We propose a continuous-time version of the adaptive robust methodology introduced in Bielecki et al. (2019). An agent solves a stochastic control problem where the underlying uncertainty follows a jump-diffusion process and the agent does not know the drift parameters of the process. The agent...

We consider statistical estimation of superhedging prices using historical stock returns in a frictionless market with d traded assets. We introduce a plugin estimator based on empirical measures and show it is consistent but lacks suitable robustness. To address this we propose novel estimators...

Distributionally robust optimization (DRO) has arose as an important paradigm to address the issue of distributional ambiguity in decision optimization. In its standard form, DRO seeks an optimal solution against the worst-possible expected value evaluated based on a set of candidate...

Commonly used tests to assess evidence for the absence of autocorrelation in a univariate time series or serial cross-correlation between time series rely on procedures whose validity holds for i.i.d. data. When the series are not i.i.d., the size of correlogram and cumulative Ljung-Box tests...

Kleibergen and Zhan (Robust Inference for Consumption-based Asset Pricing, Journal of Finance, 2020) propose a new approach to testing consumption-based asset pricing models. They find that recently proposed factors do not pass their test. I point out that their methodology also implies that the...

The purpose of this paper is to study the Stochastic Volatility Inspired model (SVI) as implied volatility model: we study the analytic part of the SVI with the arbitrage conditions, we establish the initial guess and the parameter's boundaries. Until recently it was not possible to find...

We study the distributionally robust stable tail adjusted return ratio (DRSTARR) portfolio optimization problem, in which the objective is to maximize the STARR performance measure under data-driven Wasserstein ambiguity. We consider two types of imperfectly known uncertainties, named uncertain...

We develop a forecast superiority testing methodology which is robust to the choice of loss function. Following Jin, Corradi and Swanson (JCS: 2017), we rely on a mapping between generic loss forecast evaluation and stochastic dominance principles. However, unlike JCS tests, which are not...

We investigate the empirical evidence on the Euler equation models using methods that are robust to weak instruments and structural changes for a set of eight countries. We start with the conventional closed economy model and consider extensions that include habits and hand-to-mouth consumers....