Stock Market Index Data Research Articles

Real‐Time Monitoring for Explosive Financial Bubbles

We propose new methods for the real‐time detection of explosive bubbles in financial time series. Most extant methods are constructed for a fixed sample of data and, as such, are appropriate only when applied as one‐shot tests. Sequential application of these tests, declaring the presence of a bubble as soon as one of these statistics exceeds the one‐shot critical value, would yield a detection procedure with an unknown false‐positive rate likely to be far in excess of the nominal level. Our approach sequentially applies the one‐shot tests of Astill et al. (), comparing sub‐sample statistics calculated in real time during the monitoring period with the corresponding sub‐sample statistics obtained from a prior training period. We propose two procedures: one based on comparing the real‐time monitoring period statistics with the maximum statistic over the training period, and another that compares the number of consecutive exceedances of a threshold value in the monitoring and training periods, the threshold value obtained from the training period. Both allow the practitioner to determine the false‐positive rate for any given monitoring horizon, or to ensure that this rate does not exceed a specified level by setting a maximum monitoring horizon. Monte Carlo simulations suggest that the finite‐sample false‐positive rates lie close to their theoretical counterparts, even in the presence of time‐varying volatility and serial correlation in the shocks. The procedures are shown to perform well in the presence of a bubble in the monitoring period, offering the possibility of rapid detection of an emerging bubble in a real‐time setting. An empirical application to monthly stock market index data is considered.

Return and Volatility Spillovers Effects: Study of Asian Emerging Stock Markets

Abstract This paper examines the extent of contagion and interdependence across the six Asian emerging countries stock markets (e.g., Bangladesh, China, India, Malaysia, the Philippine, and South Korea) and then try to quantify the extent of the Asian emerging market fluctuations which are described by intra-regional contagion effect. These markets experienced both fast growth and key upheaval during the sample period, and thus, provide potentially rich information on the nature of border market interactions. Using the daily stock market index data from January 2002 to December 2016 (breaking the 15 years data set into three sub periods; pre-crisis, crisis, and post crisis periods); particularly make attention to the global financial crisis of 2007∼2008. The return and volatility spillovers are modeled through the GARCH (generalized autoregressive conditional heteroscedasticity), pairwise Granger causality tests, and the forecast error variance decomposition in a generalized VAR (vector auto regression) models. This paper shows that volatility and return spillovers behave very differently over time, during the pre-crisis, crisis, and post crisis periods. Importantly, Asian emerging stock markets interaction is less before the global financial crisis period. The return and volatility spillover indices touch their respective historical peaks during the global financial crisis 2007∼2008, however Bangladeshi market faces this condition in 2009∼2010.

Macroeconomic Impact on Stock Market Returns and Volatility: Evidence from Sri Lanka

This paper examines the relationship between stock market returns and selected macroeconomic variables and examine the impact of macroeconomic uncertainty on stock market volatility in Sri Lankan stock market. Interest rate, inflation, money supply and exchange rate are selected as a set of exogenous variables to represent the macroeconomic factors that influence the stock market, returns and volatility. The sample includes monthly stock market index and macroeconomics data from 1998 to 2016 covering 228 data points. In achieving research objectives, Vector Error Correction Model (VECM) and Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) models are specified and estimated. The results of Johansen Juselius cointegration test indicate a long run relationship between macroeconomic variables and stock returns. Particularly, the results of cointegration test suggest that there is a significant negative effect of Treasury bill Rate (TBR) and Exchange Rate (EXR) on stock returns while significant positive long run effect of Money Supply (MSI) / Inflation (INF) on stock returns. The Error Correction Term (ECM) in the VECM model indicates only 4.1 percent of the long run shock adjusted in the short run period and supports the argument of weak form of market efficiency in the Colombo Stock Exchange (CSE), Sri Lanka. Further, the results of the EGARCH model evidence the presence of asymmetric volatility in the monthly stock returns which suggest that the bad news in the CSE has larger effect on the volatility of the stock market than the good news. Similarly, the model establishes that interest rate and money supply create macroeconomic risk to the volatility of the stock market returns in Sri Lankan context. Accordingly, this paper, as a whole, conclusively establishes that the stock returns and market volatility are dependent on macroeconomic variables. These findings hold managerial and policy implication at least to the Sri Lankan policy makers, market regulators, investors and market analysts. The test results suggest the information inefficiency in the Colombo stock market. Further, Investors in the market should look at the systematic risks revealed by the money supply and short term interest rates when structuring portfolios and diversification strategies. Policymakers may need to take these macroeconomic variables into account when formulating economic and financial policies.

