Stock Market Index Returns Research Articles

Tests for Volatility Dynamics in Bivariate Stochastic Volatility Models

Recently Chiba and Kobayashi (2013) have proposed the Lagrange multiplier (LM) test for the null hypothesis that volatilities of two asset return processes are driven by only one stochastic volatility (SV) process in a bivariate SV model. They apply their LM test to Asian stock market index returns, and find that the null hypothesis is not rejected for several pairs of those returns. They, however, derive the test statistic under an unconventional assumption that the conditional distribution of log squared return is normally distributed conditional on the SV, which seems inappropriate for a model of financial assets returns. This paper develops their analysis in two aspects. First, by a method of simulation, we examine the performance of their LM test under a conventional (bivariate) SV model in that each return (not log squared return) follows a normal distribution conditional on the SV. Second, because the null hypothesis examined in Chiba and Kobayashi (2013) is restrictive for many financial time series, we propose two LM tests for less restrictive but plausible null hypotheses. Our simulation study demonstrates that even for the conventional SV model, the actual size of their LM test is reasonably close to the nominal sizes and its power is high enough so that their LM test is useful for practical applications. Moreover, our empirical analysis with newly proposed LM tests finds that for many Asian stock market index returns, although their volatility processes themselves are not identical, they are likely to be driven by a common (or perfectly positively correlated) shock.

Indian culture, lunar phases and stock market returns

Indian culture, lunar phases and stock market returns

English

English

Financial contagion in the subprime crisis context: A copula approach

This paper investigates the financial contagion phenomenon and its intensity in the context of the subprime crisis by adopting the copulas approach. The wavelet technique is used to predict the accurate occurrence of the subprime crisis. To estimate the parameters of the different copulas, we use the canonical maximum likelihood method (CML). Based on the daily returns of stock market indices of five American countries (Brazil, Argentina, Mexico, Canada and the USA) and nine Asian countries (Japan, Hong Kong, India, Australia, Indonesia, Malaysia, Korea, China and Singapore) from 01/01/2003 to 30/12/2011, our results show that the contagion effect exists for all American markets as well as the Indian, Australian, Indonesian, Malaysian, Chinese and Singaporean ones. The findings also show that American markets record high levels of contagion intensity in comparison to their Asian counterparts. This study also confirms the contagious nature of the subprime crisis between USA and both American and Asian countries.

Macroeconomic Variables and Indian Stock Market Return:A study

India ranks sixth largest economy in the world by gross domestic product (GDP). In 2018 India’s economy became the world’s fastest growing major economy surpassing China. The stock market is mirror of the economy. There are so many macroeconomic variables like inflation, interest rates, GDP, FII investment; prices of crude oil, exchange rate of currency etc. can affect the return of the stock market. The aim of the research work is to measure the year wise change in FII investment in equity, year wise change in Brent crude oil price, year wise change in the INRUSD exchange rate and year wise change in the return of market (NIFTY) and find out relation between change in macroeconomic variables and the return of the Indian stock market for a period of ten years from year 2009- year 2018. Collected secondary data are analysed by statistical measures like mean, median, standard deviation, skewness, kurtosis, correlation matrix, multiple correlation and regression. It was found that Change in FII investment in equity and NIFTY return and Change in INR-USD exchange rate and Change in Crude oil price are positively related. And Change in Crude oil price is negatively related with Change in FII investment in equity and NIFTY return.

Structural Breaks and Volatility Persistence of Stock Returns: Evidence from the US and UK Equity Markets

This paper quantitatively investigates the effects of structural breaks on stock return volatility persistence by using the US and UK stock market index return data. Applying two kinds of representative univariate GARCH models of standard GARCH and EGARCH models, we derive the following interesting findings. (1) First, we find that for both the US and UK stock market returns, the volatility persistence parameter values of standard GARCH models decrease when structural breaks are taken into account. (2) Second, we further reveal that for both the US and UK stock market returns, the volatility persistence parameter values of EGARCH models again decline when structural breaks are taken into consideration.

