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.
A new method calculates risk loadings in classification ratemaking without subjective parameters.
Optimizes insurance pricing to minimize ruin probability under various claim dependencies.
Catastrophe risk is a major threat faced by individuals, companies, and entire economies. Catastrophe (CAT) bonds have emerged as a method to offset this risk and a corresponding literature has developed that attempts to provide a market-consistent pricing methodology for these and other long-dated, insurance-type cont…
Optimal insurance contracts are designed to screen risk preferences and risk types under asymmetric information.
The paper finds stocks with higher dynamic network risk have lower returns.
Model forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.
We present a new model for the electricity spot price dynamics, which is able to capture seasonality, low-frequency dynamics and the extreme spikes in the market. Instead of the usual purely deterministic trend we introduce a non-stationary independent increments process for the low-frequency dynamics, and model the la…
New model explains low-volatility anomaly using adaptive multi-factor approach.
Risk diversification is the basis of insurance and investment. It is thus crucial to study the effects that could limit it. One of them is the existence of systemic risk that affects all the policies at the same time. We introduce here a probabilistic approach to examine the consequences of its presence on the risk loa…
This paper improves credit risk analysis by incorporating state-dependent recovery rates into a factor model.
The study improves load forecasting for electricity consumers using advanced machine learning models.
Develops a method to predict stock returns with time-varying risk premia.
Statistical depth metrics help identify risky power grid scenarios.
A method for analysing the risk of taking a too low reserve level by use of Chain Ladder method is developed. We give an answer to the question of how much safety loading in terms of the Chain Ladder standard error has to be added to the Chain Ladder reserve in order to reach a specified security level in loss reservin…
Model for open, decentralized network with task load balancing.
Winterization of Texas power system profitable but risky, estimated at $11.74bn over 30 years.
We study an infinite-horizon optimal investment, consumption and insurance problem for an economic agent who consumes a perishable and a durable good. The agent trades in a risk-free asset, a risky asset, and a durable good whose price follows a correlated diffusion, while the stock of the durable good depreciates dete…
The non-storability of electricity makes it unique among commodity assets, and it is an important driver of its price behaviour in secondary financial markets. The instantaneous and continuous matching of power supply with demand is a key factor explaining its volatility. During periods of high demand, costlier generat…
A Python approach minimizes risk in decentralized exchanges.
TIP-Search optimizes market prediction accuracy and timeliness under uncertain load.
We study the dynamics of correlation and variance in systems under the load of environmental factors. A universal effect in ensembles of similar systems under the load of similar factors is described: in crisis, typically, even before obvious symptoms of crisis appear, correlation increases, and, at the same time, vari…
Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS). At the transmission system, despite of irregular load behavio…
Airlines optimize fuel loading with better flight time predictions.
A new portfolio method uses NMF for risk budgeting, outperforming classical methods.
We present a general approach to the pricing of products in finance and insurance in the multi-period setting. It is a combination of the utility indifference pricing and optimal intertemporal risk allocation. We give a characterization of the optimal intertemporal risk allocation by a first order condition. Applying t…
Enhances load forecasting for multiple entities with dynamic similarities.
Extends ASRF model for green and brown loans, accounting for systematic and idiosyncratic risks.
Data loading can dominate deep neural network training time on large-scale systems. We present a comprehensive study on accelerating data loading performance in large-scale distributed training. We first identify performance and scalability issues in current data loading implementations. We then propose optimizations t…
Paper proposes a new method for hourly load forecasting using smart meter data.
HIV RNA viral load (VL) is an important outcome variable in studies of HIV infected persons. There exists only a handful of methods which classify patients by viral load patterns. Most methods place limits on the use of viral load measurements, are often specific to a particular study design, and do not account for com…
The paper analyzes reinsurance strategies in peer-to-peer insurance schemes.
We propose a framework for constructing factor models for alpha streams. Our motivation is threefold. 1) When the number of alphas is large, the sample covariance matrix is singular. 2) Its out-of-sample stability is challenging. 3) Optimization of investment allocation into alpha streams can be tractable for a factor …
In (exploratory) factor analysis, the loading matrix is identified only up to orthogonal rotation. For identifiability, one thus often takes the loading matrix to be lower triangular with positive diagonal entries. In Bayesian inference, a standard practice is then to specify a prior under which the loadings are indepe…
Bayesian Transformer improves probabilistic load forecasting with calibrated uncertainty estimates.
Dynamic risk factor model improves portfolio performance in high dimensions.
We consider the problem of pricing derivatives written on some industrial loss index via utility indifference pricing. The industrial loss index is modelled by a compound Poisson process and the insurer can adjust her portfolio by choosing the risk loading, which in turn determines the demand. We compute the price of a…
Study models weather index insurance pricing by insurers and farmers, finding flexible pricing kernels boost profits.
Parametric insurance offers better risk-sharing in high-risk settings than traditional indemnity insurance.
Paper uses econometrics time series model with T-student Distribution for short-term load forecasting.
Deep learning boosts building energy load forecasting.
Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems. Though many STLF methods were proposed over the past decades, most of them focused on loads at high aggregation levels only. Thus, low-aggregation load forecast still requires further research and development. Comp…
New method infers viral load from pooled tests.
We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback: partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feed…
With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. In recent years, the WECC composite load model (WECC CLM) has shown to effectively capture the dynamic load re…
This paper uses a diffusion model to forecast electrical loads with uncertainty.
Paper presents a method for probabilistic load forecasting using adaptive online learning.
New method predicts heat load in thermal grids using latent variables.
The paper is motivated by a problem concerning the monotonicity of insurance premiums with respect to their loading parameter: the larger the parameter, the larger the insurance premium is expected to be. This property, usually called loading monotonicity, is satisfied by premiums that appear in the literature. The inc…