Develops a new method for financial term structure modeling.
problem Analyzing financial term structures with discontinuities.
method Cylindrical stochastic integration approach.
result Establishes a Heath-Jarrow-Morton framework.
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48 results for “financial term structure”
Unified framework models multiple financial and insurance term structures.
problem Modeling multiple term structures in various markets.
method Extended Heath-Jarrow-Morton (HJM) approach under real-world probability.
result Characterization of local martingale deflators and existence of affine realizations.
Digital transformation boosts corporate financial asset allocation, especially short-term.
problem Understanding how digital transformation affects corporate financial decisions.
method Fixed-effects models and staggered DID design using A-share listed companies data.
result Digital transformation significantly promotes corporate financial asset allocation, more pronounced in short-term.
Predicting future stock market structure by combining social and financial network informationq-fin.ST
We demonstrate that future market correlation structure can be predicted with high out-of-sample accuracy using a multiplex network approach that combines information from social media and financial data. Market structure is measured by quantifying the co-movement of asset prices returns, while social structure is meas…
Characterizing asymmetric and bimodal long-term financial return distributions through quantum walksq-fin.ST
Quantum walk model captures asymmetry and bimodality in long-term financial returns.
problem Inadequate classical models for long-term financial return distributions.
method Discrete-time quantum walk model.
result Captures bimodal and asymmetric probability distributions.
Absence-of-Arbitrage (AoA) is the basic assumption underpinning derivatives pricing theory. As part of the OTC derivatives market, the CDS market not only provides a vehicle for participants to hedge and speculate on the default risks of corporate and sovereign entities, it also reveals important market-implied default…
Unified framework for complex financial networks using lattice theory.
problem Complex financial networks with multiple currencies and dependencies.
method Recast classical financial clearing model into lattice liability networks.
result Lattice-valued clearing sections form a complete lattice, enabling tractable analysis.
Due to the lack of reliable market information, building financial term-structures may be associated with a significant degree of uncertainty. In this paper, we propose a new term-structure interpolation method that extends classical spline techniques by additionally allowing for quantification of uncertainty. The prop…
Identifies smooth curves for financial models.
problem Consistent term structures with flexible diffusion.
method Analyzes manifolds of curves for Heath-Jarrow-Morton models.
result Term structures cannot be affine but must be linear-rational.
Cross-border equity and long-term debt securities portfolio investment networks are analysed from 2002 to 2012, covering the 2008 global financial crisis. They serve as network-proxies for measuring the robustness of the global financial system and the interdependence of financial markets, respectively. Two early-warni…
New method identifies precursors of financial crises in market correlation structures.
problem Predicting long-term financial crises in non-Markovian, non-stationary markets.
method Identifying quasi-stationary market states and their precursor properties.
result Certain features of market states show potential as indicators of financial crises.
Interbank markets are often characterised in terms of a core-periphery network structure, with a highly interconnected core of banks holding the market together, and a periphery of banks connected mostly to the core but not internally. This paradigm has recently been challenged for short time scales, where interbank ma…
We present a class of Lévy processes for modelling financial market fluctuations: Bilateral Gamma processes. Our starting point is to explore the properties of bilateral Gamma distributions, and then we turn to their associated Lévy processes. We treat exponential Lévy stock models with an underlying bilateral Gamma pr…
The proposed model is aimed to reveal important patterns in the behavior of a simplified financial system. The patterns could be detected as regular cycles consisting of debt bubbles and crises. Financial cycles have a well defined structure and form periodic sequences along the axis of credit expansion while retaining…
A new model integrates LSTM and copulas for high-dimensional financial data.
problem Modeling high-dimensional dependencies across financial markets.
method Variational LSTM with regular vine copulas.
result Outperforms benchmarks in cross-market portfolio forecasting.
Financial Atoms and Moleculesq-fin.GN
Atoms and molecules are important conceptual entities we invented to understand the physical world around us. The key to their usefulness lies in the organization of nuclear and electronic degrees of freedom into a single dynamical variable whose time evolution we can better imagine. The use of such effective variables…
In this paper, we consider a discrete time economy where we assume that the short term interest rate follows a quadratic term structure of a regime switching asset process. The possible non-linear structure and the fact that the interest rate can have different economic or financial trends justify the interest of Regim…
Model forecasts market structure from financial networks using machine learning.
problem Predicting market correlation structure from financial networks.
method Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST), Dynamic Threshold Networks (DTN).
result Model improves market structure forecasting by up to 40% over benchmarks.
