Stock Market Index Returns Research Articles

Examination of Stock Market Returns Viz-a-Viz Utility Price and Exchange Rate Movements: Evidences from India after Credit Crisis

This paper constitutes an analysis on Indian stock market returns. The conducted study probes the nexus between returns on four stock market indices, namely Sensex (BSE 30), Energy Index (BSE ENERGY), Oil and Gas Index (BSE OIL & GAS), and Capital Goods (BSE CG), and returns on traded energy prices, traded gas prices, global oil prices, and rupee-dollar exchange rates, based on weekly data from June 9, 2008 to May 16, 2014. Generalized autoregressive conditional heteroskedasticity (GARCH) and exponential GARCH (EGARCH) models have been used to examine the impacts of utility price and exchange rate movements on mentioned stock market indices. The study have established that movements of traded energy price, global oil price, and exchange rate have significant impacts on the movements of considered stock market indices, and surprisingly movements in gas price have no significant impact in any of the indices. Span of the study has been selected based on the establishment of Indian Energy Exchange, and in order to rule out the possibilities of volatility clustering, which may possibly probe biasness in the results, data pertaining to lower frequency range have been selected. Data for this study have been collected from Reserve Bank of India, Indian Energy Exchange, Bombay Stock Exchange, Multi Commodity Exchange, and U.S. Energy Information Administration.

To Study Factors Affecting Investor Preference, Performance And Returns Regarding IPOs

The economy worldwide is growing more and more complex at a rapid pace. The companies are trying various methods and ways to attract investors. The investors have various ways of investment. In current scenario because of the growth of Indian stock market and higher returns in investments in stocks, investors like to invest their money in stocks. IPO one of ways to invest in stock market found to be very lucrative to the investors over the last few decades. In many cases, IPOs has offered maximum return that might transcend their expectation. However it is not a case for all IPOs at all time. This is because number of qualitative and quantitative factors that affects the IPOs. The present study was undertaken to analyze factors affecting investor preference and returns regarding IPOs. This main objective achieved by splitting it into two parts, the first dealing with the preference of investor about the IPOs and other part with the factors various quantitative factors affect return regarding IPOs. For the first objective, a questionnaire was administered to investors who invested in IPOs. After that, researcher is found out the returns of the IPOs in the respective one day, one week, one month and three month.

Forecasting Volatility and the Risk–Return Tradeoff: An Application on the Fama–French Benchmark Market Return

AbstractThe paper uses the daily stock market index returns of Fama–French to attempt a comparative forecasting analysis of different volatility models. The comparison naturally pre-requests the specification of the competing volatility frameworks and therefore the paper among other issues deals with dilemmas about whether volatility–return relations hold. As expected the analysis focuses on FIEGARCH-M models that extend the basic long memory volatility framework of

Robust Kalman filter based on a fuzzy GARCH model to forecast volatility using particle swarm optimization

Stock market volatility comprises complex characteristics of time-varying irregular behavior and asymmetric clustering properties with respect to both positive and negative stock index returns. In this paper, we present a fuzzy-GARCH model to analyze asymmetric clustering properties and a robust Kalman filter to address the problem of the time-varying irregular behavior of volatility. In our approach, we first use a fuzzy system to analyze clustering regimes based on stock market index returns. Second, we use the clustering regimes of the first stage to set up generalized autoregressive conditional heteroskedasticity (GARCH) models and reformulated state space. Finally, we use a robust Kalman filter to reduce time-varying complexity when forecasting volatility. The proposed method is based on state space and joins the parameters of membership functions and GARCH models that are highly complex and nonlinear. We present an iterative algorithm based on particle swarm optimization to estimate parameters of the membership functions and GARCH models. The effectiveness of the approach is demonstrated on stock market data from the Taiwan Stock Exchange Weighted Index (Taiwan), Hang Seng Index (Hong Kong), and Japan Nikkei 225 Index (Japan). From the simulation results, we determine that forecasting of out-of-sample volatility performance is significantly improved when the GARCH model considers both asymmetric effect and robust adaptive forecasting.

