Euro Stoxx Research Articles

Should risk-averse investors target the portfolios of socially responsible companies?

Research background: Companies are required to implement Corporate Social Responsibility (CSR) policies to mitigate the adverse social and environmental effects of their activities and gain legitimacy in the eyes of society. Sustainability initiatives are costly for companies but, at the same time, they are important value-creation drivers. Retail and institutional investors are increasingly choosing portfolios based on CSR performance. However, the relationship between CSR and market beta has hardly been studied at all in the literature, and no direct comparison of the U.S. and European markets has been conducted. Purpose of the article: The two fundamental variables that define an investment are return and risk, and the appropriate risk-return combination depends on the profile of the investors. This research aims to analyze the relationship between CSR and market risk, understood as price volatility and measured by market beta in the U.S. and European markets. Methods: Companies listed in the S&P 500 and Euro Stoxx 300 indexes from 2015 to 2019 were examined using OLS regressions with instrumental variables (IV) and fixed effects panel data. Findings & value added: The results show that those companies with higher CSR have betas below the market index in the U.S. market as well as lower volatility, and are, therefore, more appropriate choices for risk-averse investors. However, this relationship was not confirmed in the European market. This difference may be justified by two reasons: 1) The non-adherence of the United States to the Kyoto Protocol, resulting in less strict legal regulations than in Europe; 2) In the U.S. market, betas are more aggressive, while in the European market they are more defensive, with little margin for reduction. This research contributes to the current state of knowledge by providing empirical evidence that social, environmental, and corporate governance sustainability practices reduce stock volatility in the U.S. capital market, which is highly relevant for private and institutional investors who make their investments based on moral criteria. The results are current and reliable since they cover a broad and recent period for two of the most important stock market indexes.

INVESTIGATION OF THE FEASIBILITY OF INCLUDING DIFFERENT CRYPTOCURRENCIES IN THE INVESTMENT PORTFOLIO FOR ITS DIVERSIFICATION

Purpose – the main aim of this article is to identify cryptocurrencies suitable for investment and portfolio diversification. Research methodology – the methodology of empirical research includes methods of scientific literature analysis, statistical data analysis, multicriteria evaluation, correlation analysis. Findings – Bitcoin is the leading cryptocurrency, but this result could have been due to an exceptionally high market capitalization. Based on the results of the analysis, the inclusion of Bitcoin, Etherium and Dogecoin in the investment portfolio of S&P500, Euro Stoxx 50, DAX and CAC 40 indexes could be considered. Terra could be an interesting investment when considering the benefits of diversification. Research limitations – based on the results of the study, the inclusion of all studied cryptocurrencies in the investment portfolio could be considered in order to diversify the portfolio, taking into account their investment attractiveness. Practical implications – Cryptocurrencies attract investors not only because of the returns they receive, but also because of the absence of intermediaries, which allows them to reduce transaction costs. High returns are associated with high risks, so it is necessary to conduct as much research as possible to identify the benefits of cryptocurrencies and to find risk management strategies. One such benefit of cryptocurrencies highlighted in research is diversification. Originality/Value – the novelty of the study lies in evaluation of 10 selected cryptocurrencies according to different criteria using a multi-criteria valuation method to identify cryptocurrencies that are non-correlated or weakly correlated with traditional assets and the most suitable for investment and for portfolio diversification.

Risk reporting: do country-level institutional forces really matter?

PurposeThe study aims to analyse annual reports of the non-financial European firms listed at the EURO STOXX 50 index over the period of 2007 and 2011.Design/methodology/approachThis study intends to address two main issues: to what extent the country-level institutional forces compel (directly) firm's risk reporting (RR) behaviour and in which way these country-level institutional forces moderate the relationship between RR and firm-level characteristics.FindingsMain findings indicate that, during this period, the European listed companies disclosed more risk information on a voluntary basis (such as operational and strategic risks) and with better informative content (more forward-looking and focused on positive news). Consistent with institutional theory, findings confirm that the country-level institutional forces explain variations on RR. Additionally, it also indicates that the relationship between RR and leveraged firms is weaker among countries with stronger institutional forces. These findings have several implications for investors and regulators in Europe basically in helping achieve efficiency in investment decisions and to stimulate further efforts to improve RR regulations.Originality/valueThis study makes two major contributions. First, it extends Elshandidy's et al. (2015) work by using other country-level institutional forces that capture the efficacy of corporate boards, the protection of minority shareholders' interests, country's level of democracy, law enforcement mechanisms and press freedom. Second, it uses firms that are considered as a blue-chip representation of super-sector leaders in the Eurozone (but from different institutional contexts). This research setting can be more insightful in shedding some light towards our understanding on how these leading firms can promote innovative and high quality level of RR and how country-level driving forces influence these variables.