TimeMixer predicts global financial asset volatility, excelling in short-term forecasts.
problem Predicting volatility in global financial markets is challenging due to complexity and non-linear dynamics.
method Uses TimeMixer, a multiscale-mixing model for forecasting across different scales.
result TimeMixer performs exceptionally well in short-term volatility forecasting but less so in longer-term predictions.
Study shows long-term debt impacts financial growth of non-financial firms listed at Nairobi Securities Exchange.
problem Declining financial performance and reluctance to lend to non-financial firms listed at Nairobi Securities Exchange.
method Descriptive and panel data analysis of 45 non-financial firms over 10 years.
result Long-term debt positively and significantly influences financial growth measured by earnings per share and market capitalization.
Study assesses short-term debt's impact on non-financial firms' financial growth.
problem Declining financial performance and reluctance to lend to non-financial firms listed at Nairobi Securities Exchange.
method Explanatory research design, descriptive statistics, and panel data analysis.
result Short-term debt positively and significantly influences financial growth.
We study some properties of eigenvalue spectra of financial correlation matrices. In particular, we investigate the nature of the large eigenvalue bulks which are observed empirically, and which have often been regarded as a consequence of the supposedly large amount of noise contained in financial data. We challenge t…
TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusioncs.LG
TransBoost improves financial inclusion by evaluating individual financial risk.
problem Evaluating individual financial risk for new users and companies with limited data.
method Combines tree-based models and kernel methods, with parallel tree structure and efficient weights updating.
result Outperforms other transfer learning algorithms in prediction accuracy and robustness.
Enhanced multivariate GARCH model using LSTM for better volatility forecasting.
problem Limitations of traditional multivariate GARCH in capturing persistent volatility and co-movement.
method Integrates deep learning (LSTM) into multivariate GARCH models to capture nonlinear and dynamic dependence structures.
result Superior out-of-sample portfolio risk forecast compared to traditional methods.
Predicts financial asset dependencies using spatiotemporal patterns.
problem Complex dependency structures in financial assets for risk mitigation.
method Proposes Asset Dependency Matrix (ADM) and Asset Dependency Neural Network (ADNN) with ConvLSTM for spatiotemporal asset dependency prediction.
result ADNN outperforms baselines in predicting asset dependencies and their applications.
Study examines financial structure's impact on non-financial firms' growth in Kenya.
problem Declining financial performance and growth of non-financial firms listed at Nairobi Securities Exchange.
method Analyzes the effect of financial structure on financial growth.
result Established the impact of financial structure on non-financial firms' growth.
Develops information geometry for Lévy processes in finance.
problem Understanding the statistical properties of Lévy processes for financial modeling.
method Deriving -divergences from Lévy triplets, identifying Fisher information matrix and -connection.
result Identifies statistical implications and differential-geometric structures of Lévy processes.
The aim of this paper is to present a dual-term structure model of interest rate derivatives in order to solve the two hardest problems in financial modeling: the exact volatility calibration of the entire swaption matrix, and the calculation of bucket vegas for structured products. The model takes a series of long-ter…
Unified model for financial derivatives pricing with stochastic interest rates.
problem Pricing and hedging financial derivatives with stochastic interest rates.
method Volterra Stein-Stein model with correlated Gaussian Volterra processes.
result Explicit formulas for bond and cap/floor pricing, and characteristic function for log-forward index.
Gaussian Processes enhance financial forecasting by predicting mean-reverting time series with probability distributions.
problem Accurate long-term financial predictions with probability distributions.
method Functional and augmented data structures for Gaussian Processes.
result Gaussian Processes offer improved long-term predictions with probability distributions.
We consider the problem of modelling the term structure of defaultable bonds, under minimal assumptions on the default time. In particular, we do not assume the existence of a default intensity and we therefore allow for the possibility of default at predictable times. It turns out that this requires the introduction o…
The paper analyzes word embeddings and their failure to distinguish polarized terms.
problem Word embeddings fail to correctly distinguish terms with opposite polarities.
method Mathematical analysis of word2vec model, synthetic corpus generation, empirical assessment.
result Word embeddings treat antonyms as frequentist synonyms, leading to mixed polarity terms.
