Readings and resources

Read first (in this order)

1. Hamilton, Time Series Analysis (Princeton UP, 1994): ch. 3 (stationary ARMA), ch. 4 (forecasting), ch. 21 (heteroskedasticity, GARCH), ch. 22 (regime switching; the filter and smoother).
2. Campbell, J. Y. and Thompson, S. B. (2008), "Predicting excess stock returns out of sample: can anything beat the historical average?", Review of Financial Studies 21(4):1509–1531. Open copy: Harvard DASH.
3. Welch, I. and Goyal, A. (2008), "A comprehensive look at the empirical performance of equity premium prediction", RFS 21(4):1455–1508, doi:10.1093/rfs/hhm014; working paper NBER w10483.
4. López de Prado, Advances in Financial Machine Learning (Wiley, 2018): ch. 3 (labelling, triple barrier, meta-labelling), ch. 4 (sample weights, uniqueness), ch. 7 (purged CV).
5. Tetlock, P. and Gardner, D., Superforecasting: The Art and Science of Prediction (Crown, 2015).
6. Gneiting, T. and Raftery, A. E. (2007), "Strictly proper scoring rules, prediction, and estimation", JASA 102(477):359–378.

For depth

• Fama, E. (1970), "Efficient capital markets: a review of theory and empirical work", Journal of Finance 25(2):383–417.
• Grossman, S. and Stiglitz, J. (1980), "On the impossibility of informationally efficient markets", American Economic Review 70(3):393–408.
• Goyal, A., Welch, I. and Zafirov, A. (2024), "A comprehensive 2022 look at the empirical performance of equity premium prediction", RFS 37(11):3490–3557.
• Stambaugh, R. F. (1999), "Predictive regressions", Journal of Financial Economics 54(3):375–421, doi:10.1016/S0304-405X(99)00041-0.
• Engle, R. F. (1982), "Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation", Econometrica 50(4):987–1007.
• Bollerslev, T. (1986), "Generalized autoregressive conditional heteroskedasticity", Journal of Econometrics 31(3):307–327.
• Bollerslev, T. and Wooldridge, J. (1992), "Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances", Econometric Reviews 11(2):143–172.
• Nelson, D. (1990), "Stationarity and persistence in the GARCH(1,1) model", Econometric Theory 6(3):318–334.
• Patton, A. (2011), "Volatility forecast comparison using imperfect volatility proxies", Journal of Econometrics 160(1):246–256.
• Hamilton, J. D. (1989), "A new approach to the economic analysis of nonstationary time series and the business cycle", Econometrica 57(2):357–384, IDEAS.
• Kim, C.-J. (1994), "Dynamic linear models with Markov-switching", Journal of Econometrics 60(1–2):1–22.
• Brier, G. W. (1950), "Verification of forecasts expressed in terms of probability", Monthly Weather Review 78(1):1–3.
• Murphy, A. H. (1973), "A new vector partition of the probability score", Journal of Applied Meteorology 12(4):595–600.
• Mellers, B. et al. (2014), "Psychological strategies for winning a geopolitical forecasting tournament", Psychological Science 25(5):1106–1115, doi:10.1177/0956797614524255; summary in the APS Observer.
• Tetlock, P. (2005), Expert Political Judgment (Princeton UP).
• Tsay, Analysis of Financial Time Series (3rd ed.): ch. 2 (linear models), ch. 3 (GARCH family), ch. 4 (Markov switching).

Lectures MIT OCW 18.S096 (Fall 2013), lecture 8 "Time Series Analysis I" and lecture 9 "Volatility Modeling", via the video gallery.

Indian sources

• Pandey, A. (2003), "Modeling and forecasting volatility in Indian capital markets", IIM Ahmedabad working paper, IIMA listing.
• Rajvanshi, Santra and Basu, IIM Calcutta WPS 797, PDF; Banerjee and Kumar, IIM Calcutta WPS 688, PDF.
• IIMA Indian Fama–French–Momentum data library (for predictor and factor series), faculty.iima.ac.in/~iffm/Indian-Fama-French-Momentum/.
• NSE India VIX: product and methodology documents on nseindia.com (India VIX section).

Software arch (GARCH family, used as the oracle in the tests), statsmodels (MarkovRegression, ARIMA, multipletests). hmmlearn is in limited-maintenance mode.