Long vs. short term asymmetry in volatility and the term structure of risk

This short paper introduces the distinction between short and long term asymmetric effects in volatilities. With short term asymmetry we refer to the conventional one, i.e. the asymmetric response of current volatility to the most recent return shocks. In addition, we argue that there may be asymmetries with respect to the way the effect of past return shocks propagate over time. We refer to this as long term asymmetry and propose a model that enables the study of the potential occurrence of such a feature. In an empirical application using stock market index data we find evidence of the joint presence of short and long term asymmetric effects and demonstrate important implications for risk predictions. In particular, positive return shocks is ascribed substantial significance for long term risk prediction.

Restructuring at Suzlon Energy Ltd

Synopsis The case highlights the ambitious growth strategy of Suzlon, an Indian company specializing in non-conventional (wind) energy. In 2007, Suzlon announced the acquisition of REpower of Germany, one of the top wind power companies in the world. It issued zero coupon and coupon bearing foreign currency (US dollar) convertible bonds (FCCB) amounting to $760 million to finance the acquisition. These bonds were listed in Singapore. Due to deteriorating business conditions the company experienced a sharp decline in profitability and stock price resulting in a debt overhang. At the same time, the Indian rupee depreciated from INR44 to INR55 leading to losses on largely unhedged, foreign currency coupon payments. The company had to restructure its capital structure to escape bankruptcy. Since FCCB holders did not agree to restructure the terms of the instrument, the company had to turn to senior lenders to restructure debt. Eventually Suzlon had to sell-off REpower to reduce leverage. Research methodology The case is based on interviews of market intermediaries and published information. The information relating to the restructuring has been taken from the information statement filed with the Securities Exchange Board of India and the Stock Exchanges. The timeline of events were constructed from the information available in company press releases. Financial statements and other details are from the documents filed with the regulators and supplemented with the information available in Prowess database. The stock price and stock market index data are from the websites of Bombay Stock Exchange and the National Stock Exchange of India. Exchange rates, inflation and interest rates have been taken from Bloomberg and the Reserve Bank of India website. Valuation inputs like multiples are from Prowess database and security analyst reports. Sources of information are documented appropriately in the case and instructor’s manual. Although we interviewed the investment bankers involved in the restructuring we have not included any private information in the case to preserve confidentiality. Relevant courses and levels This case can be used in a corporate finance course or in a module on debt restructuring in a corporate restructuring course or in the financing module in an advanced corporate finance course or in an International Finance course. It can also be used to teach an integrated approach to valuation and financing in a valuation course. Theoretical bases The case highlights the rationale for issuing FX convertible debt, parity conditions in international finance and the use of alternate valuation models.

Financial contagion behavior analysis based on complex network approach

Recently, financial crisis occurred frequently, such as Asian crisis, sub-prime loan crisis and so on. And, financial contagion behavior analysis is an important subject in the field of international finance. This paper analyzes the financial contagion behavior of US subprime crisis and describes the co-movement behavior between US stock index return and the stock index returns of tested countries by complex network approach. The co-movement modes are defined by a coarse graining procedure, and then by weighted complex network models and evolutionary models transformation characteristics between the modes are analyzed. In order to analyze the sub-prime loan crisis contagion from US to other countries, we selected three main stock market indexes (China, Japan and Germany), S&P 500 index to present an empirical analysis, and the stock market index data is from 2006.1.1 to 2009.3.31. To analyze the contagion effect of the sub-prime crisis, the data is divided into three segments to be analyzed based on complex network approach. The empirical result shows that the co-movement modes of the S&P 500 index and the stock indexes of other countries are clustered around a few critical modes during the evolution. The co-movement modes have the characteristic of grouping, and the conversion of the co-movement modes requires an average of 3–5 days. This paper confirms the sub-prime crisis contagion behavior by comparing the parameters including degree, clustering coefficient and betweenness centrality in three stages. This research may provide further information between S&P 500 index and other stock indexes for the co-movement. More importantly, a new approach is provided for testing the financial contagion effect in this paper.