Causal coupling between European and UK markets triggered by announcements of monetary policy decisions.

We investigate high-frequency reactions in the Eurozone stock market and the UK stock market during the time period surrounding European Central Bank (ECB) and the Bank of England (BoE)’s interest rate decisions, assessing how these two markets react and co-move influencing each other. The effects are quantified by measuring linear and nonlinear transfer entropy combined with a bivariate empirical mode decomposition from a dataset of 1 min prices for the Euro Stoxx 50 and the FTSE 100 stock indices. We uncover that central banks’ interest rate decisions induce an upsurge in intraday volatility that is more pronounced on ECB announcement days and there is a significant information flow between the markets with prevalent direction going from the market where the announcement is made towards the other.

Deriving equity risk premium using dividend futures

In this paper I present a simple stock price decomposition model using the dividend discount model and dividend futures. The main contribution of this paper is the use of dividend futures which represent the risk-adjusted expectations of future dividends. This allows for the calculation of the implied equity risk premium and the decomposition of stock price movements into individual components. Due to the use of daily market data, this method can take into account the structural changes associated with falling interest rates and the Covid-19 pandemic. I empirically show the risk premium development of the S&P 500 Index and Euro Stoxx 50 Index in the last decade.

Guarantee requirements by European central counterparties and international volatility spillovers

This analysis addressed the potential systemic effects of guarantee requirements by central counterparties. Using data from the Spanish BME and German Eurex central clearing counterparties and controlling for tail risk and monetary and real activity variables, we found a significant, positive, and robust relationship between the guarantees required and the spillover or total connectedness effects among nine financial assets in the Spanish, United States, and German capital markets. Bad economic times also had a significant incremental effect on the relationship between guarantees and connectedness. These findings are robust across central clearing corporations and futures contracts in the IBEX 35, DAX 30, and EURO STOXX 50. In addition, an event study indicated that global spillover effects tend to increase before central counterparty institutions raise their guarantees. The implication of the findings is that European clearing institutions react to rather than cause bad economic times.

The Effect of Dependence on European Market Risk. A Nonparametric Time Varying Approach

Multivariate dependence measures are crucial for risk management, where variables usually have heavy tails and non-Gaussian distributions. We propose a multivariate time varying Kendall’s tau estimator in a nonparametric context, allowing for local stationary variables. Consistency and asymptotic normality of the estimator are provided. A simulation study is conducted which supports the idea of better performance than other related methods in many complex scenarios. The proposal is used to draw up a daily estimation of the dependence between European financial market indexes. Nonparametric conditional quantiles are estimated to detect any influence of the degree of dependence on the market returns. That dependence emerges as an important factor in the Euro Stoxx distribution. It is noteworthy that the Kendall’s tau only depends on the multivariate copula, so the effect is not due to hidden effects of the marginals. Local Granger causality is tested and evidence is found that the degree of dependence affects the Euro Stoxx returns in the left tail of the distribution. We believe that these results encourage further research into the effect of diversification in quantiles, linked to the factors behind systemic risk. Additionally, there is a noteworthy increase in dependence following the outbreak of COVID-19.

