Advanced research improves investment performance

Innovation and scientific discovery are difficult, yet performing groundbreaking research, deducing valuable insights, and applying creative solutions to the challenges inherent to investment management is our raison d’etre. We consume research from finance, economics, physics, and other disciplines to test our hypotheses and confirm our analysis, all for the purpose of developing more effective ways to construct portfolios and manage risk.


GDP Nowcasting

GDP Nowcasting model tracks the economy in real-time by monitoring over one hundred economic and financial indicators on a daily basis. From this model we generate our proprietary GDP forecast and a Kalman Filter Nowcasting Factor, centered around zero with a pre-2020 standard deviation of one. The point estimate of this Nowcasting Factor, its distribution, and the probability of negative tail events are important inputs when predicting periods of economic fragility. NBER recessions are shaded in gray.

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Data source: Federal Reserve (FRED), FactSet, NBER, Valravn Research Partners
Model developer: Karsten Jeske, Ph.D., CFA

Market Volatility

Volatility Regime Switching

Market Volatility Switching Model identifies three different volatility regimes of the US equity market and predicts the shift between one volatility regime to another. The model utilizes daily market prices and is updated daily to produce a probability distribution for the three volatility regimes. The white background indicates periods of low volatility; the gray background indicates periods of mid volatility; the red background indicates periods of high volatility. The blue line is the S&P 500 Index.

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Data source: FactSet, Valravn Research Partners
Model developer: Tarek Frahi, Ph.D. Candidate

Market Volatility

Financial Turbulence

Financial turbulence is a condition in which asset price behavior undergoes significant changes relative to their historical patterns. This includes extreme price moves, decoupling of correlated assets as well as the convergence of uncorrelated assets. Financial turbulence can be measured as Mahalanobis distance, the distance from the mean distribution. This measure can be used as an indicator of the presence of systemic risk and can help to construct portfolios that are more resilient to episodes of financial instability.

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Data source: FactSet, Valravn Research Partners
Model developer: Svetlana Morozov, Ph.D.

Valravn Capital, LLC.

28 Liberty Street, #657
New York, NY, 10005
(646) 450-2073

This website is for informational purposes only and is not intended to be, and should not be construed as an offer to sell, or the solicitation of an offer to buy, any interest in any entity or other investment vehicle. If such an investment opportunity should become available, a confidential private offering memorandum outlining such investment opportunity would be provided to you, and the information in this document would be qualified in its entirety by reference to all of the information, including without limitation the risk factors. An investment in any product or vehicle described herein should be regarded as highly speculative in nature and could result in the loss of all of the capital invested. Any such investment is intended only for experienced and sophisticated investors who could afford such a loss. Past performance is not indicative of future results.

Additional information about Valravn Capital, LLC and its Advisory Persons is also available on the SEC's website at by searching the firm name or the firm CRD #307231.