Statistical Models and Methods for Financial Markets

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Deep Learning Stock Volatility with Google Domestic Trends (2015, methodology): “Auto-regressive moving average model (ARMA( p , q )) [17], however, answers this question by evaluating autocorrelation and partial autocorrelation functions and setting up the p and q maximum lags.”
High-dimensionality effects in the Markowitz problem and other quadratic programs with linear constraints: Risk underestimation (2010, methodology): “As noted in Lai and Xing (2008), there is a dearth of theoretical studies regarding, in particular, the behavior of bootstrap estimators.”
Sentiment-aware stock market prediction: A deep learning method (2017, background): “evaluating autocorrelation and partial autocorrelation functions to determine the p and q maximum lags in the autoregressive moving average model(ARMA(p,q)) [36].”
Closed-loop and activity-guided optogenetic control. (2015, unknown): “…including chaos, bifurcations, and subharmonics are available (Isermann and Mü nchhof, 2011 Review models of neuronal variability that account for changing animal state should also be integrated with these and other existing models of dynamic variability (Lai and Xing, 2010; Goris et al., 2014).”