Asset pricing with financial bubble risk

This paper characterizes systematic risk stemming from the possible occurrence of price bubbles and measures the impact of this additional risk factor on asset prices. Historical stock market behavior and recent empirical experience have led economists and policy makers to acknowledge that price bubbles in financial markets do occur and need to be accounted for in risk analysis. New econometric tools for analyzing mildly explosive behavior (Phillips and Magdalinos, 2007; Phillips et al., 2011) have made it possible to detect the presence of bubbles in data and to date stamp their origination and collapse, providing empirical confirmation of such episodes in recent data. The potential for price bubbles and market collapse provides another source of stock market risk and adds to the risk premium. We provide an analytic and empirical investigation of this additional risk factor. The standard present value model is extended to allow for possible price bubbles and the effects of integrating bubble behavior into a consumption-based asset pricing model are analyzed. The theory involves attention to the investor time horizon and a study of the validity of conventional log linear approximations in the presence of nonstationary and mildly explosive data. Finite decision horizons accommodate myopic investors and are a component of speculative behavior that focuses on short run market gains rather than long run effects of fundamentals. An econometric approach to estimate bubble risk effects is developed and the methods are applied to composite stock market index data, giving new model-based equity premium and market volatility estimates that more closely match the data than traditional consumption based asset pricing models.

Measuring financial market risk contagion using dynamic MRS-Copula models: The case of Chinese and other international stock markets

Considering the asymmetric dependency structure and regime switching process, we construct the dynamic Markov Regime Switching Copula (MRS-Copula) models to measure the financial risk contagion. The dynamic MRS-Copula models consist of the marginal model and dynamic MRS Rotated-Gumbel function, and they are examined by the goodness-of-fit test method. Using the dynamic MRS-Copula models, we calculate the daily lower tail dependency by adopting the international stock market index data from January 1997 to June 2015, and provide evidence of financial risk contagion effects between Chinese stock market and other international stock markets after the reform of the RMB exchange rate system in China, and this is particular the case after in the U.S. sub-prime mortgage crisis and the European debt crisis. When conducting robust tests with weekly and monthly data, the empirical result basically holds. As for the financial risk contagion channels in Chinese stock market, the fundamental economic linkages play a more important role than liquidity, information and other factors.

Asymmetry with respect to the memory in stock market volatilities

The empirically most relevant stylized facts when it comes to modeling time-varying financial volatility are the asymmetric response to return shocks and the long memory property. Up till now, these have largely been modeled in isolation. To capture asymmetry also with respect to the memory structure, we introduce a new model and apply it to stock market index data. We find that although the effect on volatility of negative return shocks is higher than for positive ones, the latter are more persistent and relatively quickly dominate negative ones.