Forecasting Surrender Rates Using Elliptical Copulas and Financial Variables

A multistage stochastic model to forecast surrender rates for life insurance and pension plans is proposed. Surrender rates are forecasted by means of Monte Carlo simulation after a sequence of GLM, ARMA-GARCH, and copula fitting is executed. The model is illustrated by applying it to age-specific time series of surrender rates derived from pension plans with annuity payments of a Brazilian insurer. In the GLM process, the only macroeconomic variable used as an explanatory variable is the Brazilian real short-term interest rate. The advantage of such a variable is that we can take future market expectation through the current term structure of interest rates. The GLM residuals of each age/gender group are then modeled by ARMA-GARCH processes to generate i.i.d. residuals. The dependence among these residuals is then modeled by multivariate Gaussian and Student's t copulas. To produce a conditional forecast on a stock market index, in our application we used the residuals of an ARMA-GARCH model fitted to the Brazilian stock market index (Ibovespa) returns, which generates one of the marginal distributions used in the dependence modeling through copulas. This strategy is adopted to explain the high and uncommon surrender rates observed during the recent economic crisis. After applying known simulation methods for elliptical copulas, we proceeded backwards to obtain the forecasted distributions of surrender rates by application, in the sequel, of ARMA-GARCH and GLM models. Additionally, our approach produced an algorithm able to simulate multivariate elliptical copulas conditioned on a marginal distribution. Using this algorithm, surrender rates can be simulated conditioned on stock index residuals (in our case, the residuals of the Ibovespa returns), which allows insurers and pension funds to simulate future surrender rates assuming a financial stress scenario with no need to predict the stock market index.

Time-varying co-movements between stock market returns and oil price shocks

This paper investigates the time-varying co-movements between stock market returns and oil price shocks, for both oil-importing and oil-exporting countries. To achieve this, we use data from 1998 until 2013 and employ a Scalar-BEKK model. Our findings are as follows: (i) the correlation between oil price shocks and index returns are showing some differences depending on whether a country is oil-importer or oil-exporter, (ii) the correlations are industry-specific and shock-specific, (iii) the aggregate demand shocks generate moderate positive correlations with stock market returns until 2011, (iv) stock market index returns have low to zero correlation with the supply-side shocks and (v) oil specific demand shocks have a moderate positive correlation with all indices in the post-2006 period. Our results have important implication for investor, as well as, policy makers.

Estimating time-varying currency betas with contagion: New evidence from developed and emerging financial markets

This paper examines the conditional time-varying currency betas from five developed and six emerging financial markets with contagion and spillover effects. We employ a trivariate asymmetric BEKK-type GARCH-in-Mean (MGARCH-M) approach to estimate the time-varying conditional variance and covariance of returns of stock market index, the world market portfolio and bilateral exchange rate between the US dollar and the local currency. The results show that the world market and currency risks are not only priced in the stock markets, but also time-varying. It is found that currency betas are much more volatile than the world market betas, and currency betas in the emerging markets are more volatile than those in the developed markets. We find empirical evidence of contagion effect and spillovers between stock market and foreign exchange market during the recent global financial crisis, and the effect is stronger in the emerging markets than that in the developed markets. Two applications are provided to illustrate the usefulness of time-varying currency betas.

A statistical measure of financial crises magnitude

This paper postulates the concept of financial market energy and provides a statistical measure of the financial market crisis magnitude based on an analogy between earthquakes and market crises. The financial energy released by the market is expressed in terms of trading volume and stock market index returns. A financial “earthquake” occurs if the financial energy released by the market exceeds the estimated threshold of market energy called critical energy. Similar to the Richter scale which is used in seismology in order to measure the magnitude of an earthquake, we propose a financial Gutenberg–Richter relation in order to capture the crisis magnitude and we show that the statistical pattern of the financial market crash is given by two statistical regimes, namely Pareto and Wakeby distributions.

A note on cointegration of international stock market indices

Cointegration is frequently used to assess the degree of interdependence of financial markets. We show that if a stock's price follows a stock specific random walk, market indices cannot be cointegrated. Indices are a mere combination of n different random walks which itself is non-stationary by construction. We substantiate the theoretical propositions using a sample of 28 stock indices as well as a simulation study. In the latter we simulate stock prices, construct indices and test whether these indices are cointegrated. We show that while heteroscedasticity misleads cointegration tests, it is not sufficient to explain the high correlation between stock market index returns. A common random walk component and correlated price innovations are necessary to reproduce this feature.

Extracting Global Stochastic Trend from Non-Synchronous Data

We use a Kalman filter type model of financial markets to extract a global stochastic trend from the discrete non-synchronous data on daily stock market index returns of different stock exchanges. The model is tested for robustness. In addition, we derive “most important” hours of world financial market and estimate the relative importance of local versus global news for different stock markets. The model generates results that are consistent with intuition.

