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Financial Machine Learning
Kelly, Bryan T.; Xiu, Dacheng - National Bureau of Economic Research - 2023
We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...
Persistent link: https://www.econbiz.de/10014322889
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Stock Market Forecastability and Volatility : A Statistical Appraisal
Mankiw, N. Gregory - 1989
This paper presents and implements statistical tests of stock market forecastability and volatility that are immune …
Persistent link: https://www.econbiz.de/10012475889
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Comprehensive OOS Evaluation of Predictive Algorithms with Statistical Decision Theory
Dominitz, Jeff; Manski, Charles F. - National Bureau of Economic Research - 2024
We argue that comprehensive out-of-sample (OOS) evaluation using statistical decision theory (SDT) should replace the current practice of K-fold and Common Task Framework validation in machine learning (ML) research. SDT provides a formal framework for performing comprehensive OOS evaluation...
Persistent link: https://www.econbiz.de/10014512123
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Index-Option Pricing with Stochastic Volatility and the Value of Accurate Variance Forecasts
Engle, Robert F. - 1993
In pricing primary-market options and in making secondary markets, financial intermediaries depend on the quality of forecasts of the variance of the underlying assets. Hence, the gain from improved pricing of options would be a measure of the value of a forecast of underlying asset returns....
Persistent link: https://www.econbiz.de/10012474423
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Mispriced Index Option Portfolios
Constantinides, George M. - 2017
The optimal portfolio of a utility-maximizing investor trading in the S&P 500 index and cash, subject to proportional transaction costs, becomes stochastically dominated when overlaid with a zero-net-cost portfolio of S&P 500 options bought at their ask and written at their bid price in most...
Persistent link: https://www.econbiz.de/10012454974
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Why Has U.S. Inflation Become Harder to Forecast?
Stock, James H. - 2006
inflation process is well described by an unobserved component trend-cycle model with stochastic volatility or, equivalently, an …
Persistent link: https://www.econbiz.de/10012466341
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Answering the Critics : Yes, ARCH Models Do Provide Good Volatility Forecasts
Andersen, Torben G. - 1997
Volatility permeates modern financial theories and decision making processes. As such, accurate measures and good … forecasts of future volatility are critical for the implementation and evaluation of asset pricing theories. In response to this …, a voluminous literature has emerged for modeling the temporal dependencies in financial market volatility at the daily …
Persistent link: https://www.econbiz.de/10012472795
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Valuation of Variance Forecast with Simulated Option Markets
Engle, Robert F. - 1990
An appropriate metric for the success of an algorithm to forecast the variance of the rate of return on a capital asset could be the incremental profit from substituting it for the next best alternative. We propose a framework to assess incremental profits for competing algorithms to forecast...
Persistent link: https://www.econbiz.de/10012475683
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A Multiple Indicators Model for Volatility Using Intra-Daily Data
Engle, Robert F. - 2003
Many ways exist to measure and model financial asset volatility. In principle, as the frequency of the data increases …, the quality of forecasts should improve. Yet, there is no consensus about a true' or best' measure of volatility. In this … paper we propose to jointly consider absolute daily returns, daily high-low range and daily realized volatility to develop a …
Persistent link: https://www.econbiz.de/10012468577
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Interpretable Asset Markets?
Bansal, Ravi - 2002
In this paper we show that measures of economic uncertainty (conditional volatility of consumption) predict and are …
Persistent link: https://www.econbiz.de/10012469320