Intensity of shock transmission amid US-BRICS markets

Purpose – The purpose of this paper is to analyze the intensity of transmission of shocks from USA to BRICS countries in the long-run and short-run deviations and swiftness of recovery during US subprime mortgage crisis. This analysis enables the authors to explore the evolving patterns of relationships between these markets and examine whether their co-movements altered either in response to international shocks that originated in advanced markets like USA or due to their domestic fluctuations. Design/methodology/approach – Employing data of daily stock market indices (open and close) of BRICS countries for the period January 2, 2001 to May 31, 2012, this paper examines the interactions and characteristics of price movements of BRICS with US market by applying co-integration tests, vector error correction model and Granger causality relationship. The daily stock market indices data are derived from respective stock exchange web sites. Findings – The results exhibit that both long-run co-integration relationships and short-run Granger causality relationships exist between the stock markets of US-BRICS. Furthermore, this nexus is amplified in the short-run during 2007-2009, when the subprime mortgage financial crisis in the USA cropped up. This finding lends support to the prominence of developed (US) market links in the proliferation of persistent co-movements of BRICS stock markets. Research limitations/implications – The findings imply an increasing degree of global market integration due to quick dissemination of global shocks originating from developed market like USA, and swift recovery which can be attributed to the increased resilience, consistent with the moderated level of domestically driven risk in the BRICS markets. In spite of their similarities, long-run and short-run interdependences with the US stock market exhibit differences among the BRICS. This can be attributed to the regional heterogeneity in long-run risk and return co-movements with the USA. Practical implications – Changes from the US index easily affect these stock markets in the short-run, which implies that the US index may act as a leading indicator for investing funds in BRICS markets. Originality/value – This study would enable the authors to understand whether BRICS economies actually remain resilient to adverse developments in USA and could serve as alternative investment destinations for global portfolio diversification.

Scaling analysis of time series of daily prices from stock markets of transitional economies in the Western Balkans

In this paper we have analyzed scaling properties of time series of stock market indices (SMIs) of developing economies of Western Balkans, and have compared the results we have obtained with the results from more developed economies. We have used three different techniques of data analysis to obtain and verify our findings: Detrended Fluctuation Analysis (DFA) method, Detrended Moving Average (DMA) method, and Wavelet Transformation (WT) analysis. We have found scaling behavior in all SMI data sets that we have analyzed. The scaling of our SMI series changes from long-range correlated to slightly anti-correlated behavior with the change in growth or maturity of the economy the stock market is embedded in. We also report the presence of effects of potential periodic-like influences on the SMI data that we have analyzed. One such influence is visible in all our SMI series, and appears at a period $T_{p}\approx 90$ days. We propose that the existence of various periodic-like influences on SMI data may partially explain the observed difference in types of correlated behavior of corresponding scaling functions.

Detecting Randomness: A Review of Existing Tests with New Comparisons

In this article we review and compare a number of existing tests for detecting randomness in time series data, with emphasis on stock market index data. By comparing variance ratio tests with traditional statistical tests, we have the most extensive simulation comparison of such procedures. The investigated tests are compared over a diverse group of distributions, models, and stock market applications. In our stock market data analysis, the choice of data transformation can have a noticeable effect on test results. This study provides the reader with a guide as to which test and transformation is most appropriate for their use.

A Nonparametric Test for Deviation from Randomness with Applications to Stock Market Index Data

The proposed test detects deviations from randomness, without a priori distributional assumption, when observations are not independent and identically distributed (i.i.d.), which is suitable for our motivating stock market index data. Departures from i.i.d. are tested by subdividing data into subintervals and then using a conditional probability measure within intervals as a binomial test. This nonparametric test is designed to detect deviations of neighboring observations from randomness when the dataset consists of time series observations. Simulation results and a comparison with Lo and MacKinlay's (1988) variance ratio test showed that our proposed test is a competitive alternative.

An Empirical Modelling of New Zealand Hospitality and Tourism Stock Returns

This paper examines the factor risk premiums of stock returns for the hospitality and tourism companies in New Zealand. The Arbitrage Pricing Theory (APT) approach is used to investigate the expected return for stock portfolio with respect to market, macro (i.e., money supply and discount rate), and tourism factor sensitivities. Monthly stock prices, market index, tourism, and macroeconomic data are used in the study. The results indicate that the risk premiums for international tourism demand and term premium (proxy for discount rate) are positively significant at the 5% level. A one unit increase in tourist arrival sensitivity would result in expected return increase of 10 to 17 percentage point. Similarly, a one unit increase in term premium can increase hospitality-tourism expected returns by 0.2 percentage point. However, the findings for the money supply factor are not significant. As the study shows that investors face high positive tourism demand risk, it is imperative for firms and policymakers in New Zealand to promote inbound tourism through effective marketing and management. This in turn can provide high expected returns and create shareholder value for investors.

