Market Return Series Research Articles

Efficience Informationnelle faible des Marchés Boursiers Africains

In order to study the performance capacity of African countries markets, it is necessary to analyze the daily results of stock indices. For a period of six years, and based on four types of tests namely: Autocorrelation tests, runs tests, unit root and normality tests, the results obtained in a period of 1674days (from03/04/2013 to 18/12/2020) have shown and reported a mixed production of different indications. On the other hand, and at the level of the weak sense of efficiency, the African market return series indicate the low efficiency of the market. Overall, it is inferred that in African countries, daily stock index results do not follow a random walk; So it is concluded that due to the inefficiency of the market, insiders can get profit streams from arbitrage. On the other side, the concept of efficiency designates the right foot of the theory of modern finance; As a result, one can only confirm the existence of efficiency in the market if it deciphers and manifests all the information available and translated by each financial asset; in other words, the presence of efficiency expresses the capacity of the market to reconstitute its equilibrium and its persistence while allowing a better allocation of resources to investors. In other words, the presence of efficiency expresses the capacity of the market to reconstitute its equilibrium and persistence while allowing a better allocation of resources to investors. It is in the interest of explaining the doubt of the speed of information reflected in each stock exchange price that FAMA (1970) proceeded to the deconstruction of the efficiency in three sub-forms, namely: the weak form of the efficiency, the semi-strong form and finally the strong form

RELATIONSHIP BETWEEN FOREIGN DIRECT INVESTMENT AND STOCK MARKET DEVELOPMENT IN A SMALL SOUTHERN AFRICA ECONOMY

This paper investigates the relationship between foreign direct investment and stock market development in a small southern African economy. Specifically, the paper analyses long-run, short-run and causal relationships between foreign direct investment and stock market development in Eswatini for the 1990 to 2018 periods. Results of preliminary analyses of the variable show existence of positive skewness, fat-tailed, non-normal distribution, and I(1) order of integration for the foreign direct investment and stock market return series. Estimates from the ARDL model indicate evidence of a positive and statistically insignificant long-run relationship between foreign direct investment and stock market development in the kingdom of Eswatini. But in the short-run, there exist no relationship between foreign direct investment and stock market development in Eswatini. Estimates from Granger causality test do not show any evidence of causal relationship between foreign direct investment and stock market development in Eswatini. We recommend amongst others that capital market authorities should establish measures to increase the number of listings in the market so as boost investment options. In addition, there should be massive domestic investor-education on benefits of financing projects with a combination capital market funds, which has long-term tenor, and money market funds, which are of short-term nature.

Efficiency in Agricultural Spice Commodities Futures Markets in India

This paper is to analyze the efficiency of agricultural spices commodity markets by assessing the relationships between futures prices and spot market prices of major agricultural spices commodities in India during the sample period of January 2010 through December 2014. The econometric tools like Unit root tests, Johansen co-integration test, and Pairwise Granger Causality tests were employed in the study. The Augmented Dickey-Fuller tests and Phillips-Perron tests employed in the study proved that both the selected metals markets were stationary series, Johansen co-integration test proved selected metals spot and future markets are co-integrating each other and the Granger Causality test proved uni-causality relationships among these markets between spot and future market return series during the study period.

Risk and Dependence Analysis of Australian Stock Market - The Case of Extreme Value Theory

The quantification of risk and dependence are major components of financial risk modelling. Financial risk modelling frequenty uses the assumption of a normal distribution when considereing the return series which makes modelling easy but is inefficient if the data is not normally distributed or if it exhibits extreme tails. When dealing with extreme financial events to quantify extreme market risk, Extreme Value Theory (EVT) proves to be a natural statistical modelling technique of interest. Estimation of tail dependence between financial assets plays a vital role in various aspects of financial risk modelling including portfolio theory and hedging amongst applications. Extreme Value Theory (EVT) provides well established methods for considering univariate and multivariate tail distributions which are useful for forecasting financial risk or modelling the tail dependence of risky assets. In this paper we focus on the extreme risk and dependence analysis of the ASX-All Ordinaries (Australian) stock market using using univariate and multivariate EVT based methods. The empirical evidence shows that EVT can be successfully applied to financial market return series for predicting daily VaR using a GARCH(1,1) and EVT based dynamic approach. We also use nonparametric measures based on bivariate EVT to investigate asymptotic dependence and estimate the degree of tail dependence of the ASX-All Ordinaries daily returns with four other international markets, viz., the S&P-500, Nikkei-225, DAX-30 and Heng-Seng for both right and left tails of the return distribution in extreme quantiles. It is investigated whether the asymptotic dependence between these markets is related to the heteroskedasticity present in the logarithmic return series using GARCH filters. The empirical evidence from bivariate EVT methods show that the asymptotic dependence between the extreme tails of the stock markets does not necessarily exist and rather can be associated with the heteroskedasticity present in the financial time series of the various stock markets.