Forecasting Foreign Institutional Investors' (FII) Investment Flows in India: An Autoregressive Model

India is considered as one of the fastest emerging economies in the world and a leading nation in attracting foreign investments. The country offers huge investment opportunities to reap benefits for both domestic and foreign institutional investors (FIIs) in its equity market. India's FII investments are characterized by positive net inflows in most of the years since its inception in 1992. Only during 1998-99 and 2008-09, FII outflows were higher than inflows. Many earlier studies have concluded that FII flows were largely influenced by the Indian stock market returns. Considering the volatile nature of FII investment flows, this study attempted to forecast FII flows in Indian stock market using an autoregressive model. Net FII investments and stock market returns data from July 2013 to December 2015 were used to estimate an autoregressive model. Net FII flows from January 2016 to June 2016 were forecasted based on the formulated estimation. The forecasted values were then compared with the actual values to verify the model's reliability. The autoregressive model used in the study was an acceptable model as forecast errors were minimal, and it captured the investment trends correctly. Forecasting the FII investments can help in better policy formulation and are useful for making strategic decisions in the capital market.

Oil Jump Risk

We show that the innovation in the risk-neutral probability of large downward and upward jumps in oil prices has a considerable predictive power for important economic indicators such as GDP growth, consumption growth, and total investment. In addition, we observe that the upside jump risk probability is a significant predictor of stock market index return and the returns of oil futures. Furthermore, the upside jump probability is a significant and relatively strong predictor of oil market fundamentals including inventory growth, demand growth, and OPEC’s production growth. Upside jump risk is also a driver of the cross-section of stock returns before the U.S. oil production increase in 2011. The average monthly return for the high-low upside jump risk exposure portfolio is -0.94% and -1.13%, using the 1996-2014 and 1996-2011 time periods respectively. The implications of the variance risk for the cross-section of stock returns vanishes after controlling for the large upside and downside jump risks. The shale revolution and considerable increase of the US oil production have killed the effect of upside risk premium after 2011. During the sub-period 2011-2014, the variance risk premium gets significant again, like it was before 2000.

Geodetic convex boundary curvatures of the communities in stock market networks

We analyze the daily returns of stock market indices of NASDAQ and S&P 500 over the period of 2004–2015. We build network models by using a threshold method that captures strong relations amongst the agents. We also subdivide the whole time scale into 20 parts to capture structural changes in networks throughout the global economic crisis of 2008. To determine the dynamics of the structural changes, we use the mimetic discretization of the curvatures on boundaries of communities. We observe different geometric behavior of the boundary throughout the economic crisis. Our findings suggest that the mimetic discretization of boundary curvature is an efficient tool to capture granular structural changes in financial networks.

Seasonality in Indian Stock Market: Delusion or Reality

In business, seasonal variations in production and sales are a recognised element. Hence, managers often try to calculate the fluctuations in demand and predict a more accurate nature of supply schedules. Seasonality in stock returns refers to a time series data that shows a systematic and repetitive variation over a span of a year that happens periodically. The primary root of seasonal disparities is the change in climate. The objective of this paper is to investigate whether the ‘day-of-the-week’ effect is prevailing in the Indian stock market returns. Empirical evidence of the existence of seasonality and thereby consistent abnormal returns have been identified in the efficient market postulate. One noteworthy inconsistency revealed was abnormal rates of return related to the calendar months. Several studies in this field empirically verified tests and found the existence of such anomalies on the stock return data that majorly refined to theories that turn of the year, the day-of-the-week and holiday effect has steadily created abnormal returns in both the developed and emerging markets. This paper tries to enrich the existing knowledge and to inspect whether cyclical irregularities continue in the emerging markets like India. The study provides the evidence that the presence of the day-of-the-week effects, precisely Monday effect and Friday effect, have disappeared in the Indian capital markets.