Cause and effect between FII trading behaviour and stock market returns

PurposeThe purpose of this paper is to examine the direction of causality between foreign institutional investment (FII) trading volume and stock market returns in the Indian context. There is evidence of uni‐directional causalities from stock returns to FII flows across various sample periods. The paper attempts to establish whether net FII trading volume causes variations in stock market returns or vice versa.Design/methodology/approachUsing daily data on three different measures of FII trading volume as proxy for FII trading behaviour and S&P CNX Nifty returns, Granger‐causality approach is applied to investigate the bi‐directional causality between net FII trades and returns.FindingsBi‐directional causality between net FII investment and Indian stock market return is observed. In general, the FIIs seem to be chasing the Indian stock market returns. It is found that FII trading behaviour resulting in heavy trading volumes may cause variations in stock market returns only in the very short‐term, but afterwards, it is the stock market returns which cause changes in FII trading behaviour.Research limitations/implicationsSince foreign equity investors monitor the movement of stock prices, and furthermore, the role of FIIs' exerting impact on Indian stock markets tends to be growing, the authorities will have to develop an environment where FIIs would maintain their positions with confidence, thereby making the markets, as well as investments, more stable. This research considered only stock market returns to test its relationship with three measures of FII trading volume; more macroeconomic as well as microeconomic variables may further be considered for the purpose.Originality/valueThe paper contributes some empirical evidence using three different measures of FII trading volume as proxy of FII trading behaviour, and its bi‐directional relationship with Indian stock market returns.

Structural change in tail behaviour and the recent financial crises

This paper is a contribution in exploring the empirical evidence of the instability in the tail behaviour returns of stock market indices, based on some developments in the analysis of structural change models. The proposed approach can jointly determine the number of structural breaks in a series of tail–indexes and estimate the mean tail–index levels in different regimes. Here we advocate a modified Hill estimator for the tail index. We provide simulations that indicate good finite sample properties of our procedure. The proposed method is then applied to the tail behaviour returns of two international stock market indices, SP500 (USA) and CAC40 (France). The results indicate that procedures perform reasonably well and lead to an appropriate number of breaks with locations coinciding with major financial crisis and events such as the LTCM crisis, September 11, 2001 terrorist attack, sub–prime crisis in 2008 and European Union (EU) debt crisis triggered on 2010.

The Dynamical Relationship between Oil Price Shocks and Selected Macroeconomic Variables In Turkey

In many empirical studies, the dynamic relationship among energy sector variables (such as, oil, electricity, gasoline, coal, renewable energy, etc.) and economic variables (such as; financial markets, real economy and the overall economy) are studied. Oil price changes may affect the economic variables more of oil importer countries then oil exporter countries especially emerging markets. In addition to this, oil price changes and shocks may be an important device to explain stock market index return. In this paper, Istanbul stock exchange market index (ISE-100), interest rates, exchange rates and oil price are analyzed by using a vector autoregressive (VAR) approach for Turkey. The results suggest that there is a dynamic relationship among oil price shocks, Istanbul stock market index, exchange rate and interest rate.

Impact of the Monetary Policy Instruments on Islamic Stock Market Index Return

Previous studies found that Islamic stock market index in Malaysia (KLSI), does not react, or react negatively to interest rate, although one of the main criteria of Islamic finance is to avoid business and activities that yield interest because of its prohibition in Islamic laws. On the other hand, studies of Islamic stock market index in the US (DJIMI) found that there is no impact of interest rate on DJIMI. These two stock market indices have different screening criteria and different composite of securities. This study aims at investigating the monetary policy variables impact, the effect of interest rate, and the use of stock market indices as a hedge against inflation. It also examines the volatilities of monetary variables, interest rates, and inflation rate on two Islamic stock market indices. Using time series analysis such as GARCH the results are as follows. It is found that in the variance univariate models of the conventional indices that M1, M3, inflation rate, and real growth in GDP are significant in influencing KLCI volatility, while M2, M3, inflation rate and interest rate affected DJINA volatility. On the other hand, in the Islamic indices, KLSI and DJIMI variance is influenced by M2, M3, and inflation rate. In addition, in the multivariate model, DJIMI is influenced by the interest rate and the inflation rate in the mean and variance equations. In contrast, KLSI is influenced commonly in the mean and variance equations by M3, and the inflation rate.

Simulating Fundamental Analysis of a Firm Using a VECX Model

The study aims at simulating and forecasting a company’s stock returns and prices by a fundamentalist analysis process based on a Vector Error Correction with Exogenous Variables (VECX) econometric model. To achieve this, we selected relevant fundamentalist indicators and specified a model capable of simulating a fundamentalist analysis of a Brazilian publicly listed company, Sadia S/A. The VECX model statistical validation was carried out by performing unit root tests, cointegration tests, Granger causality tests, correlation analysis, impulse-response functions, and variance decomposition. Data taken from the firm’s financial statements were used to compute seven fundamentalist indicators, which were defined as the model’s endogenous variables. The exogenous variables included in the model are the Brazilian Stock Market (Bovespa) Index return, the Brazilian GDP, the Brazilian basic interest rate (Selic), the US$/BRL exchange rate, and the international price of commodities poultry and corn. The final version of the estimated model takes into account cointegration relationships among the endogenous variables. Based on this model, the Company’s ex-post forecasts of the fundamentalist indicators were obtained, as well as its stock return and price forecasts. The results show that the simulation by a VECX econometric model is robust as seen by the quality of the forecasts obtained. Finally, a prognostic fundamental analysis was carried out based on the forecasts.

