This paper develops estimation and inference methods for conditional quantile factor models. We first introduce a simple sieve estimation, and establish asymptotic properties of the estimators under large $N$. We then provide a bootstrap procedure for estimating the distributions of the...

This article presents a new approach for building robust portfolios based on stochastic efficiency analysis and periods of market downturn. The empirical analysis is done on assets traded on the Brazil Stock Exchange, B3 (Brasil, Bolsa, Balcão). We start with information on the assets from...

Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which can lead to a mix of point‐ and set‐identified models. We propose...

Classical Bayesian mechanism design relies on the common prior assumption, but the common prior is often not available in practice. We study the design of prior-independent mechanisms that relax this assumption: the seller is selling an indivisible item to n buyers such that the buyers’...

This paper introduces a unified parametric modeling approach for time-varying market betas that can accommodate continuous-time diffusion and discrete-time series models based on a continuous-time series regression model to better capture the dynamic evolution of market betas.We call this the...

When constructing parametric models to predict the cost of future claims, several important details have to be taken into account: (i) models should be designed to accommodate deductibles, policy limits, and coinsurance factors, (ii) parameters should be estimated robustly to control the...

Robust Estimation of Loss Models for Lognormal Insurance Payment Severity Data Chudamani Poudyal1Department of Statistics and Data Science University of Central Florida. The primary objective of this scholarly work is to develop two estimation procedures –maximum likelihood estimator(MLE) and...

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...

Bilateral Gamma processes generalize the Variance Gamma process and allow to capture more precisely the differences between upward and downward moves of financial returns, notably in terms of jump speed, frequency, and size. Like in most other pure jump models, option pricing under Bilateral...