With some regularity conditions maximum likelihood estimators (MLEs) al-ways produce asymptotically optimal (in the sense of consistency, efficiency, sufficiency,and unbiasedness) estimators. But in general, the MLEs lead to non-robust statisticalinference, for example, pricing models and risk...

This paper develops an efficient estimation procedure for time-varying parameter autoregressive models with stochastic volatility. Necessary restrictions are imposed on the time-varying autoregressive parameters, thus stability conditions are satisfied. We show that a conditional Gaussian...

A new tabu search algorithm is proposed for the maximum score estimator computation, where the focus is on large sample size and/or large number of parameters. The proposed algorithm shares the same solution representation with the Hyperplanes Intersection Simulated Annealing (HISA) previously...

This paper introduces a flexible local projection that generalises the model by Jorda (2005) to a non-parametric setting using Bayesian Additive Regression Trees. Monte Carlo experiments show that our BART-LP model is able to capture non-linearities in the impulse responses. Our first...

We develop an analytical framework to appropriately model and adequately analyze A/B tests in presence of nonparametric non-stationarities in the targeted business metrics. A/B tests, also known as online randomized controlled experiments, have been used at scale by data-driven enterprises to...

In this paper we study the finite sample and asymptotic properties of various weighting estimators of the local average treatment effect (LATE), several of which are based on Abadie (2003)’s kappa theorem. Our framework presumes a binary endogenous explanatory variable (“treatment”) and a...

This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A Generalized Method of...

We improve on the Instrumented Principal Component Analysis (IPCA) model developed in Kelly, Pruitt and Su (2019) by providing more efficient Generalized Least Square (GLS) estimators with a closed-form limiting distribution allowing for a more consistent (mis)pricing inference. The IPCA model...

We propose a data-driven methodology to shrink the covariance matrix using reinforcement learning (RL). Our approach does not impose any assumption about the stock returns and can be applied to any covariance matrix target/prior. We focus on the special case of the global minimum variance...

Credit Value Adjustment sensitivities represent the quantitative basis for the full spectrum of steering activities of CVA desks: hedging, control, explanation and forecasting. In realistic applications, typically involving large numbers of market risk-factors, calculation of CVA Greeks poses...