of political events on economic stability and growth. This comprehensive analysis contributes to the existing literature on political economy and market behavior, offering valuable perspectives for stakeholders involved in the Indian financial markets.

I, Paramin Khositkulporn, declare that the DBA thesis entitled "The Factors Affecting Stock Market Volatility and Contagion: Thailand and SouthEast Asia Evidence" is no more than 65,000 words in length including quotes and exclusive of tables, figures, appendices, bibliography, references and footnotes. This thesis contains no material that has been submitted previously, in whole or in part, for the award of any other academic degree or diploma. Except where otherwise indicated, this thesis is my own work.

I, Paramin Khositkulporn, declare that the DBA thesis entitled "The Factors Affecting Stock Market Volatility and Contagion: Thailand and SouthEast Asia Evidence" is no more than 65,000 words in length including quotes and exclusive of tables, figures, appendices, bibliography, references and footnotes. This thesis contains no material that has been submitted previously, in whole or in part, for the award of any other academic degree or diploma. Except where otherwise indicated, this thesis is my own work.

Predicting investors reactions to financial and political news is important for the early detection of stock market jitters. Evidence from several recent studies suggests that online social media could improve prediction of stock market movements. However, utilizing such information to predict strong stock market fluctuations has not been explored so far. In this work, we propose a novel event detection method on Twitter, tailored to detect financial and political events that influence a specific stock market. The proposed approach applies a bursty topic detection method on a stream of tweets related to finance or politics followed by a classification process which filters-out events that do not influence the examined stock market. We train our classifier to recognise real events by using solely information about stock market volatility, without the need of manual labeling. We model Twitter events as feature vectors that encompass a rich variety of information, such as the geographical distribution of tweets, their polarity, information about their authors as well as information about bursty words associated with the event. We show that utilizing only information about tweets polarity, like most previous studies, results in wasting important information. We apply the proposed method on high-frequency intra-day data from the Greek and Spanish stock market and we show that our financial event detector successfully predicts most of the stock market jitters.