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in finance and investment. This reliable resource reviews the basics and covers how to define and refine probability … Contains valuable insights on Monte Carlo simulation--an essential skill applied by many corporate finance and investment … instructions, theory, and practical example models to help apply risk analysis to such areas as derivative pricing, cost estimation …
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-sided inference problem by quantifying the returns forgone by investing in a given startup (observed) relative to an investment … half of the investments were predictably bad—based on information known at the time of investment, the predicted return of … the investment was less than readily available outside options. The cost of these poor investments is 1,000 basis points …
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We study the implications of predictability on the optimal asset allocation of ambiguity-averse long-term investors and analyze the term structure of the multivariate risk-return trade-off considering parameter uncertainty. We calibrate the model to real returns of US stocks, long-term bonds,...
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theories are imperfectly captured quantitatively. A number of different proxies are often collected for a given theory and the … generalizability of the theory index our framework assumes a collection of outcome equations. We accommodate a flexible set of … occurs on the outcome level, allowing for theories to be differentially valid. Our focus is on creating a set of theory …
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The paper applies quantile regression technique, specifically, quantile vector autoregression to stochastic debt sustainability analysis (DSA) and the construction of public debt fan charts. Stochastic approach to DSA typically uses standard ordinary least squares vector autoregression (OLS VAR)...
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