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RiskAnalytics: an R package for real time processing of Nasdaq and Yahoo finance data and parallelized quantile lasso regression methods

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  • Lukas Borke

Abstract

In order to integrate and facilitate the research, calculation and analysis methods around the Financial Risk Meter (FRM) project, the R package RiskAnalytics has been developed. Its main goal is to provide data processing and parallelized quantile lasso regression methods for risk analysis based on NASDAQ data, Yahoo Finance data and some macro variables. The derived “Risk Analytics” can help to forecast and evaluate the systemic risk for the corresponding markets. The visualization and the up-to-date FRM can be found on http://frm.wiwi.hu-berlin.de. Supplementary R codes are published on www.quantlet.de with the keyword FRM. The RiskAnalytics package is a convenient tool with the purpose of integrating lasso penalized quantile regression methods with full solution paths and cluster computing support around the topic “Risk Analytics and FRM”.

Suggested Citation

  • Lukas Borke, 2017. "RiskAnalytics: an R package for real time processing of Nasdaq and Yahoo finance data and parallelized quantile lasso regression methods," SFB 649 Discussion Papers SFB649DP2017-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    • Handle: RePEc:hum:wpaper:sfb649dp2017-006
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    File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2017-006.pdf
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    References listed on IDEAS

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    1. Lining Yu & Wolfgang Karl Härdle & Lukas Borke & Thijs Benschop, 2017. "FRM: a Financial Risk Meter based on penalizing tail events occurrence," SFB 649 Discussion Papers SFB649DP2017-003, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
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    Cited by:

    1. Lining Yu & Wolfgang Karl Härdle & Lukas Borke & Thijs Benschop, 2017. "FRM: a Financial Risk Meter based on penalizing tail events occurrence," SFB 649 Discussion Papers SFB649DP2017-003, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Lining Yu & Wolfgang Karl Hardle & Lukas Borke & Thijs Benschop, 2020. "An AI approach to measuring financial risk," Papers 2009.13222, arXiv.org.

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    More about this item

    Keywords

    Risk Analytics; FRM; Data Analytics; Systemic Risk; Quantile Regression; Lasso; Value at Risk; Parallel and Cluster Computing; EDA; Data Visualization;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G01 - Financial Economics - - General - - - Financial Crises
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation

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