Fixed Income Markets

The classical continuous univariate probability distributions, which contain one or two parameters, have been observed to break down when complexities exist in the structure of a data set such as when outliers are present, alongside observations centered around the mean. When a data set exhibits heterogeneity or exists in a multicomponent form and it becomes impossible to use a single probability distribution to capture the distinct components of the data set, using a composite distribution to model the data set becomes plausible. This situation has led to the formulation of various hybrid or composite models where each component of the hybrid model handles the specific part of the data set that it is well suited for. Furthermore, the approach or method used in the formulation of these hybrid models plays a vital role in determining how meaningful the results obtained from them are. Several approaches or methods for formulating hybrid distributions have appeared in the literature, each with their own pros and cons. We present in this paper a general two-component hybrid model for fitting heterogeneous heavy-tailed data sets with tails to the right. The functional form of the two-component hybrid family is specified by the probability density function (pdf), cumulative distribution function (cdf) and the quantile function. Three members of the family using three different distributions for the right tail are presented. A formal method based on maximum likelihood for the estimation of the parameters of the models belonging to the family is also presented. A Monte Carlo simulation study is carried out to determine the efficiency of the estimation method. An application to a real data set in finance is performed.

Green bonds became a key financial tool for financing sustainable development by financing green projects. The impact of green bond issuance on economic growth, investor participation in the Indian market, as well as on sustainability outcomes visa -vis regulatory incentives, investor confidence in turn driving green project selection, is presented in this study. The study uses a dataset with 400 samples and uses descriptive statistics, correlation, and regression modeling to evaluate the economic effect of green bonds in various sectors, such as renewable energy, cleansing movement, and sustainable infrastructure. The results show that green bond issuance has a substantial but heterogeneous effect on growth, and that policy incentives should be introduced to make the effect even stronger. Mixed Results in Investors-Investors Are More of a Positive Factor, because when there are Domestic Investors, We See Higher Significance and when we have an International Investor We See Negative Significance, Meaning its Relation with Before there More. These findings underscore the potential of different project categories, as well as the moderate contribution from various categories, potentially informing targeted project interventions in policy effectiveness. The results also indicate a strong correlation between the jobs created by green bonds and India's sustainable development goals, establishing green bonds as a key driver for long-term development and environmental improvements. This study highlights the importance of implementing standardization in policy, transparency, and diversification of investments in order to maximize the efficiency of India's green bond market. Overall, this suggests several avenues for future research, e.g., longitudinal studies assessing the impact of different green finance approaches, investor behaviour research and analysis of regulatory innovations to strengthen the green finance framework. With the establishment of strong policies, measures to instill confidence in investors, and efficient project implementation, green bonds can be an effective tool for India to counter-poise its climate commitments with sustainable development and economic growth.

This research studies the impact of macroeconomic announcement surprises on daily U.S. Treasury excess returns during the heart of Alan Greenspan's tenure as Federal Reserve Chair, addressing the possible limitations of standard static regression (SSR) models, which may suffer from omitted variable bias, parameter instability, and poor mis-specification diagnostics. To complement the SSR framework, an automated general-to-specific (Gets) modeling approach, enhanced with modern indicator saturation methods for robustness, is applied to improve empirical model discovery and mitigate potential biases. By progressively reducing an initially broad set of candidate variables, the Gets methodology steers the model toward congruence, dispenses unstable parameters, and seeks to limit information loss while seeking model congruence and precision. The findings, herein, suggest that U.S. Treasury market responses to macroeconomic news shocks exhibited stability for a core set of announcements that reliably influenced excess returns. In contrast to computationally costless standard static models, the automated Gets-based approach enhances parameter precision and provides a more adaptive structure for identifying relevant predictors. These results demonstrate the potential value of incorporating interpretable automated model selection techniques alongside traditional SSR and Markov switching approaches to improve empirical insights into macroeconomic announcement effects on financial markets.