Momentum Effect, Value Effect, Risk Premium and Predictability of Stock Returns - A Study on Indian Market

Efficient market hypothesis (EMH), one of the central pillars of modern financial theories, often fails to explain the ?financial anomalies?. One fatal challenge of EMH probably comes from the theoretical assumption of ?rational man?. According to EMH, the fully rational investor may change his demand for financial assets on the basis of available information. According to EMH, at any given point of time, the stock price should reflect all the available information, and predictability of stock returns should be impossible. However, the literature shows ample evidence of abnormal returns related to firm and market specific attributes. In financial literature, these variations are often termed as ?financial anomalies?. Within the framework of behavioural finance, there are research results that contain evidence on predictability of future stock market returns based on financial anomalies (Stanivuk et al., 2012). Value effect and momentum effect are the two prominent financial anomalies (Ho, 2012). This paper explores the predictability of Indian stock market returns using multiple discriminant analysis. Our result shows that the risk premium, momentum and value effect may have significant power for predicting the Indian stock market returns. The validity test of the model also corroborates the impact of financial anomalies over predictability of stock returns.

Kodakcoin: A Blockchain Revolution or Exploiting a Potential Cryptocurrency Bubble?

Eastman Kodak is an American technology company that produces imaging products. In 2018, it announced its intentions to enter the crytpocurrency market, raising concerns that it could be taking advantage of a potential cryptocurrency bubble for short-term gains. We analyse the relationships between Kodak, crytocurrency and stock market index returns. We find evidence of a significant, sustained increase in both the share price and price volatility of Kodak after the KODAKCoin announcement, with an increased correlation between the price of Kodak shares and Bitcoin.

The “Cubic Law of the Stock Returns” in emerging markets

Abstract Excess volatility in main emerging and developed stock markets is carefully analysed in this study. Tail distribution of returns of both stock market index and individual stocks is evaluated and compared with the theoretical distribution found by Gabaix et al. (2003, 2006). For stock market index, recursive and rolling estimation are used. In recursive estimation, we find that all the developed markets obey “the Cubic Law of the Stock Returns”, while most of the emerging countries exhibit heavier tail with a tail index lower than 3 at 95% significance level. In rolling estimation, the tail index in the developed markets does not stabilise around 3, and after 2008 financial crisis, all the developed markets and most emerging ones suffer a drop in the tail index. For individual stocks, the tail distributions of stock returns, trading volume, and the number of trades in each emerging country behave quite differently from the theoretical model by Gabaix et al. (2006), especially the stock returns.

State Space Approach to Adaptive Fuzzy Modeling: Application to Financial Investment

This paper proposes a new state space approach to adaptive fuzzy modeling under the dynamically changing environment, where Bayesian filtering sequentially learns parameters including model structures as state variables. Moreover with a particle filtering algorithm, our approach is widely applicable to the machine learning for real-time observation data flows. To show the effectiveness of our framework, a Takagi-Sugeno-Kang fuzzy model is concretely designed for financial portfolio construction based on a benchmark return, that is stock market index (e.g. S\&P 500 index) return with non-negative lower bound, and successfully attains fine risk-return profiles. An out-of-sample simulation with our proposed portfolio construction demonstrates the validity of our framework.

Quantifying the cross-sectional relationship between online sentiment and the skewness of stock returns

The constantly increasing utilization of social media as the alternative information channel, e.g., Twitter, provides us a unique opportunity to investigate the dynamics of the financial market. In this paper, we employ the daily happiness sentiment extracted from Twitter as the proxy for the online sentiment dynamics and investigate its association with the skewness of stock returns of 26 international stock market index returns. The empirical results show that: (1) by dividing the daily happiness sentiment into quintiles from the least to the most happiness days, the skewness of the Most-happiness subgroup is significantly larger than that of the Least-happiness subgroup. Besides, there exist significant differences in any pair of subgroups; (2) in an event study methodology, we further show that the skewness around the highest happiness days is significantly larger than the skewness around the lowest happiness days.

GARCH Model, Heavy Tails and the Chinese Stock Market Returns

The Chinese stock market is unique in which it is moved more by individual retail investors than institutional investors. Therefore, for economic and political stability it is more important to efficiently manage the risk of the Chinese stock market. We investigate its volatility dynamics through the GARCH model with three types of heavy-tailed distributions, the Student’s t, the NIG and the NRIG distributions. Our results show that estimated parameters for all the three types of distributions are statistical significant and the NIG distribution has the best empirical performance in fitting the Chinese stock market index returns.

