Stock Index Returns Research Articles

Long memory properties on Tunisian sectorial stock market index volatilities: evidence from FIEGARCH model

We investigate the impact of Tunisian revolution on two fundamental features of stock index returns, namely, the long memory, the asymmetric and leverage effects. Firstly, we apply the ICSS procedure to identify the eventual existence of structural breaks in volatility and secondly, we use the fractionally integrated exponential GARCH (FIEGARCH) which estimates a direct shock-persistence as well as a shock asymmetric volatility measurement. Applied to the daily returns relevant to nine sectorial stock indices and to the benchmark Tunisian index, our findings suggest that shock impact on construction, raw materials and financial service index volatility returns proves to be permanent during the revolution period, while its persistence in the other indices is found to be transitory. The leverage effect is negative, but our achieved results revealed a lower leverage effect in all indices. These results seem to be very important since the Tunisian revolution has a very important effect on the Tunisian stock exchange.

Examining Contemporaneous Relationship between Return of Nifty Index and India VIX

The study examines the contemporaneous relationship between Nifty returns and India VIX returns. Literature documents that the relationship between them is negative and asymmetric. Building on this, the study considers the linear and quadratic effect of stock index return (CNX Nifty) and examines the changes in implied volatility index (India VIX). The study finds both linear and quadratic CNX Nifty index returns are significant for changes in the level of India VIX. Findings suggest that India VIX provides insurance both for downside market movement and size of the downside movement.

The Impact of Oil Price Shocks on the Stock Return in Emerging Markets: Evidence from the BRICS Group

This paper studies the relationship between oil price and financial market in the emerging economies. It employs the Vector Auto-Regression (VAR) analysis to estimate the impact of the oil price on the stock index's return in the BRICS group. Daily data was obtained from Morgan Stanley Capital International (MSCI) World Index and BP for the oil price. The sample covers the period from 09/05/2005 till 30/03/2012.The results suggest that there is a significant interrelationship between the BRICS markets, except for South Africa, and the oil market. Oil prices do affect BRICS markets but to varying degrees. Brazil and Russia are more responsive to oil shocks in a positive way than the other markets in the study, on the other hand, the Indian and Chinese markets exhibit a negative response to oil shocks. As for the South African’s market, the response is small and slow.

Linkages and co-movement between international stock market returns: Case of Dow Jones Islamic Dubai Financial Market index

Using wavelet techniques (discrete and continuous), this paper is the first attempt to investigate the co-movement dynamics at different time scales or horizons of Islamic Dubai Financial Market (DFM-UAE) index returns with their counterpart regional Islamic indices returns such as GCC index, ASEAN index, Developing Countries index, Emerging Countries Index, and the Global Sukuk. Finally, we examine the impact of the LIBOR on the Islamic DFM-UAE return. Our first finding is that the two markets DFM_UAE, and (GCC and Saudi) are converging, in the long run, to the same level of risk and volatility with the Global Sukuk index. The wavelet analysis based on betas indicates a strong non-homogeneous correlation across scales and for different periods of time. Closer markets tend to suggest a contagion effect showing higher correlation and higher interdependence with a certain time delay. Evidence shows a flight to quality to the less risky Sukuk market mostly during the last financial crisis. The lead–lag analysis tends to indicate that the GCC leads DFM-UAE which leads Sukuk. Finally, this study sheds further light on the important leading impact of the overnight LIBOR on the returns of Islamic stock indices especially during the big changes or under shocks indicating policy implications for portfolio diversification for the international investors. The results are plausible and intuitive and have strong policy implications.

Semiparametric stochastic volatility modelling using penalized splines

Stochastic volatility (SV) models mimic many of the stylized facts attributed to time series of asset returns, while maintaining conceptual simplicity. The commonly made assumption of conditionally normally distributed or Student-t-distributed returns, given the volatility, has however been questioned. In this manuscript, we introduce a novel maximum penalized likelihood approach for estimating the conditional distribution in an SV model in a nonparametric way, thus avoiding any potentially critical assumptions on the shape. The considered framework exploits the strengths both of the hidden Markov model machinery and of penalized B-splines, and constitutes a powerful alternative to recently developed Bayesian approaches to semiparametric SV modelling. We demonstrate the feasibility of the approach in a simulation study before outlining its potential in applications to three series of returns on stocks and one series of stock index returns.

Reuters Sentiment and Stock Returns

Sentiment from more than 3.6 million Reuters news articles is tested in a vector autoregression model framework on its ability to forecast returns of the Dow Jones Industrial Average stock index. We show that Reuters sentiment can explain and predict changes in stock returns better than macroeconomic factors. We further find that negative Reuters sentiment has more predictive power than positive Reuters sentiment. Trading strategies with Reuters sentiment achieve significant outperformance with high success rates as well as high Sharpe ratios.

