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The impact of compounding on bond pricing with alternative reference rates

This paper looks at the impact of compounding on zero-coupon bond prices by considering the short rate when it follows a Gaussian diffusion process or a

Explaining explaining: a quick guide on explanatory writing

How to replace estimations and guesses with a Monte Carlo simulation

ANIXA BIOSCIENCES : Management s Discussion and Analysis of Financial Condition and Results of Operations (form 10-Q)

Information included in this Quarterly Report on Form 10-Q contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the. | June 10, 2021

Fake data can help backtesters, up to a point

Risk.net Synthetic data made with machine learning will struggle to capture the caprice of financial markets Quant investors often complain they have only a single version of history against which to test their ideas. One way to get round the problem has been to make history up. Quants have done that for a long time already – using bootstrapping or Monte Carlo simulations to create alternative time series data for the backtests they run. A new idea, though, is to employ machine learning techniques to invent wholly artificial data. Quants are experimenting with these models and say they can produce data indistinguishable in some cases from the real thing.

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