Study Italian sovereign bonds pricing using multi-factor models.
problem Empirical analysis of multi-factor models for Italian sovereign bonds.
method Calibration of Cox-Ingersoll-Ross and Vasicek models using Kalman filter and maximum likelihood estimation.
result Optimization algorithms improve term structure fitting over 12 years, including financial crises.
Paper uses PCA to analyze Chinese sovereign bonds and discusses bond immunization.
problem Analyzing factors affecting Chinese sovereign bond yield changes.
method Applied Principal Component Analysis (PCA) on bond yield data.
result Identified principal factors influencing Chinese sovereign bond yield changes.
Model prices sovereign contingent convertible bonds during crises.
problem Pricing Sovereign Contingent Convertible bonds (S-CoCo) during crises.
method Model CDS spread regime switching as a hidden Markov process, coupled with a mean-reverting stochastic process. Use Longstaff-Schwartz American option pricing framework for simulation.
result Computed future state contingent S-CoCo prices for risk management.
Study examines how COVID-19 affects bond yields using network filtering methods.
problem Impact of COVID-19 on sovereign bond yields.
method Network filtering methods applied to a correlation matrix of sovereign bond yields.
result Mean correlation decreases across all filtering methods during the COVID-19 period.
The study examines how climate risk influences sovereign debt default decisions.
problem The relationship between climate risk and sovereign debt default decisions.
method Calibration of a model to analyze the credit spreads of sovereign bonds and the impact of climate vulnerability on bond spreads.
result Climate risk does not significantly influence the decision to default on sovereign debt.
This paper examines how ESG factors influence sovereign bond yields and credit ratings.
problem The impact of ESG factors on sovereign bond yields and credit ratings is not fully understood.
method The study identifies relevant ESG indicators and compares their importance in bond pricing and credit ratings.
result ESG factors, particularly the G and S pillars, are more important for credit ratings than the E pillar.
This paper tackles missing data in Burundian bond market yield curves.
problem Missing data challenges accurate yield curve construction in Burundian sovereign bond market.
method Exploration of data limitations, proposing and testing various imputation methods (LR, Previous value, miss-Forest, Next value).
result Linear Regression method performs best across variables, approximating normal distribution for error values.
Investigates modeling emerging market bonds as FtD baskets.
problem Modeling emerging market corporate bonds as FtD baskets.
method Uses structural model to modify FtD basket approach.
result Approach feasible for modeling emerging market bonds.
Formula calculates bond prices between payments.
problem No new bond pricing formula available.
method Closed-form formula derivation.
result Formula accurately calculates bond prices.
We model bond's price curves corresponding to the sovereign uruguayan debt nominated in USD, as an alternative to the official bond prices publication released by the Central Bank of Uruguay (CBU). Four different gaussian models are fitted, based on historical data issued by the CBU, corresponding to some of the more f…
We use principle component analysis (PCA) of cross correlations in European government bonds and European stocks to investigate the systemic risk contained in the European economy. We tackle the task to visualize the evolution of risk, introducing the conditional average rolling sum (CARS). Using this tool we see that …
Study on systemic risk in European insurance sector, showing insurer connections during stress.
problem Understanding systemic risk connectedness in European insurance sector.
method Common connectedness framework applied to returns, volatility, value-at-risk, and expected shortfall.
result Insurers are a significant component of systemic risk connectedness, especially during stress episodes.
We introduce a class of interest rate models, called the α-CIR model, which gives a natural extension of the standard CIR model by adopting the α-stable L{é}vy process and preserving the branching property. This model allows to describe in a unified and parsimonious way several recent observations on the sovereign …
The paper predicts financial markets using news text and semantic network analysis.
problem Predicting financial markets with news data.
method Semantic network analysis of news text to assess economic keywords' importance.
result The index captures financial market phases and predicts returns and volatilities.
BondBERT improves sentiment analysis for bond markets.
problem Misleading sentiment models for bond markets due to domain-specific dynamics.
method Fine-tuned transformer model on bond-specific news.
result BondBERT produces positive correlations with bond returns and higher forecasting accuracy.
Study assesses risks of European Safe Bonds using credit risk models.
problem Risks associated with European Safe Bonds and related securities.
method Affine credit risk model with regime switching.
result ESBies are not truly risk-free, impacting market and policy implications.
