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.
Regularized mixtures improve inflation and interest rate forecasts, especially correcting overconfidence.
A robust machine learning approach forecasts U.S. Treasury yields, reducing risk for investors.
The aim of this paper is to propose a new methodology that allows forecasting, through Vasicek and CIR models, of future expected interest rates (for each maturity) based on rolling windows from observed financial market data. The novelty, apart from the use of those models not for pricing but for forecasting the expec…
Chronos models improve financial forecasting by integrating multivariate data.
The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Geostatistical models and machine le…
Simple models outperformed sophisticated ones in forecasting Turkish lira exchange rates.
DGNN predicts financial margin calls under stress tests.
We develop a new DTSM with nonlinearities using Gaussian Processes for better interest rate forecasting.
The paper models exchange rate risk premium using mean-reverting dynamics.
The Basel II Accords have sparked increased interest in the development of approaches based on internal ratings systems and have initiated the elaboration of models for remote ratings forecasts based on external ones as part of Risk Management and Early Warning Systems. This article evaluates the peculiarities of curre…
Study improves cryptocurrency volatility forecasting using multiple data sources.
Paper uses machine learning to forecast significant currency exchange rate fluctuations.
Model predicts default risk based on company's financial forecasts and credit conditions.
The paper studies estimation of parameters of diffusion market models from historical data. The standard definition of implied volatility for these models presents its value as an implicit function of several parameters, including the risk-free interest rate. In reality, the risk free interest rate is unknown and need …
This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the influence of different timescales on volatility forecasts. The decomposition of …
We establish optimal rates for online regression for arbitrary classes of regression functions in terms of the sequential entropy introduced in (Rakhlin, Sridharan, Tewari, 2010). The optimal rates are shown to exhibit a phase transition analogous to the i.i.d./statistical learning case, studied in (Rakhlin, Sridharan,…
This paper establishes minimax rates for online regression with arbitrary classes of functions and general losses. We show that below a certain threshold for the complexity of the function class, the minimax rates depend on both the curvature of the loss function and the sequential complexities of the class. Above this…
Recent literature seek to forecast implied volatility derived from equity, index, foreign exchange, and interest rate options using latent factor and parametric frameworks. Motivated by increased public attention borne out of the financialization of futures markets in the early 2000s, we investigate if these extant mod…
We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for efficient operation of power grid systems. In this work, we use a synthetic data set describing a power grid with 700 buses and 134 generat…
Bayesian model predicts interest rates with short-term accuracy and long-term stability.
We present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture. The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning is perform…
Study examines time-varying betas and their volatility in bank interest income and expense margins.
The paper proposes a new method to estimate interest rates consistently under both risk-neutral and real-world measures.
Study compares multivariate scoring rules for distribution forecasts.
BAVART model combines VAR and BART for non-linear forecasting.
Model forecasts motor vehicle collision rates with high accuracy.
We develop methods to estimate lag and parameters for multiple stable autoregressive processes.
BiHRNN predicts inflation by leveraging hierarchical structure and bidirectional RNNs.
Study uses zero-shot models to forecast mortality rates globally.
An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world however, Taylor rule parameters may be subject to structural instabilities, for example during the Global Financial Crisis. This paper forecasts exchange rates using such Taylor rules with Time V…
Several studies have established the predictive power of the yield curve in terms of real economic activity. In this paper we use data for a variety of E.U. countries: both EMU (Germany, France, Italy) and non-EMU members (Sweden and the U.K.). The data used range from 1991:Q1 to 2009:Q1. For each country, we extract t…
The article reviews scoring rules for estimating and evaluating forecasts.
Study enhances financial forecasting with machine learning and fuzzy MCDM.
This paper rates robustness of multi-modal time-series forecasting models.
Study predicts bond yields using machine learning and ultimate forward rates.
In this paper, we propose a stochastic investment model for actuarial use in South Africa by modelling price inflation rates, share dividends, long term and short-term interest rates for the period 1960-2018 and inflation-linked bonds for the period 2000-2018. Possible bi-directional relations between the economic seri…
This paper proposes new GARCH models for cryptocurrency volatility, showing skewed distributions improve prediction accuracy.
IUS framework predicts EUR/USD exchange rate with improved accuracy.
The problem of automatic and accurate forecasting of time-series data has always been an interesting challenge for the machine learning and forecasting community. A majority of the real-world time-series problems have non-stationary characteristics that make the understanding of trend and seasonality difficult. Our int…
This paper models yearly exchange rates between USD/KZT, EUR/KZT and SGD/KZT, and compares the actual data with developed forecasts using time series analysis over the period from 2006 to 2014. The official yearly data of National Bank of the Republic of Kazakhstan is used for present study. The main goal of this paper…
This article applies a long short-term memory recurrent neural network to mortality rate forecasting. The model can be trained jointly on the mortality rate history of different countries, ages, and sexes. The RNN-based method seems to outperform the popular Lee-Carter model.
Traditional methods outperform LLMs in forecasting corporate credit ratings.
Neural ARFIMA model improves exchange rate forecasting for BRIC economies.
US Yield curve has recently collapsed to its most flattened level since subprime crisis and is close to the inversion. This fact has gathered attention of investors around the world and revived the discussion of proper modeling and forecasting yield curve, since changes in interest rate structure are believed to repres…
Study improves crash rate forecasting in Washington, D.C. using stochastic volatility model.
This paper compares traditional econometric and contemporary machine/deep learning techniques for forecasting foreign exchange rates.
Credibility theory provides tools to obtain better estimates by combining individual data with sample information. We apply the Credibility theory to a Uniform distribution that is used in testing the reliability of forecasting an interest rate for long term horizons. Such empirical exercise is asked by Regulators (CRR…
This paper extends forecast reconciliation to non-linearly constrained time series.