The Time Varying Relation Between Consumer Confidence and Equities

We examine the impact of changes in consumer confidence measures on future stock index returns. Our analysis is built on the growing understanding that investor sentiment is an important factor in the stock market. By using frequency dependent regression methods, we show that there is a time-varying relation between consumer confidence and stock returns. Higher levels of consumer confidence imply greater returns in the short term but negative returns in the medium term. However, this effect is only observed for the small firm index. Moreover, there is evidence to suggest that consumer confidence is significantly affected by stock returns in reverse causality.

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.

Causal interrelations among market fundamentals: Evidence from the European Telecommunications sector

Abstract This study investigates the interdependence of some major financial variables applied to several European Telecommunications institutions using a multivariate vector autoregressive (VAR) approach. In particular, this paper examines the bilateral relationships among market fundamental variables, such as stock returns, index returns, earnings, capital expenditures and interest rate, with respect to causality and impulse responses, for companies that play major role in their home stock markets. Unlike the fact that the selected Telecommunications companies have many similar characteristics, this research finds that only few of them support common behavior.

The calm after the storm: implied volatility and future stock index returns

This article explores the predictive power of five implied volatility indices for subsequent returns on the corresponding underlying stock indices from January 2000 through October 2013. Contrary to previous research, very low volatility levels appear to be followed by significantly positive average returns over the next 20, 40 or 60 trading days. Rolling trading simulations show that positive adjusted excess returns can be achieved when long positions in the stock indices are taken on days of very low implied volatility. This may be a hint that market inefficiencies exist in some markets, especially outside the USA. The excess returns measured against a buy and hold benchmark are significant for the German and Japanese market when tested with a bootstrap methodology. The results are robust against a broad spectrum of specifications.

New estimates of time-varying currency betas: A trivariate BEKK approach

This paper examines the conditional time-varying currency betas from five developed markets and four emerging markets. We employ a modified trivariate BEKK-GARCH-in-mean model of Engle and Kroner (1995) to estimate the time-varying conditional variance and covariance of returns of stock index, the world market portfolio and changes in bilateral exchange rate between the US dollar and the local currency. It is found that currency betas are more volatile than those of the world market betas. Currency betas in emerging markets are more volatile than those in the developed markets. Moreover, we find evidence of long-memory in currency betas. The usefulness of time-varying currency betas are illustrated by two applications.

Time-varying financial spillovers from the US to frontier markets

We examine US stock index return and volatility spillovers on the mean and volatility of stock index returns of 21 Frontier markets. We entertain potential time variation in spillovers in mean returns by considering a time-varying parameter (TVP) model. Spillovers in volatility are modelled by augmenting a standard GARCH (1, 1) model with current and one-period lagged US conditional volatility effects. The resulting model can be cast in state space form. However, it is not time invariant as the ‘coefficient’ multiplying the state variable (the TVP parameter) is current period US returns. The model is estimated by the Kalman filter. Several important hypotheses of interest are tested using a variety of restricted versions of the general model. An important contribution of the paper is a detailed analysis of the relative contributions from US and own-country lagged effects on both the mean and the volatility of returns in Frontier countries. In summary, our TVP model detects statistically significant time variation in return spillovers, and statistically and quantitatively important volatility spillovers for most Frontier markets. However, the model captures only a small portion of their daily return fluctuations. Most Frontier markets display volatility that is greater both in magnitude and variability relative to the US. Time-varying and quantitatively significant spillovers from the US are important in 13 of the 21 Frontier countries. Quantitative or statistically significant impact of US conditional volatility is found for at least 14 markets. Our results strongly reject the polar null hypotheses of complete market segmentation or complete market integration. Thus, Frontier markets are characterized as neither completely segmented from the US nor completely integrated with it. Results further suggest possible orthogonality in the contributions of current US and lagged own-country returns on Frontier countries’ mean returns.

Interdependence of oil prices and stock market indices: A copula approach

In this paper we study the relationship between the oil price and stock market index of various countries between 1982 and 2007. We exclude oil and gas stock companies from the stock indices to remove the obvious direct linkage. Oil price series are converted into local currency to account for possible exchange rate effects. The method of copula is used to model the general dependence between stock returns and oil price returns. Our findings suggest a weak dependence between oil prices and stock indices for most cases, which are consistent with the results from previous studies. Exceptions are for the stock index returns of large oil consuming and producing countries (United States and Canada), which are shown to have a relatively strong dependence with the oil price series. The introduction of Euro in 1999 altered considerably dependence between oil prices and stock returns.