Italian banks use swaps to hedge against rising interest rates, offsetting losses on debt securities.
problem Interest rate risk on Italian banks' debt securities.
method Analysis of granular regulatory data on euro interest rate swap trades.
result Swaps can offset losses on debt securities, reducing interest rate exposure.
Copula models for sovereign ratings improved by incorporating climate risk.
problem Modeling nonlinear dependence and clustering in sovereign rating migrations.
method Mixed-difference transformation, MAGMAR(1,1) copula process, consistent and asymptotically normal estimators.
result Gumbel MAGMAR(1,1) specification outperforms other models in empirical performance.
Study finds no significant impact of US sovereign credit rating downgrade on equity market.
problem Impact of US sovereign credit rating downgrade on US equity market.
method Event study methodology using three companies and S&P500 index.
result No significant effects of US sovereign credit rating downgrade on US equity market.
Geopolitical and geoeconomic shocks affect sovereign risk differently, with distinct transmission channels.
problem Understanding how geopolitical and geoeconomic shocks impact sovereign credit risk.
method Daily panel data of 42 economies over 2018-2025; semistructural framework; Shapley-Taylor decomposition; machine learning predictions; placebo and sign-restricted SVAR evidence.
result Geopolitical shocks primarily increase sovereign credit spreads through direct repricing, while geoeconomic shocks mainly affect spreads through financial conditions and policy uncertainty.
This study uses machine learning to predict sovereign credit ratings and identifies key factors.
problem Predicting sovereign credit ratings and identifying important factors.
method Used Multilayer Perceptron (MLP), Classification and Regression Trees (CART), Support Vector Machines (SVM), Naïve Bayes (NB), and Ordered Logit (OL) models.
result MLP is the best model for predicting sovereign credit ratings with a 68% accuracy.
Optimizes financial portfolios to minimize systemic risk.
problem Minimizing systemic risk in financial markets.
method Network optimization to rearrange overlapping portfolios.
result Systemic risk can be reduced by more than a factor of two without harming individual banks.
Dataset of Italian municipalities' income taxes from 2007-2011.
problem Understanding the economic structure of Italian municipalities.
method Annual aggregated income taxes of all Italian municipalities, clustered by regions and provinces.
result Data useful for economic comparisons and understanding municipal structures.
Possible solution of problem of sovereign debts is suggested. At the current moment this solution still can be provided only by methods of the world monetary policy.
BERTino is a lightweight Italian DistilBERT model for NLP tasks.
problem High computational and memory demands of large language models.
method Developed a DistilBERT model tailored for Italian language.
result F1 scores comparable to BERTBASE with significant speed improvements.
We extend the "No-dynamic-arbitrage and market impact"-framework of Jim Gatheral [Quantitative Finance, 10(7): 749-759 (2010)] to the multi-dimensional case where trading in one asset has a cross-impact on the price of other assets. From the condition of absence of dynamical arbitrage we derive theoretical limits for t…
Tether's dominance in U.S. Treasury bills lowers bond yields by 24 basis points.
problem Impact of Tether's market share on U.S. Treasury bill yields.
method Baseline semi-log time trend model and threshold regression analysis.
result Tether's market share reduces 1-month yields by 24 basis points.
Word2Vec embedding for Italian language developed.
problem Lack of word embedding for Italian language.
method Parameter tuning of Word2Vec for Italian language.
result An embedding for Italian language created.
The global financial crisis, beginning in 2008, took an historic toll on national economies around the world. Following equity market crashes, unemployment rates rose significantly in many countries: Italy was among those. What will be the impact of such large shocks on Italian healthcare finances? An empirical model f…
Recently, there has been a growing interest in network research, especially in these fields of biology, computer science, and sociology. It is natural to address complex financial issues such as the European sovereign debt crisis from the perspective of network. In this article, we construct a network model according t…
One of the main issues affecting the Italian NHS is the healthcare deficit: according to current agreements between the Italian State and its Regions, public funding of regional NHS is now limited to the amount of regional deficit and is subject to previous assessment of strict adherence to constraint on regional healt…
Data science reveals unexpected tax manipulation patterns in Italian regions.
problem Detecting tax income manipulation in Italian municipalities.
method Adopting Benford's first digit law to analyze tax data.
result Marked disparities found in tax distributions across regions, contrary to expectations.
