Stock Index Returns Research Articles

Modeling Maximum Entropy Distributions for Financial Returns by Moment Combination and Selection

In empirical finance, conditional distributions of financial returns are often established by specifying the standardized error distributions of GARCH-type models. In this article, we apply the maximum entropy (MaxEnt) approach and propose a moment combination and selection method to explore this distribution-building problem. We demonstrate that this framework is useful for unifying and comparing existing distribution specifications, generating more suitable distribution spec-ifications, and shedding light on the roles of different moments in the distribution-building process. We also show the applicability of our method to real data by means of an empirical study on stock index returns.

Realized stochastic volatility with leverage and long memory

The daily return and the realized volatility are simultaneously modeled in the stochastic volatility model with leverage and long memory. The dependent variable in the stochastic volatility model is the logarithm of the squared return, and its error distribution is approximated by a mixture of normals. In addition, the logarithm of the realized volatility is incorporated into the measurement equation, assuming that the latent log volatility follows an Autoregressive Fractionally Integrated Moving Average (ARFIMA) process to describe its long memory property. The efficient Bayesian estimation method using Markov chain Monte Carlo method (MCMC) was proposed and implemented in the state space representation. Model comparisons are performed based on the marginal likelihood, and the volatility forecasting performances are investigated using S&P500 stock index returns.

Estimating Time-Varying Currency Betas: New Evidence from Nine Developed and Emerging Markets

This paper examines the conditional time-varying currency betas from five developed markets and four emerging markets. We employ BEKK multivariate GARCH models 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.

Flexible Modelling of Dependence in Volatility Processes

This paper proposes a novel volatility model that draws from the existing literature on autoregressive stochastic volatility models, aggregation of autoregressive processes, and Bayesian nonparametric modelling to create a dynamic SV model that can explain long range dependence. The volatility process is assumed to be the aggregate of autoregressive processes where the distribution of the autoregressive coefficients is modelled using a flexible Bayesian approach. The model provides insight into the dynamic properties of the process. An efficient algorithm is defined which uses recently proposed adaptive Monte Carlo methods. The proposed model is applied to the daily returns of stock indices.

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.

Modeling and simulation of the open-end equity mutual fund market in Taiwan by using self-organizing map

This research applies artificial intelligence (AI) of unsupervised learning self-organizing map neural network (SOM-NN) to establish a model to select the superior funds. This research period is from year 2000 to 2010 and picks 100 domestic equity mutual funds as study object. This research used 30days prior to the beginning of each month’s prior 30days, 60days, 90days on fund’s net asset value and the Taiwan Weighted Stock Index (TAIEX) return as the fund’s relative performance evaluation indicators classified by month. Finally, based on the superior rate or the average return rate, this research select the superior funds and simulate investment transactions according to this model.The empirical results show that using the mutual fund’s net asset value and the TAIEX’s relative return as SOM-NN input variables not only finds out the superior fund but also has a good predictive ability. Applying this model to simulate investment transactions will be better than the random trading model and market. The experiments also found that the investment simulation of a three-month interval has the highest profitability. The model operation suggests that it is more suitable for short-term and medium-term investment. This research can assist investors in making the right investment decisions while facing rapid financial environment changes.

Study and Analysis of Fat-Tail Risk and Other Statistical Properties of Indian Stock Market

In this paper, the existence of fat-tail risk and other statistical properties of the Indian Stock Market are studied and analyzed. A fat-tail risk in financial markets refers to extreme swings in the markets. The daily data of S&P CNX Nifty during the years 2001 to 2009 is selected for experiment. The actual distribution of returns are examined which exhibit the statistical characteristics of leptokurtosis and the frequencies of distributions falling into certain ranges of daily returns are compared. The daily return of stock index is forecasted by Fuzzy Time Series Model. The statistical study has shown that the Indian Stock Market has more volatile or risky trading environment than normal distribution.

Analysing the influence of headline news on the stock market in Japan

This research analysed the relationship between headlines in the news and the stock market in Japan through text mining technology. As a result of intensive analyses, we found a significant relationship between the stock index return changes and the negative or positive news classified by keywords. At the same time, we also found that the measure of constructing a keyword list has a significant impact on the performance of this classification.

