Academic Research on DAX-100

• Spillovers and correlations between US and major European stock markets: the role of the euro, Savva, C. S., Osborn, D. R., & Gill, L. (2009).Applied Financial Economics,19(19), 1595-1604. This article investigates the impact of the introduction of the euro on the interactions across the New York, London, Frankfurt and Paris stock markets. After controlling for possible returns and volatility spillovers, the article focuses on the correlations of shocks using the framework of Dynamic Conditional Correlations (DCC). 
• The nonlinear dynamics of stock prices, Shively, P. A. (2003).The Quarterly Review of Economics and Finance,43(3), 505-517. This paper evaluates the nonlinear dynamics of stock prices using a three-regime, nonlinear threshold random-walk model and daily, international data from the Cotation Assistee en Continu (CAC) 40, Deutscher Aktienindex (DAX) 30, Financial Times Stock Exchange (FTSE) 100, Nikkei 225, Standard & Poors (S&P) 500 and Toronto Stock Exchange (TSE) 300 stock-price indexes from January 1, 1970 through December 29, 2000. 
• The design and implementation of a German stock price research index (DeutscherAktien-Forschungsindex DAFOX), Gppl, H., & Schtz, H. (1993). InMathematical Modelling in Economics(pp. 506-519). Springer, Berlin, Heidelberg. This paper develops a German Stock Price Research Index (Deutscher Aktien-FOrschungsindeX DAFOX). In this paper. the authors describe the concept and construction elements of this index as well as its statistical properties and correlations to other existing stock market indices. 
• The intraday ex ante profitability ofDAX-Futures arbitrage for institutional investors in Germany-The case of early and late transactions, Rder, K., & Bamberg, G. (1994).Finanzmarkt und Portfolio-Management,8(1), 50-62. The German futures market Deutsche Terminbrse started trading in DAX futures contracts on November 23, 1990. The foundation of these futures contracts, the DAX¹, is based on the prices of 30 German Blue Chips. The standardised delivery months are March, June, September, December. The German Options and Futures Exchange simultaneously lists three subsequent delivery dates. The final cash settlement takes place on the third Friday of the delivery month. 
• Life time of correlation between stocks prices on established and emerging markets, Buda, A. (2011). This paper suggests that the correlation coefficient between stocks depends on price history and includes information on hierarchical structure in financial markets. The paper introduces the Life Time of Correlation between stocks prices to know how far to investigate the price history to obtain the optimal durability of correlation. The research is carried out on emerging (Poland) and established markets (in the USA, Great Britain and Germany). Other methods, including the Minimum Spanning Trees, tree half-life, decomposition of correlations and the Epps effect are also discussed. 
• Intraday volatility spillovers in the German equity index derivatives markets, Booth, G. G., & So, R. W. (2003). Applied Financial Economics,13(7), 487-494. This paper examines the intraday information transmission process among the Deutscher Aktienindex (DAX), DAX futures and DAX options in Germany. Using the extreme value volatility approach developed by Boothet al. , the volatilities of the three markets are found to spill over to one another. These results support the notion that the three index assets are informationally linked, and the three markets should be considered a complete system for intraday information processing. 
• DAX'sJanuary 2008 Crash: A Routine Correction or Outright Panic, Balaz, M. (2009). This work provides a comprehensive study of the January 2008 stock market downturn and its impact on DAX index. It finds three possible causes of the crash: adverse economic news, technical trading signals and possible market manipulation (by SocGen). The piece also finds strong interdependencies among world equity indices during this period. It further examines DAXs development in the preceding years and do not find any proof of a price bubble.   
• Short-and long-term links among European and US stock markets, Gerrits, R. J., & Yuce, A. (1999). Applied Financial Economics,9(1), 1-9. The relationship between equity markets in various countries has been examined extensively in the literature. This study tests the interdependence between stock prices in Germany, the UK, the Netherlands and the US, using daily closing prices for the period between March 1990 and October 1994. Results of the tests show that the US exerts a significant impact on European markets. Implications of the findings are documented. 
• Regional Integration and the Diversity of Corporate Governance: Some Lessons from European Integration, Jackson, G. (2002). 
• How wacky is theDAX? The changing structure of German stock market volatility, Werner, T., & Stapf, J. (2003). Discussion paper Series 1/Volkswirtschaftliches Forschungszentrum der Deutschen Bundesbank. This paper investigates the volatility structure of the German stock market index DAX and its constituents. Using a recently developed test, the paper finds a volatility break in 1997.The paper suggests that domestic factors which may help to explain the break in volatility are the growing number of institutional investors and the increase in the volatility of longer-term interest rates.
• Deep Learning with Gated Recurrent Unit Networks for Financial Sequence Predictions, Shen, G., Tan, Q., Zhang, H., Zeng, P., & Xu, J. (2018).Procedia computer science,131, 895-903. This paper suggests that gated recurrent unit (GRU) networks perform well in sequence learning tasks and overcome the problems of vanishing and explosion of gradients in traditional recurrent neural networks (RNNs) when learning long-term dependencies. It also analyses their seldom use in financial time series. The paper proposes GRU networks and its improved version for predicting trading signals for stock indexes of the Hang Seng Indexes (HSI), the Deutscher Aktienindex (DAX) and the S&P 500 Index from 1991 to 2017, and compare the GRU-based models with the traditional deep net and the benchmark classifier support vector machine (SVM).