Study predicts firm defaults using machine learning on Italian credit data.
problem Predicting firm defaults to inform bank lending policies.
method Used large granular credit data from Italian Central Credit Register, combined with public balance sheet data, and applied ensemble techniques and random forest models.
result Ensemble techniques and random forest provide the best results for predicting firm defaults.
Study fills the Italian pension gap with an optimal investment strategy.
problem Italian pension system reform gap between old and new pensions.
method Stochastic Optimal Control approach to fill the pension gap.
result It is possible to cover the pension gap with additional income from a pension scheme.
The paper develops a model for sovereign debt dynamics with explicit maturity structure.
problem Analyzing the sustainability and risk of long-term sovereign debt issuance.
method Discrete-time model with explicit maturity structure, deterministic and stochastic extensions.
result The model identifies conditions for ergodic convergence and derives analytical formulas for key metrics.
Deep learning improves macroeconomic forecasting and risk assessment.
problem Improving accuracy in macroeconomic forecasting and sovereign risk assessment.
method Nowcasting and forecasting using deep learning techniques.
result Deep learning methods outperform traditional econometric techniques in out-of-sample performance.
In this paper, we search whether the Benford's law is applicable to monitor daily changes in sovereign Credit Default Swaps (CDS) quotes, which are acknowledged to be complex systems of economic content. This test is of paramount importance since the CDS of a country proxy its health and probability to default, being a…
Study compares ZBDT model to BDT for financial derivatives valuation.
problem Valuation of financial derivatives under catastrophic events.
method Introduced Zero Black-Derman-Toy (ZBDT) model with jumps to zero interest rate.
result ZBDT model better matches financial slowdown risk.
Covid lockdown increased interest in Italian stock market, leading to new investors.
problem Impact of Covid lockdown on Italian stock market investors.
method Analysis of trading activity and investor demographics before and during lockdown.
result New investors during lockdown were more skilled traders than pre-lockdown investors.
Using a data set which includes all transactions among banks in the Italian money market, we study their trading strategies and the dependence among them. We use the Fourier method to compute the variance-covariance matrix of trading strategies. Our results indicate that well defined patterns arise. Two main communitie…
Deep Learning predicts e-commerce activity from Italian enterprise websites.
problem Predicting e-commerce activity from Italian enterprise websites.
method Developed a sophisticated processing pipeline using Convolutional Neural Networks and Word Embeddings.
result Deep Learning outperforms traditional Machine Learning methods for text classification.
New clustering method for financial data with known cluster number.
problem Clustering financial data with known number of clusters.
method Introduced a covariance-based dissimilarity measure for multifractional Brownian motions.
result Asymptotically consistent clustering algorithms for multifractional Brownian motions.
We investigate the shape of the Italian personal income distribution using microdata from the Survey on Household Income and Wealth, made publicly available by the Bank of Italy for the years 1977--2002. We find that the upper tail of the distribution is consistent with a Pareto-power law type distribution, while the r…
Italy and the Eurozone are heading in the year 2012 into a financial depression of unprecedented magnitude, with a forthcoming multitude of often contradictory public economic and financial stability emergency interventions whose ultimate endogenous and exogenous effects on public and private health spending and on the…
Study of bonded knots and braids with new algebraic models.
problem Classifying and understanding physical or chemical bonds in knots and braids.
method Developed new algebraic models (bonded knots, braids, braidoids) and invariants.
result New algebraic structures and invariants for bonded knots and braids.
This study assesses risk concentration in MDB portfolios using Monte Carlo simulations.
problem Risk concentration in MDB portfolios of a few borrowers.
method Realistic MDB portfolio simulations and Monte Carlo analysis.
result Current risk adjustments may be overly conservative.
Machine learning models predict housing prices using macroeconomic factors.
problem Predicting housing prices using macroeconomic data.
method Used machine learning (kNN and tree-bagging) on a dataset of macroeconomic factors.
result Machine learning models can predict housing prices with uncertainties better than existing index uncertainties.
Developed algebraic theory of bonded braids, proving Markov theorem.
problem Studied bonded knots and their algebraic properties.
method Introduced bonded braid monoid, proved Alexander and Markov theorems.
result Every bonded knot is the closure of a bonded braid, and equivalent knots have related braid representatives.