MULTIFRACTALS IN WESTERN MAJOR STOCK MARKETS HISTORICAL VOLATILITIES IN TIMES OF FINANCIAL CRISIS

In this paper, the generalized Hurst exponent is used to investigate multifractal properties of historical volatility (CHV) in stock market price and return series before, during and after 2008 financial crisis. Empirical results from NASDAQ, S&P500, TSE, CAC40, DAX, and FTSE stock market data show that there is strong evidence of multifractal patterns in HV of both price and return series. In addition, financial crisis deeply affected the behavior and degree of multifractality in volatility of Western financial markets at price and return levels.

Dynamic VaR Measurement of Gold Market with SV-T-MN Model

VaR (Value at Risk) in the gold market was measured and predicted by combining stochastic volatility (SV) model with extreme value theory. Firstly, for the fat tail and volatility persistence characteristics in gold market return series, the gold price return volatility was modeled by SV-T-MN (SV-T with Mixture-of-Normal distribution) model based on state space. Secondly, future sample volatility prediction was realized by using approximate filtering algorithm. Finally, extreme value theory based on generalized Pareto distribution was applied to measure dynamic risk value (VaR) of gold market return. Through the proposed model on the price of gold, empirical analysis was investigated; the results show that presented combined model can measure and predict Value at Risk of the gold market reasonably and effectively and enable investors to further understand the extreme risk of gold market and take coping strategies actively.

Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility

Realized measures employing intra-day sources of data have proven effective for dynamic volatility and tail-risk estimation and forecasting. Expected shortfall (ES) is a tail risk measure, now recommended by the Basel Committee, involving a conditional expectation that can be semi-parametrically estimated via an asymmetric sum of squares function. The conditional autoregressive expectile class of model, used to implicitly model ES, has been extended to allow the intra-day range, not just the daily return, as an input. This model class is here further extended to incorporate information on realized measures of volatility, including realized variance and realized range (RR), as well as scaled and smoothed versions of these. An asymmetric Gaussian density error formulation allows a likelihood that leads to direct estimation and one-step-ahead forecasts of quantiles and expectiles, and subsequently of ES. A Bayesian adaptive Markov chain Monte Carlo method is developed and employed for estimation and forecasting. In an empirical study forecasting daily tail risk measures in six financial market return series, over a seven-year period, models employing the RR generate the most accurate tail risk forecasts, compared to models employing other realized measures as well as to a range of well-known competitors.

Financial Integration and Portfolio Diversification: Evidence from CIVETS Stock Markets

This paper investigates the extent of financial integration among a new group of six frontier markets called “CIVETS” by utilizing the multivariate GARCH framework of Engle and Kroner [1]. These countries are expected to show sustainable growth in productivity and domestic consumption over the next decade and are considered as potential corridor for the international investor from portfolio diversification point of view. We utilize weekly stock market return series of all the CIVIETS nations, and results exhibit significant return and volatility spillovers among all the markets under investigation. Our results reveal that there are significant linkages among CIVETS stock markets during the time of our analysis. However, the direction of relationship is asymmetric depending on the countries in the model. We believe, CIVIETS stock markets have full potential of being the future investment targets worldwide.

Risk-return tradeoff in Chinese stock markets: some recent evidence

Purpose – The purpose of this paper is to pay more attention to four different research questions at least. One is that this study intends to explore the changes of the risk-return relationship over time, because the institutions and environment have changed a lot and might tend to influence the risk-return regime in the Chinese stock markets. The second question is whether there is any difference for the risk-return relationship between Shanghai and Shenzhen stock markets. The third question is to compare the similarities and dissimilarities of the risk-return tradeoff for different frequency data. The fourth question is to compare the explanation power of different GARCH-M type models which are all widely used in exploring the risk-return tradeoff. Design/methodology/approach – This paper investigates the risk-return tradeoff in the Chinese emerging stock markets with a sample including daily, weekly and monthly market return series. A group of variant specifications of GARCH-M type models are used to test the risk-return tradeoff. Additionally, some diagnostic checks proposed by Engle and Ng (1993) are used in this paper, and this will help to assess the robustness of different models. Findings – The empirical results show that the dynamic risk-return relationship is quite different between Shanghai and Shenzhen stock markets. A positive and statistically significant risk-return relationship is found for the daily returns in Shenzhen Stock Exchange, while the conditional mean of the stock returns is negatively related to the conditional variance in Shanghai Stock Exchange. The risk-return relationship usually becomes much weaker for the lower frequency returns in both markets. A further study with the sub-samples finds a positive and significant risk-return trade-off for both markets in the second stage after July 1, 1999. Originality/value – This paper extends the existing related researches about the Chinese stock markets in several ways. First, this study uses a longer sample to investigate the relationship between stock returns and volatility. Second, this study estimates the returns and volatility relationship with different frequency sample data together. Third, a group of variant specifications of GARCH-M type models are used to test the risk-return tradeoff. In particular, the author employs the Component GARCH-M model which is relatively new in this line of research. Fourth, this study investigates if there is any structural break affecting the risk-return relationship in the Chinese stock markets over time.

