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MULTIVARIATE VOLATILITY FORECASTS FOR STOCK MARKET INDICES

[ARTICLE] This study forecasts realized variance for major international stock market indices, incorporating jump, continuous, and option-implied variance components, using multivariate HAR-type models. by Jeroen Rombouts (ESSEC Business School), Ines Wilms, Christophe Croux Volatility forecasts aim to measure future risk and they are key ...

DYNAMICS OF VARIANCE RISK PREMIA: A NEW MODEL FOR DISENTANGLING THE PRICE OF RISK

[ARTICLE] This paper presents a dynamic model for the variance risk premium that separates the continuous component from jump impacts, using a regime switching structure to analyze S&P500 data and explain return predictability driven by normal price fluctuations versus tail ...

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Technology related risks

EXPLAINING AND PREDICTING THE IMPACT OF AUTHORS WITHIN A COMMUNITY: AN ASSESSMENT OF THE BIBLIOMETRIC LITERATURE AND APPLICATION OF MACHINE LEARNING

[ARTICLE] This paper evaluates how well bibliometric data and theories predict author impact and breakthroughs in research, finding limited explanatory ...

EXACT APPROACHES FOR NETWORK DESIGN PROBLEM WITH RELAYS

[ARTICLE] This paper proposes two integer programming formulations for the directed network design problem with relays (DNDPR) and demonstrates that ...

CONSOLIDATING UNORGANISED RETAIL BUSINESSES THROUGH DIGITAL PLATFORMS: IMPLICATIONS FOR ACHIEVING THE UN SUSTAINABLE DEVELOPMENT GOALS

[ARTICLE] This study develops a framework showing how digitalization of unorganized retail in emerging markets transforms socio-economic conditions for subsistence ...

INFINITE-DIMENSIONAL GRADIENT-BASED DESCENT FOR ALPHA-DIVERGENCE MINIMISATION

[ARTICLE] This paper presents the (α,Γ)-descent algorithm for Bayesian α-divergence minimization, demonstrating improved optimization for mixture models in high dimensions ...

ROBUST HIGH DIMENSIONAL LEARNING FOR LIPSCHITZ AND CONVEX LOSSES

[ARTICLE] This paper establishes risk bounds for Regularized Empirical Risk Minimizers (RERM) under both subgaussian and heavy-tailed assumptions, introducing an ...

LEARNING WITH SEMI-DEFINITE PROGRAMMING: STATISTICAL BOUNDS BASED ON FIXED POINT ANALYSIS AND EXCESS RISK CURVATURE

[ARTICLE] This paper leverages advanced techniques from empirical process theory and Statistical Learning Theory to provide precise statistical analysis and ...

A MOM-BASED ENSEMBLE METHOD FOR ROBUSTNESS, SUBSAMPLING AND HYPERPARAMETER TUNING

[ARTICLE] This paper constructs a robust alternative to cross-validation for hyperparameter tuning and model selection using a median-of-means principle. by Guillaume ...

SIMULTANEOUS DIMENSION REDUCTION AND CLUSTERING VIA THE NMF-EM ALGORITHM

[ARTICLE] This paper proposes a new parameter constraint for non-Gaussian mixture models using a small dictionary of elements. by Pierre Alquier ...

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