Volatility Spillover Effect in Indian Stock Market

The study aims to empirically examine the transmission of volatility from global stock markets to Indian stock market. The study is based on time series data comprising of daily closing stock market indices from National Stock Exchange (NSE), India and major foreign stock exchange of the three countries one each from America, Europe and Asia making the highest portfolio investment in Indian stock market. The study period covers 11 years from 1st January, 2005 to 31st December, 2015 comprising a total of 2731 observations. The Indian stock index used is CNX Nifty 50 and the foreign indices are S & P 500 from USA, FTSE 100 from UK, and Nikkei 225 from Japan. The results reveal that the Indian stock market return is co-integrated with market returns of US, UK and Japanese stock markets. Therefore, the return and hence volatility of Indian stock market is associated with global markets which depicts that it is getting integrated with global financial markets. The results provide empirical evidence for volatility transmission or volatility spillover in the Indian stock market from global markets. There exists inbound volatility transmission from US market to Indian stock market. The Indian and UK stock market have bi-directional volatility transmission. However, there exists presence of only outbound volatility transmission from Indian stock market to Japanese stock market. The volatility transmission from global markets to India is rapid with the spillover effect existing for up to three days only.Janapriya Journal of Interdisciplinary Studies, Vol. 5 (December 2016), page: 83-101

Asymmetric Volatility of the Indian Stock Market and Foreign Portfolio Investments : An Empirical Study

Generally, a stock index may respond more to bad news (negative shocks) than to good news (positive shocks). It means stock market volatility may tend to be greater in a declining market than in a rising market. This behavior of stock return volatility is known as asymmetric volatility or leverage effect of volatility. A healthy and vibrant capital market is important for economic development of a nation. In the present Indian scenario, stock prices inIndiafrequently deviate from fundamental values, and it is believed that these variations are mainly due to the presence of the most dominant investment group - foreign portfolio investors. This paper attempted to analyze the stock return volatility, especially the asymmetric effect of Indian stock market return volatility and contribution of foreign portfolio investment to that volatility. The present study was conducted by taking daily data for a period of 12 years fromApril 1, 2003toMarch 31, 2015consisting of 2898 trading observations. To study the leverage effect and impact of FPI on stock market volatility, the study used ARCH family models; GARCH, E-GARCH, and TARCH. The results of the study confirmed the existence of volatility clustering and leverage effect in the Indian stock market. Hence, it was observed from the study that the investment activities of FPIs have had a significant impact on the volatility of the Indian stock market.

Oil price changes and stock market returns: cointegration evidence from emerging market

The aim of this paper is to examine whether changes in nominal oil prices (Brent and West Texas Intermediate (WTI)) affect the stock market returns in the context of an emerging market framework. The Autoregressive Distributed Lag bounds testing approach of cointegration is used to test for the long run relation between the two variables, where the daily stock market index return is calculated using the first difference in the natural logarithms of stock market index. Further, we test for the stability of the cointegration relationship by examining the sensitivity analysis where diagnostic tests for serial correlation (namely the Breusch–Godfrey serial correlations LM test) and cumulative sum of recursive residuals (CUSUM) are employed. Using daily data from January 3, 2000 to December 9, 2015, the findings suggest that there is long run integration between oil prices and stock returns series in which the daily oil price shocks have a negative impact on stock returns. The highly significant error correction coefficient indicates high rate of convergence to equilibrium. In addition, the Toda and Yamamoto (J Econom 66(2):225–250, 1995) Granger non‐causality test indicates significant bidirectional causality between stock market returns and Brent nominal oil price, meanwhile there is unidirectional causality running from WTI oil price to stock market returns. These findings are, up to some extent, meaningful for investors, portfolio managers and policy makers.

Do oil futures prices predict stock returns?

Abstract This paper explores stock return predictability by exploiting the cross-section of oil futures prices. Motivated by the principal component analysis, we find the curvature factor of the oil futures curve predicts monthly stock returns: a 1% per month increase in the curvature factor predicts 0.4% per month decrease in stock market index return. This predictive pattern is prevailing in non-oil industry portfolios, but is absent for oil-related portfolios. The in- and out-of-sample predictive power of the curvature factor for non-oil stocks is robust and outperforms many other predictors, including oil spot prices. The predictive power of the curvature factor comes from its ability to forecast supply-side oil shocks, which only affect non-oil stocks and are hedged by oil-related stocks.