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T.L. Lai 375loss function and assuming known hyperparameters) when Y i = y is observed,the basic principle underlying EB is that a parametric form of G (as in Stein,1956) or even G itself (as in Robbins, 1956) can be consistently estimatedfrom Y 1 ,...,Y n ,leadingtotheEBruledĜ. DynamicEBextendsthisideatolongitudinal data Y it ; see Lai et al. (2013). In the context of insurance claimsover time for n contracts belonging to the same risk class, the conventionalapproach to insurance rate-making (called “evolutionary credibility” in actuarialscience) assumes a linear state-space for the longitudinal claims data sothat the Kalman filter can be used to estimate the claims’ expected values,which are assumed to form an autoregressive time series. Applying the EBprinciple to the longitudinal claims from the n insurance contracts, Lai andSun (2012) have developed a class of linear mixed models as an alternativeto linear state-space models for evolutionary credibility and have shown thatthe predictive performance is comparable to that of the Kalman filter whenthe claims are generated by a linear state-space model. This approach can bereadily extended to GLMMs not only for longitudinal claims data but also fordefault probabilities of n firms, incorporating frailty, contagion, and regimeswitching. Details are given in Lai and Xing (2013).33.5 Statistics in the new era of financeStatistics has been assuming an increasingly important role in quantitativefinance and risk management after the financial crisis, which exposed theweakness and limitations of traditional financial models, pricing and hedgingtheories, risk measures and management of derivative securities and structuredproducts. Better models and paradigms, and improvements in risk managementsystems are called for. Statistics can help meet these challenges, whichin turn may lead to new methodological advances for the field.The Dodd–Frank Act and recent financial reforms in the European Unionand other countries have led to new financial regulations that enforce transparencyand accountability and enhance consumer financial protection. Theneed for good and timely data for risk management and regulatory supervisionis well recognized, but how to analyze these massive datasets and usethem to give early warning and develop adaptive risk control strategies isachallengingstatisticalproblemthatrequiresdomainknowledgeandinterdisciplinarycollaboration. The monograph by Lai and Xing (2013) describessome recent research in sequential surveillance and early warning, particularlyfor systemic risk which is the risk of a broad-based breakdown in the financialsystem as experienced in the recent financial crisis. It reviews the criticalfinancial market infrastructure and core-periphery network models for mathematicalrepresentation of the infrastructure; such networks incorporate thetransmission of risk and liquidity to and from the core and periphery nodes