Long memory in international financial markets trends and short movements during 2008 financial crisis based on variational mode decomposition and detrended fluctuation analysis

The purpose of this study is to investigate long-range dependence in trend and short variation of stock market price and return series before, during, and after 2008 financial crisis. Variational mode decomposition (VMD), a newly introduced technique for signal processing, is adopted to decompose stock market data into a finite set of modes so as to obtain long term trends and short term movements of stock market data. Then, the detrended fluctuation analysis (DFA) and range scale (R/S) analysis are used to estimate Hurst exponent in each variational mode obtained from VMD. For both price and return series, the empirical results from twelve international stock markets show evidence that long term trends are persistent, whilst short term variations are anti-persistent before, during, and after 2008 financial crisis.

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.

Intra-Market Linkages Among Civets Stock Markets: A New Frontier for Investments

This paper utilizes the multivariate GARCH framework of Engle and Kroner (1995) to examine the return and volatility spillovers among a new group of six frontier markets called ‘CIVETS’. These markets are considered to be the future hosts of investments due to their huge potential and abundance of resources. The analysis of weekly stock market return series revealed that these markets have significant return and volatility spillovers among each other. These findings suggest that the portfolio investors who invest in emerging and frontier markets for better returns should take into account the correlation of risk and returns among CIVETS stock markets. The diversification benefits should be assessed keeping in view the extent of inter-market linkages of Colombia, Indonesia, Vietnam, Egypt, Turkey and South Africa.

Bayesian estimation of smoothly mixing time-varying parameter GARCH models

Smoothly time-varying (TV) GARCH models via an asymmetric logistic function mechanism are proposed, which are incorporated into the conditional volatility equation for capturing smooth structural breaks in the volatility of financial time series. The proposed models allow smooth transitions of varying “speed” between multiple, persistent regimes. A Bayesian computational method is employed to identify the locations of smooth structural transitions, and for estimation and inference, simultaneously accounting for heteroskedasticity. An informative prior is proposed to help ensure identification and allow accurate inference. The proposed methods are illustrated using simulated data, and an empirical study provides evidence for significant improvements in fit for the proposed smooth asymmetric time-varying volatility TV-GARCH models in two international stock market return series. A forecast study shows the proposed models significantly add to forecast accuracy for both volatility and Value-at-Risk.

EVT and tail-risk modelling: Evidence from market indices and volatility series

Value-at-Risk (VaR) has become the universally accepted risk metric adopted internationally under the Basel Accords for banking industry internal control, capital adequacy and regulatory reporting. The recent extreme financial market events such as the Global Financial Crisis (GFC) commencing in 2007 and the following developments in European markets mean that there is a great deal of attention paid to risk measurement and risk hedging. In particular, to risk indices and attached derivatives as hedges for equity market risk. The techniques used to model tail risk such as VaR have attracted criticism for their inability to model extreme market conditions. In this paper we discuss tail specific distribution based Extreme Value Theory (EVT) and evaluate different methods that may be used to calculate VaR ranging from well known econometrics models of GARCH and its variants to EVT based models which focus specifically on the tails of the distribution. We apply Univariate Extreme Value Theory to model extreme market risk for the FTSE100 UK Index and S&P-500 US markets indices plus their volatility indices. We show with empirical evidence that EVT can be successfully applied to financial market return series for predicting static VaR, CVaR or Expected Shortfall (ES) and also daily VaR and ES using a GARCH(1,1) and EVT based dynamic approach to these various indices. The behaviour of these indices in their tails have implications for hedging strategies in extreme market conditions.

Extreme market risk and extreme value theory

The phenomenon of the occurrence of rare yet extreme events, “Black Swans” in Taleb's terminology, seems to be more apparent in financial markets around the globe. This means there is not only a need to design proper risk modelling techniques which can predict the probability of risky events in normal market conditions but also a requirement for tools which can assess the probabilities of rare financial events; like the recent global financial crisis (2007–2008). An obvious candidate, when dealing with extreme financial events and the quantification of extreme market risk is extreme value theory (EVT). This proves to be a natural statistical modelling technique of relevance. Extreme value theory provides well-established statistical models for the computation of extreme risk measures like the return level, value at risk and expected shortfall. In this paper we apply univariate extreme value theory to model extreme market risk for the ASX-All Ordinaries (Australian) index and the S&P-500 (USA) Index. We demonstrate that EVT can be successfully applied to financial market return series for predicting static VaR, CVaR or expected shortfall (ES) and expected return level and also daily VaR using a GARCH(1,1) and EVT based dynamic approach.

