Similar Search Results

Cross-section without factors : correlation risk, strings and asset prices

Distaso, Walter; Mele, Antonio; Vilkov, Grigory - 2021 - This version: January 13, 2021

Many asset pricing theories treat the cross-section of returns volatility and correlations as two intimately related quantities driven by common factors, which hinders achieving a neat definition of a correlation premium. We formulate a model without factors, but with a continuum of securities...

Persistent link: https://www.econbiz.de/10012421289

Common Risk Factors in the Returns on Cryptocurrencies

Liu, Weiyi - 2019

This paper identifies three common risk factors in the returns on cryptocurrencies, which are related to cryptocurrency market return, market cap (size) and momentum of cryptocurrencies. Using the empirical data of cryptocurrencies, we find strong evidence that there are anomalous returns that...

Persistent link: https://www.econbiz.de/10012871481

Common Risk Factors in the Cross Section of Catastrophe Bond Returns

Braun, Alexander; Herrmann, Markus; Hibbeln, Martin Thomas - 2021

Historically, cat bonds have provided high single-digit average annual returns, paired with a low volatility and little correlation to other asset classes. While there is an extensive literature that explains (ex-ante) cat bonds spreads, there is no factor model in the academic literature that...

Persistent link: https://www.econbiz.de/10013216898

Correlation risk, strings and asset prices

Mele, Antonio; Distaso, Walter; Vilkov, Grigory - 2019

Persistent link: https://www.econbiz.de/10012181112

Equilibrium-Based Volatility Models of the Market Portfolio Rate of Return (Peacock Tails or Stotting Gazelles)

Feldman, David - 2015

Volatility models of the market portfolio's return are central to financial risk management. Within an equilibrium framework, we introduce an implementation method and study two families of such models. One is deterministic volatility, represented by current popular models. Another is in the...

Persistent link: https://www.econbiz.de/10013036566

Principal portfolios

Kelly, Bryan T.; Malamud, Semyon; Pedersen, Lasse Heje - 2020 - This version: June 8, 2020

We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes...

Persistent link: https://www.econbiz.de/10012271188

Tail risks, asset prices, and investment horizons

Baruník, Jozef; Nevrla, Matěj - 2019

We examine how extreme market risks are priced in the cross-section of asset returns at various horizons. Based on the frequency decomposition of covariance between indicator functions, we define the quantile cross-spectral beta of an asset capturing tail-specific as well as horizon-, or...

Persistent link: https://www.econbiz.de/10012009758

Instability of Factor Strength in Asset Returns

Massacci, Daniele - 2022

We study the problem of detecting structural instability of factor strength in asset pricing models for financial returns. We allow for strong and weaker factors, in which the sum of squared betas grows at a rate equal to and slower than the number of test assets, respectively: this growth rate...

Persistent link: https://www.econbiz.de/10013311483

Stock profiling using time-frequency-varying systematic risk measure

Mestre, Roman - In: Financial innovation : FIN 9 (2023) 1, pp. 1-29

(CAPM) framework. The dynamic of systematic risk across time and frequency is analyzed to investigate stock risk … different asset allocation. We conclude that the standard CAPM assumes short-run investment. Then, investors should consider … time-frequency CAPM to perform systematic risk analysis and portfolio allocation. …

Persistent link: https://www.econbiz.de/10014289044

Complexity in Factor Pricing Models

Didisheim, Antoine; Ke, Shikun; Kelly, Bryan T.; … - 2023

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance—in terms of SDF Sharpe ratio and average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best...

Persistent link: https://www.econbiz.de/10014254198