Circular Economy Impact Analysis on Stock Performances: An Empirical Comparison with the Euro Stoxx 50® ESG Index

Environmental issues have a considerable impact in all economic sectors, also influencing financial markets. As a result, environmental, social, and governance (ESG) awareness is rising in the financial sector. In this perspective, the concept of circular economy (CE) assumes central relevance. The aim of our study is to investigate the relationship between CE strategies and market performance; to this end, we use ESG scores as a proxy for CE. Our initial assumption is that since CE is a component of the “E” factor—in that it can generate positive and measurable environmental impacts—then it can be associated with the ESG score. Therefore, we can methodologically overcome the lack of a specific score related to CE. We use a preselection model based on historical performance by verifying the percentages of the presence of stocks in the two selected indices, namely ESG Euro Stoxx 50® and Euro Stoxx 50-ESG. Overall, we find that ESG profiles have a positive impact on stock performance, although ESG scores do not express higher performance per se. Furthermore, our analysis shows that, to date, there is no evidence that CE initiatives can influence stock returns.

Assessment of the impact of the usage of derivatives on the company’s value

During periods of increased uncertainty, financial market participants are looking for ways to manage risk. The derivatives can be considered as one of the potential instruments for hedging risk. There is no consensus in the scientific literature on whether the application of derivatives has an impact on a value of a company. Thus, the main purpose of this paper is to quantitatively assess the impact of the application of derivatives on the value of a company. The research hypothesis is formulated as follows: the use of derivatives increases the company’s value, i.e. the application of derivatives has a statistically significant positive impact on the value of the company. Seeking to achieve the main purpose and test the hypothesis, besides the analysis of relevant academic literature, the method of panel data analysis (linear multiple regression) is used to quantitatively assess the effect the application of derivatives has made on the company’s value. 28 companies (constituents of EURO STOXX 50 ESG Index) are analyzed in the period of 2005-2020. The results of the research allow stating the effect of derivatives on the value of companies has proven to be statistically significant and positive.

Equity Portfolio Trading with Volatility and Dividend Derivatives

In this article, I investigate trading strategies for equity portfolio analysis that considers diversification using dividend derivatives. The equity portfolio is assumed to be perfectly or highly correlated with the Euro STOXX 50® Index. The strategy employing dividend derivatives for hedging is compared with the more common strategy based on using VSTOXX® derivatives. I highlight that hedging with dividend derivatives offers a viable and possibly superior long-term alternative for hedging equity portfolios with stock index derivatives. In addition, I show that ATM volatility options could have been used successfully to hedge equity tail risk associated with the Brexit event in 2016 that caused a 9% drop in the Euro STOXX 50 immediately after the vote.

Sticky Stock Market Analysts

Technological progress in recent years has made new methods available for making forecasts in a variety of areas. We examine the success of ex-ante stock market forecasts of three major stock market indices, i.e., the German Stock Market Index (DAX), the Dow Jones Industrial Index (DJI), and the Euro Stoxx 50 (SX5E). We test whether the forecasts prove true when they reach their effective dates and are therefore suitable for active investment strategies. We revive the thoughts of the American sociologist William Fielding Ogburn, who argues that forecasters consistently underestimate the variability of the future. In addition, we draw on some contemporary measures of forecast quality (prediction-realization diagram, test of unbiasedness, and Diebold–Mariano test). We reveal that (a) unusual events are underrepresented in the forecasts, (b) the dispersion of the forecasts lags behind that of the actual events, (c) the slope of the regression lines in the prediction-realization diagram is <1, (d) the forecasts are highly biased, and (e) the quality of the forecasts is not significantly better than that of naïve forecasts. The overall behavior of the forecasters can be described as “sticky” because their forecasts adhere too strongly to long-term trends in the indices and are thus characterized by conservatism.

Connectivity costs and price efficiency: An event study

AbstractThis paper investigates the impact of changes in the speed and pricing of direct market access on market efficiency, as measured by the frequency, duration, and profitability of arbitrage strategies. Two natural experiments on the Eurex exchange are identified: an exchange‐wide improvement in technology reducing message latency, and a reduction in the direct exchange access fee. We analyze their impact on the trading of Euro Stoxx 50 Index futures and the Xtrackers Euro Stoxx 50 Ucits ETF. A decrease in the frequency and duration of arbitrages following both events is observed.