Heat Waves or Meteor Showers: Empirical Evidence from Indian Stock Market

In order to study the volatility spillovers / the transfer of volatilities from spot and futures markets for the period 1st January 2001 to 30th November 2005 with high frequency data i.e., one minute intervals, we have used GARCH models to compute volatilities and VAR models for the returns of different markets and for the volatilities. It is evident that, these VAR models for the volatilities can exhibit the nature of the change in volatility.In a heat wave, the conditional variance of the returns in spot (futures) market depends only upon the past shocks in the given market. For meteor showers, the impact of shocks on spot (futures) markets are transferred from other i.e., futures (spot) markets. With the VAR (1)-GARCH (1,1) analysis, we found that both series are I(1) and that a bi-directional relationship exists between the spot and future market return series. Empirically it is evident that both heat waves and meteor showers exist in Indian spot and futures markets.

China’s Stock Market Integration with a Leading Power and a Close Neighbor

Current integration and co-movement among international stock markets has been boosted by increased globalization of the world economy, and profit-chasing capital surfing across borders. With a reputation as the fastest growing economy in the world, China’s stock market has continued gaining momentum during recent years and incurred growing attention from academicians, as well as practitioners. Taking into account economic and geographical considerations, the US and Hong Kong are considerably the most comparable stock markets to China. As the usual vector error correction model (VECM) could overlook the long memory feature of cointegration residual series, which can in turn exert bias on the resulting inferences, we chose to employ a fractionally integrated VECM (FIVECM) in this paper to investigate the long-term cointegration relations binding China’s stock market to the aforementioned stock markets. In addition, by augmenting the FIVECM with multivariate GARCH model, the return transmission and volatility spillover between market return series were revealed simultaneously. Our empirical results show that China’s stock market is fractionally cointegrated with the two markets, and it appears that China’s stock market has stronger ties with its neighboring Hong Kong market than with the world superpower, the US market.

China’s Stock Market Integration with a Leading Power and a Close Neighbor

Current integration and co-movement among international stock markets has been boosted by increased globalization of the world economy, and profit-chasing capital surfing across borders. With a reputation as the fastest growing economy in the world, China’s stock market has continued gaining momentum during recent years and incurred growing attention from academicians, as well as practitioners. Taking into account economic and geographical considerations, the US and Hong Kong are considerably the most comparable stock markets to China. The usual vector error correction model (VECM) could overlook the long memory feature of cointegration residual series, which can in turn exert bias on the resulting inferences. To overcome its limitations, we employ a fractionally integrated VECM (FIVECM) in this paper to investigate the long-term cointegration relations binding China’s stock market to the aforementioned stock markets. In addition, by augmenting the FIVECM with multivariate GARCH model, the return transmission and volatility spillover between market return series were revealed simultaneously. Our empirical results show that China’s stock market is fractionally cointegrated with the two markets, and it appears that China’s stock market has stronger ties with its neighboring Hong Kong market than with the world superpower, the US market.

An Empirical Analysis of Foreign Institutional Investment and Stock Market Returns in India

Augmented Dickey Fuller and Phillips-Perron tests were employed to examine the stationarity of both net foreign institutional investment and NSE market return series. Besides, Instantaneous Granger Causality test was employed to examine the contemporaneous relationship between net foreign institutional investment flows and equity market returns in India for the pre-global financial crisis and during a crisis period. By and large, our analysis reveals that there is an evidence of negative feedback trading hypothesis and positive feedback trading hypothesis by foreign investors before the global financial crisis period and during a crisis period respectively. This implies that foreign institutional investment acts as smoothening effect and destabilizing forces before and during the crisis period respectively. However, such positive feedback trading strategies from foreign institutional investors seem to be rational during the period of global financial crisis.

Volatility Forecasting with Double Markov Switching GARCH Models

This paper investigates inference and volatility forecasting using a Markov switching heteroscedastic model with a fat-tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time series. The motivation for extending the Markov switching GARCH model, previously developed to capture mean asymmetry, is that the switching variable, assumed to be a first-order Markov process, is unobserved. The proposed model extends this work to incorporate Markov switching in the mean and variance simultaneously. Parameter estimation and inference are performed in a Bayesian framework via a Markov chain Monte Carlo scheme. We compare competing models using Bayesian forecasting in a comparative value-at-risk study. The proposed methods are illustrated using both simulations and eight international stock market return series. The results generally favor the proposed double Markov switching GARCH model with an exogenous variable.