We introduce an event based framework of directional changes and overshoots to map continuous financial data into the so-called Intrinsic Network - a state based discretisation of intrinsically dissected time series. Defining a method for state contraction of Intrinsic Network, we show that it has a consistent hierarch…
Optimal B-robust estimate is constructed for multidimensional parameter in drift coefficient of diffusion type process with small noise. Optimal mean-variance robust (optimal V -robust) trading strategy is find to hedge in mean-variance sense the contingent claim in incomplete financial market with arbitrary informatio…
The paper optimizes insurance purchases for financial goals.
problem Maximizing probability of achieving financial goals with insurance.
method Analyzes deferred term insurance in deterministic and stochastic frameworks, considering income, consumption, and risky investment.
result Provides optimal insurance and investment strategies for achieving financial goals.
We propose a stylized model of production and exchange in which long-term investors set their production decision over a horizon τ , the "time to produce", and are liquidity constrained, while financial investors trade over a much shorter horizon δ (<< τ ) and are therefore more duly informed on the exogenous shocks af…
Combines CNN and Transformer for financial time series forecasting.
problem Forecasting financial time series, especially stock prices, is challenging due to short-term and long-term dependencies.
method Uses CNN for short-term dependencies and Transformer for long-term dependencies.
result Demonstrated superior performance in forecasting stock price changes compared to traditional methods.
Simplicial persistence of financial markets: filtering, generative processes and portfolio riskq-fin.ST
Simplicial persistence measures financial market dynamics, revealing long-term structure evolution.
problem Understanding the long-term structure evolution of financial markets.
method Simplicial persistence, null models, TMFG filtering, thresholding, generative process analysis.
result More liquid markets exhibit slower persistence decay, suggesting higher fragility to systemic shocks.
Study uses LSTM models to detect Wyckoff patterns in currency trading.
problem Understanding market dynamics and identifying trading opportunities.
method Dissecting Wyckoff Phases, using CNNs for spatial data and LSTM for temporal data.
result Deep learning models enhance pattern recognition in financial markets.
Quantifying dynamics of the financial correlationscond-mat.soft
A novel application of the correlation matrix formalism to study dynamics of the financial evolution is presented. This formalism allows to quantify the memory effects as well as some potential repeatable intradaily structures in the financial time-series. The present study is based on the high-frequency Deutsche Aktie…
This paper intends to meet recent claims for the attainment of more rigorous statistical methodology within the econophysics literature. To this end, we consider an econometric approach to investigate the outcomes of the log-periodic model of price movements, which has been largely used to forecast financial crashes. I…
Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant d…
Modeling financial systemic risk with optimal control theory for stability.
problem Analyzing and stabilizing systemic risk in interconnected financial entities.
method Developed a theoretical model using optimal control theory, including steps for synthesizing stabilizing controllers.
result The model ensures that the norms of the mappings from disturbance to output are less than a predefined constant, stabilizing the system.
The study examines volatility models and finds decoupling of short- and long-term correlation structures.
problem Understanding the dynamic of volatility at different time scales.
method Developed a composite likelihood estimation framework for parametric continuous-time stationary Gaussian processes.
result The short- and long-term correlation structures of stochastic volatility are decoupled.
Could short selling make financial markets tumble?cond-mat.stat-mech
It is suggested to consider long term trends of financial markets as a growth phenomenon. The question that is asked is what conditions are needed for a long term sustainable growth or contraction in a financial market? The paper discuss the role of traditional market players of long only mutual funds versus hedge fund…
MS-CASTLE learns causal structures across multiple time scales.
problem Inferring causal relationships between time series data at different scales.
method Uses stationary wavelet transform and non-convex optimization to estimate causal structures.
result MS-CASTLE reveals meaningful causal interactions, especially at mid-term time resolutions.
Develops a framework to analyze financial structures.
problem Difficulty in systematic analysis, comparison, and verification of financial structures.
method Formalizes financial structures as structured allocation systems with explicit allocation operators.
result Specifies inputs, structural requirements, and feasibility restrictions for financial structures.
CoCos can increase financial fragility in certain network structures.
problem The effectiveness of CoCos in enhancing financial stability depends on the network structure.
method Analysis of phase transitions in a network of interconnected banks.
result CoCos can increase financial fragility under certain network structures.