The impact of the financial crisis on transatlantic information flows: An intraday analysis

We use intraday stock index return data from both sides of the Atlantic during overlapping trading hours to analyze the dynamic interactions between European and US stock markets. We are particularly interested in differences of information transmission before, during, and after the financial crisis of 2007–2009. Our analysis draws on the concept of Rényi transfer entropy to allow for a flexible and model-free empirical assessment of linear as well as non-linear market dependencies. Thereby the importance of extreme (tail) observations of the return distributions is highlighted. The results show significant bi-directional information transfer between the US and the European markets with a dominant flow from the US market. During the crisis dynamic interactions increase. At the same time information flows from European markets increase. The US market does not entirely regain its leading role in the after crisis period.

Wavelet analysis of stock return energy decomposition and return comovement - a case of some central European and developed European stock markets

In this article we investigate comovement of the three Central and Eastern European (CEE) stock markets (Slovenia, the Czech Republic and Hungary) with certain developed European stock markets (Austria, France, Germany and the United Kingdom) through the novel approach of maximal overlap discrete wavelet transform (MODWT). We use two features of MODWT to explore energy decomposition of stock market returns at different time scales and to apply methodology of [29] to study comovement between investigated stock markets. We show that most of the energy (variability) of stock market return series is captured by scale 1 (which correspond to 2–4 days return dynamics) and scale 2 (which correspond to 4–8 days return dynamics) MODWT coefficients. MODWT details are used to show that comovement between stock markets is scale- dependent and declines from raw (daily) return series to first- and second-scale reconstructed return series. The findings of the survey then have important implications for foreign financial investors who already hold international portfolios that exactly replicate those of non-Czech or non- Hungarian stock markets: international investing in the Czech or Hungarian stock markets with investment horizons corresponding to scale 2 (4 to 8 days) brings greater international diversification benefits than shorter (2 to 4 day horizon) international trading diversification strategies. The Slovenian stock market differs from the Czech and Hungarian markets also in this respect, as when the scale is increased the benefits of diversification are reduced. We also find that the volatility of Slovenian stock index returns is less synchronized with other observed stock return series. Interestingly, the Czech and Slovenian stock markets seem to comove with the Austrian stock market to a greater extent than with other developed stock markets.

Discrete stochastic autoregressive volatility

We use Markov chain methods to develop a flexible class of discrete stochastic autoregressive volatility (DSARV) models. Our approach to formulating the models is straightforward, and readily accommodates features such as volatility asymmetry and time-varying volatility persistence. Moreover, it produces models with a low-dimensional state space, which greatly enhances computational tractability. We illustrate the proposed methodology for both individual stock and stock index returns, and show that simple first- and second-order DSARV models outperform generalized autoregressive conditional heteroscedasticity and Markov-switching multifractal models in forecasting volatility.

Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models

We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear, non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent Metropolis-Hastings algorithm or in importance sampling. Our method provides a computationally more efficient alternative to several recently proposed algorithms. We present extensive simulation evidence for stochastic intensity and stochastic volatility models based on Ornstein-Uhlenbeck processes. For our empirical study, we analyse the performance of our methods for corporate default panel data and stock index returns.(This paper is an updated version of the paper that appeared earlier as Barra, I., Hoogerheide, L.F., Koopman, S.J., and Lucas, A. (2013) Joint Independent Metropolis-Hastings Methods for Nonlinear Non-Gaussian State Space Models. TI Discussion Paper 13-050/III. Amsterdam: Tinbergen Institute.)

Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models

The purpose of this paper is to develop and identify the best hybrid model to predict stock index returns. We develop three different hybrid models combining linear ARIMA and non-linear models such as support vector machines (SVM), artificial neural network (ANN) and random forest (RF) models to predict the stock index returns. The performance of ARIMA-SVM, ARIMA-ANN and ARIMA-RF are compared with performance of ARIMA, SVM, ANN and RF models. The various competing models are evaluated in terms of statistical metrics and trading performance criteria via a trading strategy. The analysis shows that the hybrid ARIMA-SVM model is the best forecasting model to achieve high forecast accuracy and better returns.

Did the contagion effect exist? Evidence from Abu Dhabi, Jordan and America

This article aims to test the contagion effect between the stock markets of Abu Dhabi, Jordan and America. The Lagrange multiplier (LM) principle for causality in variance test is used in this study. Four American stock indexes, Dow Jones Industrial Average, NASDAQ Composite, RUSSELL 2000, and PHLX Semiconductor Sector Index, are in this study. The testing results of the four major American stock price indexes and the Jordan stock index (Amman) are significant. The testing results of the four American stock price indexes and the Abu Dhabi stock index (ADX) are also significant. This study finds that the variances of returns of four major American stock price indexes have the contagion effects on the variances of stock index returns of Jordan and Abu Dhabi.

RETURNS AND CORRELATIONS OF INDIAN SECTOR STOCK INDICES

RETURNS AND CORRELATIONS OF INDIAN SECTOR STOCK INDICES