- Abbasi A, Hossain L (2013) Hybrid centrality measures for binary and weighted networks. In: Complex networks, pp 1–7. Springer, Berlin.
Paper not yet in RePEc: Add citation now
Abedifar P, Giudici P, Hashem SQ (2017) Heterogeneous market structure and systemic risk: evidence from dual banking systems. J Financ Stabil 33:96–119.
Ahelegbey DF, Billio M, Casarin R (2016) Bayesian graphical models for structural vector autoregressive processes. J Appl Econ 31(2):357–386.
Ahelegbey DF, Giudici P, Mojtahedi F (2021) Tail risk measurement in crypto-asset markets. Int Rev Financ Anal 73:101604.
- Baitinger E, Papenbrock J (2017) Interconnectedness risk and active portfolio management: the information-theoretic perspective.
Paper not yet in RePEc: Add citation now
Balla E, Ergen I, Migueis M (2014) Tail dependence and indicators of systemic risk for large us depositories. J Financ Stabil 15(1):195–209.
Billio M, Caporin M (2009) A generalized dynamic conditional correlation model for portfolio risk evaluation. Math Comput Simul 79(8):2566–2578.
- Billio M, Caporin M, Gobbo M (2006) Flexible dynamic conditional correlation multivariate garch models for asset allocation. Appl Financ Econ Lett 2(02):123–130.
Paper not yet in RePEc: Add citation now
Bodnar T, Lindholm M, Thorsén E, Tyrcha J (2021) Quantile-based optimal portfolio selection. Comput Manag Sci, 1–26.
Bonanno G, Caldarelli G, Lillo F, Micciche S, Vandewalle N, Mantegna RN (2004) Networks of equities in financial markets. Eur Phys J B 38(2):363–371.
- Cerqueti R, Lupi C (2017) A network approach to risk theory and portfolio selection. Math Stat Methods Actuarial Sci Finance, pp 73–82.
Paper not yet in RePEc: Add citation now
- Chauveau D, Garel B, Mercier S (2019) Testing for univariate two-component gaussian mixture in practice. J de la Société Française de Statistique 160(1):86–113.
Paper not yet in RePEc: Add citation now
Chen H, Tao S (2020) Tail risk networks of insurers around the globe: an empirical examination of systemic risk for G-SIIS v.s. non G-SIIS. J Risk Insur 87(2):285–318.
- Chen J, Li P (2009) Hypothesis test for normal mixture models: the EM approach. Ann Stat 37(5A):2523–2542.
Paper not yet in RePEc: Add citation now
De Luca G, Zuccolotto P (2011) A tail dependence-based dissimilarity measure for financial time series clustering. Adv Data Anal Classif 5(4):323–340.
- Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B 39(1):1–38.
Paper not yet in RePEc: Add citation now
Diebold FX, Yılmaz K (2014) On the network topology of variance decompositions: measuring the connectedness of financial firms. J Econ 182(1):119–134.
Durante F, Pappadà R, Torelli N (2014) Clustering of financial time series in risky scenarios. Adv Data Anal Classif 8(4):359–376.
Durante F, Pappadà R, Torelli N (2015) Clustering of time series via non-parametric tail dependence estimation. Stat Papers 56(3):701–721.
- Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 95(25):14863–14868.
Paper not yet in RePEc: Add citation now
Engle R (2002) Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Business Econ Stat 20(3):339–350.
Engle RF, Sheppard K (2001) Theoretical and empirical properties of dynamic conditional correlation multivariate garch. Tech. rep, National Bureau of Economic Research.
- Feng Y, Palomar DP et al. (2016) A signal processing perspective on financial engineering, vol 9. Now Publishers.
Paper not yet in RePEc: Add citation now
Furman E, Su J, Zitikis R (2015) Paths and indices of maximal tail dependence. ASTIN Bull J Int Actuarial Assoc, Forthcoming.
Furman E, Wang R, Zitikis R (2017) Gini-type measures of risk and variability: Gini shortfall, capital allocations, and heavy-tailed risks. J Banking Finance 83(1):70–84.
Gardes L, Girard S (2021) On the estimation of the variability in the distribution tail. Test.
Giudici P, Pagnottoni P (2020) Vector error correction models to measure connectedness of bitcoin exchange markets. Appl Stochast Models Bus Ind 36(1):95–109.
- Giudici P, Polinesi G (2019) Crypto price discovery through correlation networks. Ann Oper Res, pp 1–15.
Paper not yet in RePEc: Add citation now
Harris RD, Nguyen LH, Stoja E (2019) Systematic extreme downside risk. J Int Financ Markets Instit Money 61:128–142.