Scalable inference for a full multivariate stochastic volatility model

We introduce a multivariate stochastic volatility model that imposes no restrictions on the structure of the volatility matrix and treats all its elements as functions of latent stochastic processes. Inference is achieved via a carefully designed feasible and scalable MCMC that has quadratic, rather than cubic, computational complexity for evaluating the multivariate normal densities required. We illustrate how our model can be applied on macroeconomic applications through a stochastic volatility VAR model, comparing it to competing approaches in the literature. We also demonstrate how our approach can be applied to a large dataset containing 571 stock daily returns of Euro STOXX index.

Efficiency of Using Cryptocurrencies as an Investment Asset

The study of the effectiveness of using cryptocurrencies as an investment resource was conducted on the basis of testing the hypothesis that the introduction of leading cryptocurrencies that are components of the CRIX index into the investment portfolio improves its quality (efficiency). Cryptocurrency investment opportunities are explored on the basis of statistics for July 2016-June 2019. An average annual return on investment (ROI), which is adjusted for passive income on an investment asset (PI), is used to evaluate investment performance. In this study, cryptocurrencies are compared with the following alternative investment areas: Forex market, equities (companies with the highest weights in Nasdaq 100, Euro STOXX 50), commodities, government bonds, real estate. The criteria were determined by the increase in the Sharpe ratio of the investment portfolio and its average annual return. Optimization of investment portfolios without cryptocurrencies and with them was performed on the basis of the Markowitz model. The result shows the confirmation of the hypothesis: the introduction of 3 cryptocurrencies – Bitcoin, Ripple, Litecoin – in the proportions of 2.31%, 1%, 1%, respectively, increased the Sharpe ratio of the investment portfolio by 3.29 points, and the coefficient of return by 9.42 percentage points while increasing the risk by only 0.51 percentage points. This result indicates that the quality (increase in efficiency) of the investment portfolio due to the introduction of cryptocurrencies and the ability to control the investment risk of the portfolio despite the high volatility of cryptocurrencies. This proves the investment (speculative) function of crypto-assets, which can be the basis for developing a model of accounting for crypto-assets.

Autoregressive Neural Network EURO STOXX 50 Forecasting Model Based on Principal Component Stock Selection

Purpose: The given study looks into forecast accuracy of a traditional ARIMA model while comparing it to Autoregressive Neural Network (AR-NN) model for 984 trading days on EURO STOXX 50 Index.
 Methodology: A hybrid model is constructed by combining ARIMA model and feed-forward neural network model aiming to attain linear and non-linear price fluctuations. The study also incorporates the investigation of component stock prices of the index, that can be selected to improve the predictability of the hybrid model. 
 Findings:The reached ARIMA (1,1,3) model showed higher scores than AR-NN model however integrating selected exogenous stock prices from the index components gave much notable accuracy results. The selected exogenous stocks were extracted after conducting PCA and model scores were compared via MAPE and RMSE.
 Unique contribution to theory, practice and policy: The major contribution of this work is to provide the researcher and fnancial analyst a systematic approach for development of intelligent methodology to forecast stock market. This paper also presents the outlines of proposed work with the aim to enhance the performance of existing techniques. Therefore, Empirical analysis is employed along with a hybrid model based on a feed-forward Neural Network. Lesser error is attained on the test set of Index stock price by comparing the performance of ARIMA and AR-NN while forecasting. Hence, The components of extracted Index stock price like exogenous features are added to make an influence from the AR-NN model.

Forecasting Daily Volatility of Stock Price Index Using Daily Returns and Realized Volatility

A comprehensive comparison of the volatility predictive abilities of different classes of time-varying volatility models is considered. The models include the exponential GARCH (EGARCH) and stochastic volatility (SV) models using daily returns, the heterogeneous autoregressive (HAR) model using daily realized volatility (RV) and the realized EGARCH (REGARCH) and realized SV (RSV) models using both. All the models are extended to accommodate the well-known phenomenon in stock markets of a negative correlation between today’s return and tomorrow’s volatility. The HAR model is estimated by the ordinary least squares method, while the EGARCH and REGARCH models are estimated by the quasi-maximum likelihood method. Since it is not straightforward to evaluate the likelihood of the SV and RSV models, a Bayesian estimation via Markov chain Monte Carlo is employed. The models are applied to daily returns and/or RVs of four stock indices: the Dow Jones Industrial Average, the Nikkei 225, the Financial Times Stock Exchange 100, and the Euro Stoxx 50. By conducting predictive ability tests and analyses based on model confidence sets, it is confirmed that the models that use RV (RSV, REGARCH, and RSV) outperform those that do not (EGARCH and SV); this suggests that RV provides useful information in forecasting volatility. Moreover, it is found that the RSV model performs better than the HAR and REGARCH models.