WAVELET ANALYSIS OF THE CRISIS EFFECTS IN STOCK INDEX RETURNS

Abstract. The article considers local peculiarities of the world stock indices in 2007–first half 2012 and offers a comparative analysis of the indices. The research is based on the time series decomposition of the most liquid European, Asian, USA, and Brazil stock indices. The aim of the research was to localize and describe the crisis effects on index dynamics in time and scope by using wavelet decomposition techniques. This approach allows to identify clusters of stock indices and to study their common and individual features. The window transformation method is used for the investigation of index returns’ volatility dynamics. This method allows to investigate the nature and characteristics of the identified critical waves in the stock markets studied. The combined application of wavelet transform, neural networks and SSA is proposed for the prediction purposes. This approach is used for the return forecast of the German index DAX30.Key words: economic crisis, stock index returns, the wavelet transform, neural networks, SSA

Moving the Goalposts: Subjective Performance Benchmarks and the Aumann-Serrano Measure of Riskiness

This paper parameterizes the well-known Aumann and Serrano (2008) measure to explicitly allow the riskiness of financial instruments to be examined relative to benchmark levels of return other than zero. Using real data, we demonstrate that the ordering of two financial instruments in terms of their relative riskiness may change even with only small changes in the benchmark level of return. We then show that one asset may be (uniformly) riskier than another in the sense that every member of this generalized class ranks the two assets in the same way, and that this occurs only when the Laplace transforms of the assets can be ordered over a compact subset of the positive real line. A statistical test for this Laplace order is proposed, and a simulation study shows that this test is reliable in finite samples. Finally, the proposed methodology is illustrated using data on global stock index returns.

Modeling the Conditional Distribution of Daily Stock Index Returns: An Alternative Bayesian Semiparametric Model

This article introduces a new family of Bayesian semiparametric models for the conditional distribution of daily stock index returns. The proposed models capture key stylized facts of such returns, namely, heavy tails, asymmetry, volatility clustering, and the “leverage effect.” A Bayesian nonparametric prior is used to generate random density functions that are unimodal and asymmetric. Volatility is modeled parametrically. The new model is applied to the daily returns of the S&P 500, FTSE 100, and EUROSTOXX 50 indices and is compared with GARCH, stochastic volatility, and other Bayesian semiparametric models.

Seasonal affective disorder: onset and recovery

Kamstra et al. (2003, 2009, 2012) offer a seasonal affective disorder hypothesis to explain variations in the daily returns of stock indices. We examine Kamstra et al. (2012) new variable called SAD onset/recovery. The analysis reveals concerns for the validity of the SAD hypothesis.

Evaluating the Forecasting Performance of GARCH Models. Evidence from Romania

Modeling and forecasting the volatility of stock markets has been one of the major topics in financial econometrics in the last years. The aim of the study is to evaluate the forecasting performance of GARCH-type models in terms of their in-sample and out-of-sample forecasting accuracy in the case of Romanian stock market. We use daily stock index return data from Romania (BET index) covering the period 09/03/2001 to 02/29/2012.We find that the TGARCH model is the most successful in forecasting the volatility of BET index. Our results have important significance in the calculation of Value-at-Risk (VaR) and in risk management process.

Cross‐sectional analysis of emerging market volatility index (India VIX) with portfolio returns

PurposeEarlier studies establish a positive relationship between volatility index (VIX) and the stock index returns. These studies are mainly restricted to developed markets and research in this regard in emerging markets is scarce. The purpose of this paper is to fill this gap.Design/methodology/approachThe paper studies the direct and cross‐sectional relationship of India VIX in relation to three important parameters: viz., stock beta, market to book value of equity and market capitalization. The paper constructs value weighted portfolio sorted on the basis viz., stock beta, market to book value of equity and market capitalization. The paper employs three‐factor multiple regression to find out the results.FindingsThe paper finds that India VIX has a positive and significant relationship with the returns of the value‐weighted high‐low portfolios sorted on the basis of the above parameters. The paper examines the behavior of India VIX in the presence of the above two parameters. The India VIX yields a positive and significant relationship with the above sorted portfolio returns.Research limitations/implicationsIndia VIX was recently introduced in November, 2007 and therefore the research is expected to suffer from small sample bias.Practical implicationsThe findings suggest India VIX is a distinct risk factor capable of predicting the price discovery mechanism of the market.Originality/valueIn the rapidly expanding emerging markets the introduction of Volatility Index is a recent phenomenon. Research in this regard is scarce, particularly in the area of finding predictive ability of the Volatility Index. This research is in this direction and would definitely help the market regulators and policy‐makers with their understanding of the market and market direction. It would help them to correct the market imbalances and avert crisis, which has been recently witnessed.

Conditional correlations and volatility spillovers between crude oil and stock index returns

This paper investigates the conditional correlations and volatility spillovers between the crude oil and financial markets, based on crude oil returns and stock index returns. Daily returns from 2 January 1998 to 4 November 2009 of the crude oil spot, forward and futures prices from the WTI and Brent markets, and the FTSE100, NYSE, Dow Jones and S&P500 stock index returns, are analysed using the CCC model of Bollerslev (1990), VARMA-GARCH model of Ling and McAleer (2003), VARMA-AGARCH model of McAleer, Hoti, and Chan (2008), and DCC model of Engle (2002). Based on the CCC model, the estimates of conditional correlations for returns across markets are very low, and some are not statistically significant, which means the conditional shocks are correlated only in the same market and not across markets. However, the DCC estimates of the conditional correlations are always significant. This result makes it clear that the assumption of constant conditional correlations is not supported empirically. Surprisingly, the empirical results from the VARMA-GARCH and VARMA-AGARCH models provide little evidence of volatility spillovers between the crude oil and financial markets. The evidence of asymmetric effects of negative and positive shocks of equal magnitude on the conditional variances suggests that VARMA-AGARCH is superior to VARMA-GARCH and CCC.

