Automated Volatility Forecasting
References
- (1998) Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. Internat. Econom. Rev. 39(4):885–905.Crossref, Google Scholar
- (2006) Volatility and correlation forecasting. Elliott G, Granger CWJ, Timmermann A, eds. Handbook of Economic Forecasting (North Holland, Amsterdam), 777–878.Crossref, Google Scholar
- (2001) The distribution of realized exchange rate volatility. J. Amer. Statist. Assoc. 96(453):42–55.Crossref, Google Scholar
- (2003) Modeling and forecasting realized volatility. Econometrica 71(2):579–625.Crossref, Google Scholar
- (2016) Lassoing the HAR model: A model selection perspective on realized volatility dynamics. Econometric Rev. 35(8–10):1485–1521.Crossref, Google Scholar
- (2022) Predicting corporate bond returns: Merton meets machine learning. Working paper, Georgetown University, Washington, DC.Google Scholar
- (2016) An intelligent medicine recommender system framework. Proc. IEEE 11th Conf. Indust. Electronics Appl. (IEEE, Piscataway, NJ), 1383–1388.Crossref, Google Scholar
- (2016a) Roughing up beta: Continuous vs. discontinuous betas and the cross-section of expected stock returns. J. Financial Econom. 120(3):464–490.Crossref, Google Scholar
- (2020) Good volatility, bad volatility, and the cross section of stock returns. J. Financial Quant. Anal. 55(3):751–781.Crossref, Google Scholar
- (2016b) Exploiting the errors: A simple approach for improved volatility forecasting. J. Econometrics 192(1):1–18.Crossref, Google Scholar
- (2018) Risk everywhere: Modeling and managing volatility. Rev. Financial Stud. 31(7):2729–2773.Crossref, Google Scholar
- (2020) Realized volatility forecasting with neural networks. J. Financial Econom. 18(3):502–531.Crossref, Google Scholar
- (2011) The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets. J. Econometrics 160(1):48–57.Crossref, Google Scholar
- (2020) Using machine learning to predict realized variance. J. Investment Management 18(2):1–16.Google Scholar
- (2018) On the long-run volatility of stocks. J. Amer. Statist. Assoc. 113(523):1050–1069.Crossref, Google Scholar
- (2024) Deep learning in asset pricing. Management Sci. 70(2):714–750.Link, Google Scholar
- (1998) The relation between implied and realized volatility. J. Financial Econom. 50(2):125–150.Crossref, Google Scholar
- (2018) Illiquidity premia in the equity options market. Rev. Financial Stud. 31(3):811–851.Crossref, Google Scholar
- (2006) The common and specific components of dynamic volatility. J. Econometrics 132(1):231–255.Crossref, Google Scholar
- (2009) A simple approximate long-memory model of realized volatility. J. Financial Econometrics 7(2):174–196.Crossref, Google Scholar
- (2021) When moving-average models meet high-frequency data: Uniform inference on volatility. Econometrica 89(6):2787–2825.Crossref, Google Scholar
- (2023) Machine learning and fund characteristics help to select mutual funds with positive alpha. J. Financial Econom. 150(3):103737.Crossref, Google Scholar
- (1995) Comparing predictive accuracy. J. Bus. Econom. Statist. 13(3):253–263.Crossref, Google Scholar
- (2004) Do stock prices and volatility jump? Reconciling evidence from spot and option prices. J. Finance 59(3):1367–1403.Crossref, Google Scholar
- (2021) The volatility premium. Quart. J. Finance 11(3):1–35.Crossref, Google Scholar
- (2015) A non-linear dynamic model of the variance risk premium. J. Econometrics 187(2):547–556.Crossref, Google Scholar
- (1993) Common risk factors in the returns on stocks and bonds. J. Financial Econom. 33(1):3–56.Crossref, Google Scholar
- (2015) A five-factor asset pricing model. J. Financial Econom. 116(1):1–22.Crossref, Google Scholar
- (2019) All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously. J. Machine Learn. Res. 20(177):1–81.Google Scholar
- (2006) Predicting volatility: Getting the most out of return data sampled at different frequencies. J. Econometrics 131(1–2):59–95.Crossref, Google Scholar
