Shiny Applications

A goal of the ISK is to further strengthen the connection between academic research and practical application.

As a way to connect both worlds, dynamic applications provide the possibility to make academic findings readily accessible and to give users the ability to ‘interact’ with the findings.

The following list provides links to web applications associated with the ISK and that are considering various topics and analyses:

The application provides a dynamic interface to the results published in the article "Forecasting Long-Horizon Factor Volatility".

Specifically, the app allows to...

  • ...access the original tables of the article concerning aggregated in- & out-of-sample forecast accuracy. (→"Article Setting")
  • ....answer more detailed questions on forecast accuracy by dynamically change the analyzed factor (portfolio) strategies and the tested model settings being aggregated. (→"Dynamic Setting")
    →Two application examples:
    • Users mainly interested in FX factors can explore the behavior of metrics across tested lookback/forecast windows and model equations over the strategy subset of their choice.
    • Similarly, users can -for example- check which model equation works best for a given forecast horizon.
  • ...download displayed results in an excel (.xlsx) file for further use.

Associated research paper:

Zeissler, Tom O. K. (2022), Forecasting Long-Horizon Factor Volatility, Journal of Beta Investment Strategies, 13(4), 54-106. https://doi.org/10.3905/jbis.2022.1.017

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The app allows to analyze equilibrium effects of non-tradable assets on optimal policy portfolios. The existence of non-tradable assets impacts optimal asset allocation decisions of investors who own such assets and of investors who do not have access to non-tradable assets.

Associated research paper:

Randl, Otto, Arne Westerkamp and Josef Zechner (2022), Equilibrium policy portfolios when some investors are restricted from holding certain assets, China Finance Review International (ahead-of-print)

 

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The application is based on the paper 'A Comprehensive Look at The Empirical Performance of Equity Premium Prediction' by Goyal and Welch (2008)  and serves as a tool to compare the in-sample and the out-of-sample performance of the potential equity premium predictors that are frequently analysed in the literature. The app enables to dynamically track the performance of predictive models at monthly and annual forecast horizons. It helps identify which models produce stable and robust results over time by considering the sample start and data frequency.

Associated research paper:

Welch, Ivo, and Amit Goyal  (2008), A comprehensive look at the empirical performance of equity premium prediction, The Review of Financial Studies 21(4), 1455-1508.


! Start Application !

 

 

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