ABSTRACT: This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We estimate a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data.
ABSTRACT: This paper studies in some detail a class of continuous-time stochastic volatility models. These models are direct models of daily asset return volatility based on realized measures constructed from high-frequency data.
ABSTRACT: Elections rely on people, hardware, and software, all of which are fallible and subject to manipulation. Well resourced nation-states continue to attack U.S. elections. Voting equipment is built by private vendors–some foreign, but all using foreign parts.
ABSTRACT: Modeling return correlations between thousands of stocks poses great challenges, as empirical estimators tend to perform poorly when assets don’t share common risk factors, such as country or industry sector. In this paper, we show the advantages of using Hierarchical Principal Component Analysis (HPCA) for modeling correlations, as opposed to the classic PCA.
ABSTRACT: We present evidence that sustainability is inextricably linked with market-implied uncertainty. We derive an econometric decomposition of sustainability ratings yielding three orthogonal components capturing uncertainty, investor sentiment, and an idiosyncratic sustainability factor.
ABSTRACT: We study the spread of losses and defaults in financial networks with two features: collateral requirements and resolution and bankruptcy stay rules. When collateral is committed to a firm’s counterparties, a solvent firm may default if it lacks sufficient liquid assets to meet its payment obligations.
ABSTRACT: Two-point time-series data, characterized by baseline and follow-up observations, are frequently encountered in health research. In analyzing such time-series data, the two-point pattern must be adequately accounted to balance the trade-off between efficient inference and estimation bias.
ABSTRACT: We investigate to what extent one can improve reinforcement learning algorithms. For this, we first show that the classical asymptotic convergence rate O(1/√N) is pessimistic and can be replaced by O((log(N)/N)^Beta) with Beta in [0.5,1] and N the number of iterations. Second, we propose a dynamic optimal policy for the choice of the learning rate.
ABSTRACT: Factor modeling of asset returns has been a dominant practice in investment science since the introduction of the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT). The factors, which account for the systematic risk, are either specified or interpreted to be exogenous.
ABSTRACT: As risk, trading, strategy, and decision-support systems have become more deeply integrated into financial services firms’ workflows, predicting a collection of economic and market indicators becomes even more critical to support these systems than in the past. At the same time, the underlying processes that drive economies and markets have become increasingly dynamic given they are more likely to be subject to rapid successions of regime changes...
ABSTRACT: We study a generalization of the GPS method and provide a class of estimators of beta that further improve the l_2 error and accuracy of the minimum variance portfolio weights. To do this we utilize additional observable information about the separation of betas into sub-collections where the betas in each sub-collection are close to each other.