Conditional Dependency of Financial Series: An Application of Copulas

We develop a new methodology that measures conditional dependency. We achieve this by using copula functions that link marginal distributions, here chosen to obey a GARCH-type model with time-varying skewness and kurtosis. We apply this model to daily returns of stock-market indices. We find strong evidence of persistence in dependency both for local currency and $ US denominated series. For European stock markets, we also find evidence that large simultaneous returns of either sign lead to higher subsequent dependency. We show that dependency changes through time, as well. For stock markets within Europe, dependency increased whereas it decreased since the mid 90s when involving the S&P 500 or the Nikkei. We also suggest extensions for conditional asset pricing models involving time variation of co-skewness and co-kurtosis.

The Effect Of IFRS Adoption On Global Market Integration

This paper examines the effect of the adoption of international financial accounting standards (IFRS) on global capital market integration, measure by correlation matrix of the stock market index returns. We investigate a sample of countries that have adopted IFRS as their accounting standards for listed companies. Our results are consistent with the suggestion that these markets will have a higher degree of integration among them after their IFRS adoption as compared to the period before the adoption.

Anchoring and pre-existing knowledge in economic and financial settings

Contemporary research documents various psychological aspects of economic thought and decision-making. The main goal of our study is to analyze the role of the anchoring (bias) (Tversky and Kahneman, 1974) in perceiving economic and financial information, and, in particular, the effect of pre-existing knowledge on the degree of the bias. Anchoring bias refers to people's tendency to form their estimates for different categories, starting from a particular available, and often irrelevant, value and insufficiently adjusting their final judgments from this starting value. We carry out an experiment involving a group of MBA students, asking them to recall a number of recent economic and financial indicators (stock and bond market index returns, rates of inflation, currency exchange rates, etc.), with half of the participants receiving actual information about some unrelated indicators (anchors), before answering the questions. We document that significant anchoring bias is exhibited, on average, for each of our experimental questions and by vast majority of the participants, the degree of the bias being higher for women and older participants. Furthermore, in the context of the effect of pre-existing knowledge, we find that people exhibit significantly stronger bias for relatively more difficult questions. Our findings indicate that the less people know about an object or a category, the more they may be influenced by some, even arbitrary, anchors when estimating its characteristics or forecasting related future outcomes.

Stochastic volatility models with leverage and heavy-tailed distributions: A Bayesian approach using scale mixtures

This paper studies a heavy-tailed stochastic volatility (SV) model with leverage effect, where a bivariate Student- t distribution is used to model the error innovations of the return and volatility equations. Choy et al. (2008) studied this model by expressing the bivariate Student- t distribution as a scale mixture of bivariate normal distributions. We propose an alternative formulation by first deriving a conditional Student- t distribution for the return and a marginal Student- t distribution for the log-volatility and then express these two Student- t distributions as a scale mixture of normal (SMN) distributions. Our approach separates the sources of outliers and allows for distinguishing between outliers generated by the return process or by the volatility process, and hence is an improvement over the approach of Choy et al. (2008). In addition, it allows an efficient model implementation using the WinBUGS software. A simulation study is conducted to assess the performance of the proposed approach and its comparison with the approach by Choy et al. (2008). In the empirical study, daily exchange rate returns of the Australian dollar to various currencies and daily stock market index returns of various international stock markets are analysed. Model comparison relies on the Deviance Information Criterion and convergence diagnostic is monitored by Geweke’s convergence test.

Is There a Correlation Between World Cups and S&P 500 Performance?

Coinciding with the start of the 2010 edition of the FIFA World Cup it is fitting to explore if historically there has been any significant correlation between this event and stock market performance. Although it has been previously reported that there is a strong correlation between results of individual football matches and corresponding, short-term local stock market trends, there has only been one unpublished study examining the potential relationship between periodic, multi-week, multi-national sporting events and returns of a broad stock market index. Building on the work of Kaplansky and Levy (2008), by using data from 1950 to 2006, it was shown empirically that the S&P 500 Composite Index has an expected return of -2% over the period of a FIFA World Cup tournament. This relationship could be classified as a subset of the calendar effect class of behavioral finance anomalies. ----- UPDATE: The author has been informed that the working paper by Kaplansky and Levy was recently published in the Journal of Financial and Quantitative Analysis, vol. 45.