Contagio financiero entre economías : análisis exploratorio desde la econometría. Caso Colombia-Estados Unidos.

El artículo, estudia las relaciones de dependencia entre los mercados financieros internacionales, indagando sobre el efecto del contagio financiero entre dos series de retornos de índices accionarios: el Dow Jones (DJ) y el índice general de la Bolsa de Valores de Colombia (IGBC). Lo anterior, gracias a que Estados Unidos es el principal país con el que Colombia mantiene un fuerte nexo comercial y económico. Se hará uso de información histórica de carácter público, comprendida entre enero 1 de 2007 y diciembre 31 de 2010, proveniente de fuentes objetivas y reconocidas en el mercado. Se desarrollará un ejercicio de tipo econométrico, en el cual se establece el nivel de relacionamiento existente entre los dos índices mencionados. El principal aporte de este trabajo es la implementación de la econometría como técnica para el análisis del contagio, que permite inferir las relaciones de causalidad entre los mercados y determinar modelos de predicción robustos que generan un mayor acervo informativo e interpretativo sobre la dinámica implícita en los retornos de los diferentes activos financieros.

Density prediction of stock index returns using GARCH models: Frequentist or Bayesian estimation?

Using GARCH models for density prediction of stock index returns, a comparison is provided between frequentist and Bayesian estimation. No significant difference is found between qualities of whole density forecasts, whereas the Bayesian approach exhibits significantly better left-tail forecast accuracy.

Value Matters: Predictability of Stock Index Returns

We present a simple dynamical model of stock index returns which is grounded on the ability of the Cyclically Adjusted Price Earning (CAPE) valuation ratio devised by Robert Shiller to predict long-horizon performances of the market. More precisely, we discuss a discrete time dynamics in which the return growth depends on three components: i) a momentum component, naturally justified in terms of agents' belief that expected returns are higher in bullish markets than in bearish ones, ii) a fundamental component proportional to the logarithmic CAPE at time zero. The initial value of the ratio determines the reference growth level, from which the actual stock price may deviate as an effect of random external disturbances, and iii) a driving component which ensures the diffusive behaviour of stock prices. Under these assumptions, we prove that for a sufficiently large horizon the expected rate of return and the expected gross return are linear in the initial logarithmic CAPE, and their variance goes to zero with a rate of convergence consistent with the diffusive behaviour. Eventually this means that the momentum component may generate bubbles and crashes in the short and medium run, nevertheless the valuation ratio remains a good reference point of future long-run returns.

Quantile Regression Analysis of the Asymmetric Return‐Volatility Relation

We use quantile regression to investigate the short‐term return‐volatility relation between stock index returns and changes in implied volatility index. Neither the leverage hypothesis nor the volatility feedback hypothesis effectively explains the asymmetric return‐volatility relation. Instead, behavioral explanations, such as the affect and representativeness heuristics, are supported by our results, particularly in the short‐term; the affect heuristic plays an important role. Moreover, in the context of an extreme volatility change distribution, the affect heuristic and time‐pressure dominate. Thus, we observe strong negative and asymmetric relations between each volatility index and its corresponding stock market index. The asymmetry increases monotonically from the median quantile to the uppermost quantile (i.e., 95%); therefore, ordinary least squares (OLS) regression underestimates this relation at upper quantiles. Additionally, the VIX presents the highest asymmetric return‐volatility relation, followed by the VSTOXX, VDAX, and VXN. Finally, the observed asymmetry is more pronounced with the new volatility index measure than with the old, at‐the‐money volatility index measure. © 2012 Wiley Periodicals, Inc. Jrl Fut Mark 33:235–265, 2013

Smile in Motion: An Intraday Analysis of Asymmetric Implied Volatility

We present a new method to measure the intraday relationship between movements of implied volatility smiles and stock index returns. It exploits a specific characteristic of the smile profile in high-frequency data. Using transaction data for EuroStoxx 50 options from 2000 to 2011 and DAX options from 1995 to 2011 (14 million transactions), we find that the intraday evolution of volatility smiles is generally not consistent with traders' rules of thumb such as the sticky strike or sticky delta rule. On average, the impact of index return on implied volatility is 1.3 to 1.5 times stronger than the sticky strike rule predicts. The main factor driving variations of the adjustment factor is the index return. Our results have implications for option valuation, hedging and the understanding of the leverage effect.