On the role of the estimation error in prediction of expected shortfall

In the estimation of risk measures such as Value at Risk and Expected shortfall relatively short estimation windows are typically used rendering the estimation error a possibly non-negligible component. In this paper we build upon previous results for the Value at Risk and discuss how the estimation error comes into play for the Expected Shortfall. We identify two important aspects where it may be of importance. On the one hand there is in the evaluation of predictors of the measure. On the other there is in the interpretation and communication of it. We illustrate magnitudes numerically and emphasize the practical importance of the latter aspect in an empirical application with stock market index data.

On the Role of the Estimation Error in Prediction of Expected Shortfall

In the estimation of risk measures such as Value at Risk and Expected shortfall relatively short estimation windows are typically used rendering the estimation error a possibly non-negligible component. In this paper we build upon previous results for the Value at Risk and discuss how the estimation error comes into play for the Expected Shortfall. We identify two important aspects where it may be of importance. On the one hand there is in the evaluation of predictors of the measure. On the other there is in the interpretation and communication of it. We illustrate magnitudes numerically and emphasize the practical importance of the latter aspect in an empirical application with stock market index data.

Do the Major Oil Companies Anticipate Production Allocations?An Examination of Oil and Financial Market Data for Arbitrage Investment Opportunities

This article examines an oil stock market index and oil company stock price data in 30-day and 60-day windows on either side of OPEC production allocation meetings. The study investigates causal relationships over the full period in the sample. The results justify asking the question of whether oil companies anticipate and use information on the outcomes of these meetings. Do they encourage or condone cartel behavior? The study concludes that the pricing of crude oil is likely to have more to do with the behavior of oil market protagonists than freely interacting supply and demand forces. This continues to have implications for the health of the global economy and financial system but, more importantly, it means that market anomalies have created possible arbitrage opportunities for investors in oil markets and industries. <b>TOPICS:</b>Security analysis and valuation, commodities, legal/regulatory/public policy

Solving the Puzzle: Stock Market Spillover Effects between Namibia and South Africa

This paper presents an empirical analysis of spillover effects between the Namibian Stock Exchange (NSX) and the Johannesburg Stock Exchange (JSE) in order to estimate the level of market integration between the two. Using daily stock market index data from January 2002 to October 2007, an EGARCH model is applied to measure unilateral returns and volatility spillover effects from JSE to NSX. Despite strong cultural, institutional, and economic linkages between Namibia and South Africa, previous studies revealed conflicting results on the subject matter. In this analysis, we find that some volatility but no returns spillovers are transmitted from the JSE market to Namibian stocks captured by the NSX Local index. The findings suggest that large opportunities for diversification exist for South African market participants by investing in Namibian stocks. Meanwhile, extensive returns spillovers are transmitted to the NSX Overall index due to its composition of primarily dual-listed South African firms. A test for lagged spillover effects onto NSX Local due to market inefficiencies reveals no persistent arbitrage opportunities for regional investors.

Dividends, prices and the present value model: firm-level evidence

Recent stock price movements have led to a re-examination of the present value model. Typically, empirical studies have employed a long span of US stock market index data, and have attributed a failure to detect cointegration to the presence of bubbles. This study considers UK firm-level data, and implements panel unit root and cointegration tests. Recent panel tests that allow for cross-sectional dependence control for factors such as bubbles that may result in temporary deviations from the long-run price–dividend relationship. The panel test results largely support the present value model, yielding evidence of cointegration between real prices and dividends.

Evidence of an asymmetry in the relationship between volatility and autocorrelation

This paper focuses on the general determinants of autocorrelation and the relationship between autocorrelation and volatility in particular. Using UK stock market index and individual stock price data, a multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) model is used to generate estimates of conditional autocorrelation. The covariance equation of this model is modified to include the potential determinants of autocorrelation including volatility, which is proxied using the time series of filtered probabilities of a Markov regime switching model. Consistent with the previous literature, this paper documents a negative relationship between volatility and autocorrelation. The results suggest that an asymmetry exists in this relationship which is attributed to the constraints placed on short selling.