Efficient Nested Estimation of CoVaR: A Decoupled Approach
References
- (2016) CoVaR. Amer. Econom. Rev. 106(7):1705–1741.Crossref, Google Scholar
- (2012) Random search for hyper-parameter optimization. J. Machine Learn. Res. 13(1):281–305.Google Scholar
- , et al. (2023) Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. Wiley Interdisciplinary Rev. Data Mining Knowledge Discovery 13(2):e1484.Crossref, Google Scholar
- (1973) The pricing of options and corporate liabilities. J. Political Econom. 81(3):637–654.Crossref, Google Scholar
- (2015) Risk estimation via regression. Oper. Res. 63(5):1077–1097.Link, Google Scholar
- (2007) Optimal rates for the regularized least-squares algorithm. Foundations Comput. Math. 7(3):331–368.Crossref, Google Scholar
- (2023) Generalized importance sampling for nested simulation. Corlu CG, Hunter SR, Lam H, Onggo BS, Shortle J, Biller B, eds. Proc. Winter Simulation Conf. (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 409–420.Google Scholar
- (2010) Dynamic Asset Pricing Theory (Princeton University Press, Princeton, NJ).Google Scholar
- (2011) Density Functional Theory (Springer, Berlin).Crossref, Google Scholar
- (2019) Hyperparameter optimization. Hutter F, Kotthoff L, Vanschoren J, eds. Automated Machine Learning: Methods, Systems, Challenges (Springer International Publishing, Cham, Switzerland), 3–33.Crossref, Google Scholar
- (2021) Making sense of data: Introduction to statistics for gravitational-wave astronomy, International Max Planck Research School for Gravitational Wave Astronomy, Lecture Notes (Albert Einstein Institute, Potsdam, Germany).Google Scholar
- (2023) Efficient risk estimation for the credit valuation adjustment. Preprint, submitted January 14, https://arxiv.org/abs/2301.05886.Google Scholar
- (2004) Monte Carlo Methods in Financial Engineering (Springer, Berlin).Crossref, Google Scholar
- (2010) Nested simulation in portfolio risk measurement. Management Sci. 56(10):1833–1848.Link, Google Scholar
- (2002) A Distribution-Free Theory of Nonparametric Regression (Springer, Berlin).Crossref, Google Scholar
- (2008) Uniform convergence rates for kernel estimation with dependent data. Econom. Theory 24(3):726–748.Crossref, Google Scholar
- (2016) Train faster, generalize better: Stability of stochastic gradient descent. Balcan MF, Weinberger KQ, eds. Proc. 33rd Internat. Conf. Machine Learn. (PMLR, New York), 1225–1234.Google Scholar
- (1993) A closed-form solution for options with stochastic volatility with applications to bond and currency options. Rev. Financial Stud. 6(2):327–343.Crossref, Google Scholar
- (2009) Estimating quantile sensitivities. Oper. Res. 57(1):118–130.Link, Google Scholar
- (2017) Kernel smoothing for nested estimation with application to portfolio risk measurement. Oper. Res. 65(3):657–673.Link, Google Scholar
- (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2(5):359–366.Crossref, Google Scholar
- (2024) Monte Carlo estimation of CoVaR. Oper. Res. 72(6):2337–2357.Link, Google Scholar
- (2022) Options, Futures, and Other Derivatives, 11th ed. (Pearson, Harlow, England).Google Scholar
- (2023) Sup-norm convergence of deep neural network estimator for nonparametric regression by adversarial training. Preprint, submitted July 8, https://arxiv.org/abs/2307.04042.Google Scholar
- (2013) An Introduction to Statistical Learning (Springer, Berlin).Crossref, Google Scholar
- (2018) Gaussian processes and kernel methods: A review on connections and equivalences. Preprint, submitted July 6, https://arxiv.org/abs/1807.02582.Google Scholar
- (2025) The FA–SAA algorithm for CVaR optimization. Asia-Pacific J. Oper. Res. 42(6):2540002.Crossref, Google Scholar
- (2010) Stochastic kriging for efficient nested simulation of expected shortfall. J. Risk 12(3):3–27.Crossref, Google Scholar
- (1964) On estimating regression. Theory Probability Its Appl. 9(1):141–142.Crossref, Google Scholar
- (1959) On elliptic partial differential equations. Annali Della Scuola Normale Superiore Pisa-Scienze Fisiche Matematiche 13(2):115–162.Google Scholar
- (2010) Numerical Solution of Stochastic Differential Equations with Jumps in Finance (Springer Science & Business Media, Boston).Crossref, Google Scholar
- (2001) A generalized representer theorem. Helmbold D, Williamson B, eds. Computational Learning Theory: COLT 2001, Lecture Notes in Computer Science, vol. 2111 (Springer, Berlin, Heidelberg), 416–426.Google Scholar
- (2009) Approximation Theorems of Mathematical Statistics (John Wiley & Sons, New York).Google Scholar
- (2004) Stochastic Calculus for Finance II: Continuous-Time Models (Springer, Berlin).Crossref, Google Scholar
- (2012) Practical bayesian optimization of machine learning algorithms. Pereira F, Burges CJC, Bottou L, Weinberger KQ, eds. Proc. 26th Annual Conf. Neural Information Processing Systems (Curran Associates, Inc., Red Hook, NY), 2951–2959.Google Scholar
- (2014) Dropout: A simple way to prevent neural networks from overfitting. J. Machine Learn. Res. 15(1):1929–1958.Google Scholar
- (2009) Optimal rates for regularized least squares regression. Proc. 22nd Annual Conf. Learning Theory (COLT 2009) (Conference on Learning Theory, Montreal), 79–93.Google Scholar
- (2023) Optimal randomized multilevel Monte Carlo for repeatedly nested expectations. Krause A, Brunskill E, Cho K, Engelhardt B, Sabato S, Scarlett J, eds. Proc. 40th Internat. Conf. Machine Learn. (PMLR, New York), 33343–33364.Google Scholar
- (2008) Introduction to Nonparametric Estimation (Springer, Berlin).Google Scholar
- (2024) Smooth nested simulation: Bridging cubic and square root convergence rates in high dimensions. Management Sci. 70(12):9031–9057.Link, Google Scholar
- (1964) Smooth regression analysis. Indian J. Statist. Ser. A 26(4):359–372.Google Scholar
- (2020) On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing (Amsterdam) 415:295–316.Crossref, Google Scholar
- (2017) Frequentist coverage and sup-norm convergence rate in Gaussian process regression. Preprint, submitted August 16, https://arxiv.org/abs/1708.04753.Google Scholar
- (2017) Error bounds for approximations with deep ReLU networks. Neural Networks 94:103–114.Crossref, Google Scholar

