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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 test asset pricing errors---is improving in model parameterization (or "complexity''). Our empirical findings verify the...
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components, namely continuous volatility and jump, and future market excess return. Building on quadratic variation theory, we …
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these events. Using theory and simulations we study the implications of the imminent threat of climate change on different …
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When excess returns are used to estimate linear stochastic discount factor (SDF) models, researchers often adopt a normalization of the SDF that sets its mean to 1, or one that sets its intercept to 1. These normalizations are often treated as equivalent, but they are subtly different both in...
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When Capital Asset pricing Model (CAPM) is considered as valid asset pricing theory, Security Market Line (SML) is … returns need not be same as implied discount rates even when CAPM is applicable and markets are efficient. This is because the … over the forecast period. The single period return of CAPM changes every year as the market changes with economic …
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