In this paper, we propose the discrete time Compound Beta-Binomial Risk Model with by-claims, delayed by-claims and randomized dividends. We then analyze the Gerber-Shiu function for the cases where the dividend threshold and under the assumption that the constant discount rate . More specifical…
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
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12 results for “beta-binomial”
New method predicts language model scaling risks with limited data.
problem Accurately predicting model capabilities and risks with limited data.
method Introduces a robust estimation framework using beta-binomial distribution and dynamic sampling strategy.
result More accurate predictions of rare risks and capabilities at lower computational cost.
We show how to analyze and interpret the correlation structures, the conditional expectation values and correlation coefficients of exchangeable Bernoulli random variables. We study implied default distributions for the iTraxx-CJ tranches and some popular probabilistic models, including the Gaussian copula model, Beta …
Identifiability of Generalized Hypergeometric Distribution (GHD) Directed Acyclic Graphical Modelsstat.ML
We introduce a new class of identifiable DAG models where the conditional distribution of each node given its parents belongs to a family of generalized hypergeometric distributions (GHD). A family of generalized hypergeometric distributions includes a lot of discrete distributions such as the binomial, Beta-binomial, …
An Empirical Study on Arrival Rates of Limit Orders and Order Cancellation Rates in Borsa Istanbulq-fin.MF
Order book dynamics play an important role in both execution time and price formation of orders in an exchange market. In this study, we aim to model the limit order arrival rates in the vicinity of the best bid and the best ask price levels. We use limit order book data for Garanti Bank, which is one of the most trade…
A new Bayesian model improves forecasting for intermittent demand.
problem Sparse observations, cold-start items, and obsolescence in intermittent demand forecasting.
method Hierarchical Bayesian TSB model with partial pooling and calibrated probabilistic configuration.
result TSB-HB achieves the lowest RMSE and RMSSE on the UCI Online Retail dataset.
The probability of default (PD) estimation is an important process for financial institutions. The difficulty of the estimation depends on the correlations between borrowers. In this paper, we introduce a hierarchical Bayesian estimation method using the beta binomial distribution and consider a multi-year case with a …
The study uses the Merton model to estimate PD and finds a phase transition affecting convergence speed.
problem Estimating the probability of default (PD) using limited historical data.
method Adopted the Merton model and analyzed phase transitions in default correlation.
result PD estimation converges slowly when temporal correlation decays by power law less than one.
Shared Keyboard: An improved Bayesian design for phase I clinical trials via Beta kernel processstat.AP
Shared Keyboard design improves phase I clinical trials by borrowing information across doses.
problem Interim decisions based on current dose data may overlook signals from neighboring doses.
method Bayesian model-assisted design using Beta kernel process with kernel-weighted pseudo-counts.
result Significant improvements in identifying maximum tolerated dose and safety.
Temporal Coarse-Graining of Multi-Sector Default Count Data Generates Posterior-Implied Copulasq-fin.RM
This paper models default data to capture dynamic dependence across sectors.
problem Static models fail to explain monthly default dependence.
method Dynamic low-rank state-space model for monthly multi-sector default-count data.
result Effective correlation matrices and copulas are induced from monthly data.
Temporal Coarse-Graining of Multi-Sector Default Count Data Generates Posterior-Implied Copulasq-fin.RM
Temporal coarse-graining of multi-sector default count data generates effective correlation matrices and rank copulas.
problem Explaining the difference in default dependence between monthly and annual aggregation.
method Dynamic low-rank state-space model with AR(1) latent credit-state factors.
result Effective correlation matrices and rank copulas are generated from monthly default count data.
Automated scoring prioritizes risky driving behavior in telematic auto insurance policies.
problem Identifying risky driving behavior in telematic auto insurance policies using machine learning.
method Bayesian approach using MCMC to model propensity of policyholders to undertake trips resulting in positive classification.
result The approach improves efficiency of human resource allocation in identifying risky driving behavior.