Contagion and portfolio management in times of COVID-19

This paper aims to investigate the COVID-19 pandemic impacts on the interconnectedness between the Chinese stock market and major financial and commodity markets—gold, silver, Bitcoin, WTI, S&P 500, and Euro STOXX 50—and analyze the portfolio design implications. Using daily data from 2018 to 2021, we first apply the wavelet power spectrum (WPS) to visualize volatility shifts. In contrast to previous research, we empirically identify the precise COVID-19 outbreak dates for each market using the Perron (1997) breakpoint test. Finally, we employ the bivariate DCC-GARCH model to analyze the connectedness between markets. The findings reveal that the COVID-19 pandemic caused volatility shifts of different intensities for all of the studied markets. Moreover, each return series exhibits one break date, which is specific to each market and corresponds to a distinct COVID-19-related event. Correlations, hedge ratios, and optimal portfolio weights changed significantly after the COVID-19 outbreak. There is evidence of contagion effects between the Chinese stock market and S&P 500, Euro STOXX 50, gold, and silver. Interestingly, the latter two assets lost their safe haven property with SSE. However, WTI and Bitcoin act as safe havens against SSE risks.

Inventory effects on the price dynamics of VSTOXX futures quantified via machine learning

The VSTOXX index tracks the expected 30-day volatility of the EURO STOXX 50 equity index. Futures on the VSTOXX index can, therefore, be used to hedge against economic uncertainty. We investigate the effect of trader inventory on the price of VSTOXX futures through a combination of stochastic processes and machine learning methods. We formulate a simple and efficient pricing methodology for VSTOXX futures, which assumes a Heston-type stochastic process for the underlying EURO STOXX 50 market. Under these dynamics, approximate analytical formulas for the implied volatility smile and the VSTOXX index have recently been derived. We use the EURO STOXX 50 option implied volatilities and the VSTOXX index value to estimate the parameters of this Heston model. Following the calibration, we calculate theoretical VSTOXX futures prices and compare them to the actual market prices. While theoretical and market prices are usually in line, we also observe time periods, during which the market price does not agree with our Heston model. We collect a variety of market features that could potentially explain the price deviations and calibrate two machine learning models to the price difference: a regularized linear model and a random forest. We find that both models indicate a strong influence of accumulated trader positions on the VSTOXX futures price.

Hedging With Linear Regressions and Neural Networks

We study neural networks as nonparametric estimation tools for the hedging of options. To this end, we design a network, named HedgeNet, that directly outputs a hedging strategy. This network is trained to minimize the hedging error instead of the pricing error. Applied to end-of-day and tick prices of S&P 500 and Euro Stoxx 50 options, the network is able to reduce the mean squared hedging error of the Black-Scholes benchmark significantly. However, a similar benefit arises by simple linear regressions that incorporate the leverage effect.

Is gold a hedge or a safe-haven asset in the COVID–19 crisis?

This study examines the role of gold as a hedge or safe-haven asset in different phases of the COVID-19 pandemic crisis, corresponding to the timing of fiscal and monetary stimuli to support the weakened economy. Using high-frequency data, the results show that gold served as a safe-haven asset for stock markets during Phase I (December 31, 2019–March 16, 2020) of the pandemic. However, gold lost its safe-haven role during Phase II (March 17−April 24, 2020). The optimal weights of gold in S&P 500, Euro Stoxx 50, Nikkei 225, and WTI crude oil portfolios significantly increased during Phase II, suggesting that investors expanded investment in gold as a ‘flight-to-safety asset’ during the crisis. Further, hedging costs increased significantly during Phase II. These findings provide insight for individual and institutional investors and guidance to policymakers, regulators, and media on how gold evolved as a hedge and safe-haven asset in different phases of the pandemic.