This is a summary of links featured on Quantocracy on Monday, 12/21/2020. To see our most recent links, visit the Quant Mashup. Read on readers!
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Monte Carlo option pricing – comparison of R and Julia languages [Mateusz Dadej]This example investigates the performance of R in comparison to Julia language. Additionally shows how to easily call Julia inside R code. With that being said, we will load JuliaCall library that enables us to do so. Alternatively, there is also XRJulia library available. library(JuliaCall) It is necessery to tell R where is Julia.exe stored, so the loaded library can communicate with it
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Visualizing Correlations Among Dow 30 Stocks Via NetworkX [Machine Learning Applied]NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Using daily adjusted close data from 20201118 to 20201218 for Dow 30 stocks, we compute correlation coefficients, apply a threshold of 0.8 to find similar stocks, and produce two types of graphs with NetworkX. To compute correlation coefficients, we read in daily
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Fed Model Improvement? [CXO Advisory]Is there a better way than the Fed model to measure relative attractiveness of equities and bonds. In his October 2020 paper entitled Towards a Better Fed Model, Raymond Micaletti examines seven Fed Model alternatives, each comparing a 10-year forward annualized estimate of equity returns to the yield of 10-year constant maturity U.S. Treasury notes (T-note). The seven estimates of future
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Another miserable year for market forecasters [Mathematical Investor]Suppose, during a nightly TV weather broadcast, that a reporter presented forecasts by persons, with no credentials in mathematical meteorology, who based their analysis on eyeballing a few charts and graphs. If anyone took such amateur forecasts seriously, when a severe storm was approaching, rather than relying on the consensus of qualified scientists assisted by state-of-the-art supercomputer