Volatility Scaling with Autocorrelation

Sergey Okun  This article was written by Sergey Okun – Senior Financial Analyst, I Know First, Ph.D. in Economics.

Summary:

  • Autocorrelation enables us to estimate the volatility of an investment portfolio in a more precise way.
  • The S&P 500 returns characterize by negative autocorrelation which means that the S&P 500 has a less grade of risk than the estimation based on the assumption of stock returns independency.
  • The I Know First AI algorithm provides us with the tool to select the most promising stocks.

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VaR Estimation: Condition Shortfall

Sergey Okun  This article was written by Sergey Okun – Senior Financial Analyst, I Know First, Ph.D. in Economics.

Summary:

  • VaR does not provide insight into what losses might occur if the situation is worse than the threshold that we assume.
  • The Expected Shortfall method enables us to estimate the amount of tail risk an investment portfolio has.
  • The I Know First AI algorithm provides us with the tool to select the most promising stocks.

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VaR Estimation: Variance-Covariance and Historical Simulation Methods

Sergey Okun  This article was written by Sergey Okun – Senior Financial Analyst, I Know First, Ph.D. in Economics.

Summary:

  • VaR allows us to estimate possible financial losses in different scenarios.
  • Historical Simulation VaR provides us with a significantly different result from the respective Variance-Covariance VaR for very high confidence intervals which depends on the normality assumption.
  • The I Know First AI algorithm provides us with the tool to select the most promising stocks.

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The Conceptual Framework of Applying ML and AI Models to Analyze and Forecast Financial Assets

This article was written by:

Sergey Okun  Sergey Okun – Financial Analyst at I Know First, Ph.D. in Economics.


Eugen KalaidinEugene Kalaidin – Professor, Dept. of Mathematics and Computer science, The Financial University under the Government of the Russian Federation, Ph.D., D. Sci. (Habilitation) in Physics and Mathematics.

Highlights:

  • Knowledge significantly decreases the speculative risk of investment
  • ML and AI technologies allow us to get relevant knowledge about the financial market
  • Information asymmetry is a key factor in getting the arbitrage return
  • Models of nonlinear dynamic systems allow correctly to evaluate financial assets by determining the backbone behavior of assets

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