Hartman D, Hlinka J (2018) Nonlinearity in stock networks. arXiv preprint arXiv:1804.10264 .
- Joachim P (2017) The solvency II standard formula, linear geometry, and diversification. J Risk Financ Manag 10(2):1–12.
Paper not yet in RePEc: Add citation now
Kuan CM, Yeh JH, Hsu YC (2009) Assessing value at risk with care, the conditional autoregressive expectile models. J Econometr 150(2):261–270.
- Liu F, Wang R (2021) A theory for measures of tail risk. Math Oper Res.
Paper not yet in RePEc: Add citation now
Liu X, Wu J, Yang C, Jiang W (2018) A maximal tail dependence-based clustering procedure for financial time series and its applications in portfolio selection. Risks 6(4):115.
- Mantegna RN (1997) Degree of correlation inside a financial market. In: Proceedings of the ANDM 97 International Conference, vol 411. AIP press.
Paper not yet in RePEc: Add citation now
Mantegna RN (1999) Hierarchical structure in financial markets. Eur Phys J B-Condens Matter Complex Syst 11(1):193–197.
- Mariani F, Ciommi M, Chelli FM, Recchioni MC (2020) An iterative approach to stratification: Poverty at regional level in Italy. Soc Indicat Res, 1–31.
Paper not yet in RePEc: Add citation now
Mariani F, Recchioni MC, Ciommi M (2019) Merton $$\prime $$ ′ s portfolio problem including market frictions: a closed-form formula supporting the shadow price approach. Eur J Oper Res 275(3):1178–1189.
- Markowitz H (1952) Portfolio selection. J Financ 7(1):77–91.
Paper not yet in RePEc: Add citation now
Miccichè S, Bonanno G, Lillo F, Mantegna RN (2003) Degree stability of a minimum spanning tree of price return and volatility. Phys A Stat Mech Appl 324(1–2):66–73.
- Onnela JP, Chakraborti A, Kaski K, Kertesz J, Kanto A (2003) Asset trees and asset graphs in financial markets. Physica Scripta 2003(T106):48.
Paper not yet in RePEc: Add citation now
- Onnela JP, Chakraborti A, Kaski K, Kertesz J, Kanto A (2003) Dynamics of market correlations: taxonomy and portfolio analysis. Phys Rev E 68(5):056110.
Paper not yet in RePEc: Add citation now
Paraschiv F, Reese SM, Skjelstad MR (2020) Portfolio stress testing applied to commodity futures. Comput Manag Sci 17(2):203–240.
- Parliament E (2009) Council: Directive 2009/138/EC of the European Parliament and of the Council of 25 November 2009 on the taking-up and pursuit of the business of Insurance and Reinsurance (Solvency II).
Paper not yet in RePEc: Add citation now
Peralta G, Zareei A (2016) A network approach to portfolio selection. J Empir Financ 38:157–180.
Pittau MG, Zelli R (2014) Poverty status probability: a new approach to measuring poverty and the progress of the poor. J Econ Inequal 12(4):469–488.
- Puerto J, Rodríguez-Madrena M, Scozzari A (2020) Clustering and portfolio selection problems: a unified framework. Comput Oper Res 117:104891.
Paper not yet in RePEc: Add citation now
- Singh A, Singh RR, Iyengar S (2020) Node-weighted centrality: a new way of centrality hybridization. Comput Soc Netw 7(1):1–33.
Paper not yet in RePEc: Add citation now
- Stanley HE, Mantegna RN (2000) An introduction to econophysics. Cambridge University Press, Cambridge.
Paper not yet in RePEc: Add citation now
Tola V, Lillo F, Gallegati M, Mantegna RN (2008) Cluster analysis for portfolio optimization. J Econ Dyn Contr 32(1):235–258.
Torri G, Giacometti R, Paterlini S (2019) Sparse precision matrices for minimum variance portfolios. Comput Manag Sci 16(3):375–400.
Wang GJ, Xie C, Stanley HE (2018) Correlation structure and evolution of world stock markets: evidence from pearson and partial correlation-based networks. Comput Econ 51(3):607–635.
- Wiedermann M, Donges JF, Heitzig J, Kurths J (2013) Node-weighted interacting network measures improve the representation of real-world complex systems. EPL Europhys Lett 102(2):28007.
Paper not yet in RePEc: Add citation now
- Yang ZR, Chen S (1998) Robust maximum likelihood training of heteroscedastic probabilistic neural networks. Neural Netw 11(4):739–747.
Paper not yet in RePEc: Add citation now