- (2020) Empirical asset pricing via machine learning. Rev. Financial Stud. 33(5):2223–2273.Crossref, Google Scholar
- Guijaro-Ordonez J, Pelger M, Zanotti G (2024) Deep learning statistical arbitrage. Management Sci. Forthcoming.Google Scholar
- (2011) Variance risk premium and cross-section of stock returns. Working paper, University of Toronto, Toronto.Google Scholar
- (2020) The information content of the implied volatility surface: Can option prices predict jumps? Working paper, University of North Carolina at Charlotte, Charlotte, NC.Google Scholar
- (2016) The common factor in idiosyncratic volatility: Quantitative asset pricing implications. J. Financial Econom. 119(2):249–283.Crossref, Google Scholar
- (1993) Returns to buying winners and selling losers: Implications for stock market efficiency. J. Finance 48(1):65–91.Crossref, Google Scholar
- (2023) (Re-)imag(in)ing price trends. J. Finance 78(6):3193–3249.Crossref, Google Scholar
- (2021) Pervasive underreaction: Evidence from high-frequency data. J. Financial Econom. 141(2):573–599.Crossref, Google Scholar
- (2016) Horizon pricing. J. Financial Quant. Anal. 51(6):1769–1793.Crossref, Google Scholar
- (2023) Machine-learning the skill of mutual fund managers. J. Financial Econom. 150(1):94–138.Crossref, Google Scholar
- (2019) Characteristics are covariances: A unified model of risk and return. J. Financial Econom. 134(3):501–524.Crossref, Google Scholar
- (2023) Deep learning from implied volatility surfaces. Working paper, Yale University, New Haven, CT.Google Scholar
- (2021) Selecting mutual funds from the stocks they hold: A machine learning approach. Working paper, Wuhan University, Wuhan, China.Google Scholar
- (2015) Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes. J. Econometrics 187(1):293–311.Crossref, Google Scholar
- (2017) A unified approach to interpreting model predictions. Proc. 31st Internat. Conf. Neural Inform. Processing Systems (NIPS’17) (Curran Associates Inc., Red Hook, NY), 4768–4777.Google Scholar
- (2018) Forecasting of realised volatility with the random forests algorithm. J. Risk Financial Management 11(4):1–15.Crossref, Google Scholar
- Murray S, Xiao H, Xia Y (2024) Charting by machines. J. Financial Econom. 153(1):103791.Google Scholar
- (1987) A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica. 55(3):703–708.Crossref, Google Scholar
- (2010) A survey on transfer learning. IEEE Trans. Knowledge Data Engrg. 22(10):1345–1359.Crossref, Google Scholar
- (2015) Good volatility, bad volatility: Signed jumps and the persistence of volatility. Rev. Econom. Statist. 97(3):683–697.Crossref, Google Scholar
- (2012) ‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables. J. Financial Econom. 106(3):527–546.Crossref, Google Scholar
- (2024) Machine learning for realised volatility forecasting. Working paper, University of Manchester, Manchester, UK.Google Scholar
- (2013) International stock return predictability: What is the role of the United States? J. Finance 68(4):1633–1662.Crossref, Google Scholar
- (2015) Imagenet large scale visual recognition challenge. Internat. J. Comput. Vision 115(1):211–252.Crossref, Google Scholar
- (1997) Forecasting economic time series using flexible vs. fixed specification and linear vs. nonlinear econometric models. Internat. J. Forecasting 13(4):439–461.Crossref, Google Scholar
- (2007) Asset Price Dynamics, Volatility, and Prediction (Princeton University Press, Princeton, NJ).Google Scholar
- (2006) Forecast combinations. Elliott G, Granger CWJ, Timmermann A, eds. Handbook of Economic Forecasting (North Holland, Amsterdam), 135–196.Crossref, Google Scholar
- (1996) The lack of a priori distinctions between learning algorithms. Neural Comput. 8(7):1341–1390.Crossref, Google Scholar
- (2005) A tale of two time scales: Determining integrated volatility with noisy high-frequency data. J. Amer. Statist. Assoc. 100(472):1394–1411.Crossref, Google Scholar

