This paper develops a semiparametric estimation method that jointly identifies the probability weighting and utility functions implicit in option prices. Our econometric method avoids direct specification of the objective conditional return distributions, which are instead obtained by...

Probabilistic editing has been introduced to enable valid inference using established survey sampling theory in situations when some of the collected data points may have measurement errors and are therefore submitted to an editing process. To reduce the editing effort and avoid over-editing, in...

The main objective of the research described in the article is to study the maximum likelihood (ML) estimation and the Bayesian approach for parameter estimation of the Lomax distribution. Additionally, the study aims to determine the approximate intervals for the parameters and the survival...

Deviation of an asset price from its fundamental value, commonly referred to as a price bubble, is a well-known phenomenon in financial markets. Mathematically, a bubble arises when the deflated price process transitions from a martingale to a strict local martingale. This paper explores price...

Economic data are often contaminated by measurement errors and truncated by ranking. This paper shows that the classical measurement error model with independent and additive measurement errors is identified nonparametrically using only two order statistics of repeated measurements. The...

This paper studies the long-term consequences of unemployment on different dimensions of social exclusion. Based on longitudinal linked survey and administrative data from Germany and an event study analysis combined with inverse propensity score weighting, I document that becoming unemployed...

Accurate forecasting of solar PV generation is critical for integrating renewable energy into power systems. This paper presents a multivariate probabilistic forecasting model that addresses the challenges posed by imbalanced data resulting from day and night-time periods in solar photovoltaic...

Climate risk refers to the risks associated with climate change and has already started to impact various sectors of the economy. In this work, we focus on the impact of physical risk on the probability of default for a firm in the agribusiness sector. The probability of default is estimated...

We study risky inter-temporal choice in a large random student sample (n=721) and a large rural sample (n=835) in Malawi. All respondents were exposed to the same 20 Multiple Choice Lists with a rapid elicitation method that facilitated the identification of near-future Certainty Equivalents of...

Abrupt changes are a prevalent feature of financial data sets, such as prices of financial assets, returns of stocks, exchange rates, etc. An early warning system (EWS) can detect existing changes and predict possible future changes before they occur. Two important statistical models for change...

In risk assessment, numerous subfactors influence the probabilities of the main factors. These main factors reflect adverse outcomes, which are essential in risk assessment. A Bayesian network can model the entire set of subfactors and their interconnections. To assess the probabilities of all...

Uncertain future development presents a significant challenge during the distribution strategy planning process. Traditional planning approaches, reliant on creating potential scenarios and assigning probabilities, often struggle due to future developments' inherent unpredictability, which can...

We consider a two-dimensional risk model with simultaneous Poisson arrivals of claims. Each claim of the first input process is at least as large as the corresponding claim of the second input process. In addition, the two net cumulative claim processes share a common Brownian motion component....

Discrete choice experiments (DCEs) often present concise choice scenarios that may appear incomplete to respondents. To allow respondents to express uncertainty arising from this incompleteness, DCEs may ask them to state probabilities with which they expect to make specific choices. The...

Purpose - This paper aims mainly at introducing applied statisticians and econometricians to the current research methodology with non-Euclidean data sets. Specifically, it provides the basis and rationale for statistics in Wasserstein space, where the metric on probability measures is taken as...

We obtain the upper and lower bounds for the ruin probability in the Sparre-Andersen model. These bounds are established under various conditions: when the adjustment coefficient exists, when it does not exist, and when the interarrival distribution belongs to certain aging classes....

Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available. However, the options for robust alternatives to a MLE become significantly limited when dealing with grouped loss severity...

Probability forecasts for binary outcomes, often referred to as probabilistic classifiers or confidence scores, are ubiquitous in science and society, and methods for evaluating and comparing them are in great demand. We propose and study a triptych of diagnostic graphics focusing on distinct...