of cross-border payments is bound to increase as the internal market establishes itself and possibly develops towards full economic and monetary union. ' An increase in the volume of payments brings with it an increase in risk. This applies not only at the consumer level, where, inter alia, the lack of transparency in conditions and techniques, the quality of performance of funds transfers, and "double-charging" by intermediary and beneficiary banks are a permanent issue-at least in the EU member countries. Of course, self-regulation would in theory be an ideal solution for eradicating problems encountered in funds transfers. However, whenever a transaction has a connection with more than one jurisdiction, additional uncertainties may exist as to what specific rules may be applicable in the context. One way of solving such potential conflicts of laws is to harmonize applicable rules. Harmonization greatly reduces the necessity to resort to domestic rules of private international law. At the same time, harmonization of rules reduces the risk that a problem will be treated and solved differently in other countries, thus also curtailing a tendency towards "forum shopping." In the field of payments, two major legal initiatives received a fair amount of international attention since they focused on harmonizing the rules that govern credit transfers, especially cross-border credit transfers: * a U.S. initiative to harmonize domestic rules governing wholesale wire credit transfers. This initiative resulted in the drafting of a new instrument, Article 4-A "Funds Transfers," to be incorporated into the Uniform Commercial Code (UCC). The new rules contained therein were subsequently adopted by a large number of U.S. states, most importantly New York, 2 and " an initiative by the United Nations Commission on International Trade Law (UNCITRAL) that resulted in the adoption and publication of Model Rules on International Credit Transfers in May 1992. 3 UNCITRAL's Model Law and Article 4A UCC are not the first and only instruments that aim to standardize certain issues arising in the context of interna-I. In March 1992 the volume of retail payments below ECU 2,500 was estimated at 200 million transactions: "Payment systems in Europe," opening address by Commissioner d'Achirafi at the European Finance Convention, 3rd December 1993. In the EU the Commission typically focuses on retail payments, whereas work on large-value payment systems has been undertaken by central banks; see infra parts Ill and IV. 2. The draft was finalized in August 1989. Article 4A was designed to be state, not federal, law. As of 1st January 1993, 42 U.S. states had made Article 4A part of their UCC (Alabama, Alaska, New Jersey, South Carolina, and Puerto Rico are considering enactment). The text also constitutes an Appendix to the Federal Reserve System's "Regulation J" applicable to funds transfers through "Fedwire," as amended with effect from 1st January 1991: 12 C.F.R. § 210, Subpart B. Article 4A is explicitly designated as the law governing Fedwire. In the United States, Fedwire, CHIPS, S.W.I.F.T., telex, and book transfers are covered by Article 4A. 3. See infra part 111. 10.

Recent years have seen substantial growth in cross-border banking activities. On the one hand, banks are increasingly seeking to establish subsidiaries, or at least offices, in countries other than their own. On the other hand, the commercial activities of banks, above all funds transfers, have expanded tremendously. This growth has occurred in terms of both the amount of individual payments and the total amounts "moved" from one entity to another. For instance, in 1978 S.W.I.F.T. was considered very successful because it linked about 500 banks in sixteen countries and had achieved an annual traffic volume of almost 25 million messages. Today S.W.I.F.T. handles the same number of messages in a few weeks. Within the European Union (EU) the volume Note: The American Bar Association grants permission to reproduce this article, or a part thereof, in any not-for-profit publication or handout provided such material acknowledges original publication in this issue of The International Lawyer and includes the title of the article and the name of the author.

In the present paper we study some existing duality features between two very known models in Risk Theory. The classical Cramér–Lundberg risk model with application to insurance, and the dual risk model with (some) financial application. For simplicity the former will be referred as the primal model. The former has been of extensive treatment in the literature, it assumes that a given surplus process has constant deterministic gains (premiums) and random loses (claims) that come at random times. On the other hand, the latter, called as dual model, works in opposite direction, losses (costs) are constant and deterministic, and the gains (earnings) are random and come at random times. Sometimes this one is called the negative model. Similar quantities, with similar mathematical properties, work in opposite direction and have different meanings. There is however an important feature that makes the two models quite distinct, either in their application or in their nature: the loading co...

For actuarial applications we consider the Sparre Andersen risk model when the interclaim times follow a Phase Type distribution, PH(n). First of all we focus our attention on the generalized Lundberg's equation to determine the cases when multiple roots can arise, with the highest possible level of accuracy. Second, we study the linear independence of the eigenvectors related to the Lundberg's matrix. Finally we apply our results to compute the ultimate and nite time ruin probabilities, the probability of arrival to a barrier prior to ruin, severity of ruin and its maximum, the expected discounted future dividends, among others.

In this work-in-progress, we consider perturbed risk processes that have an underlying Markov structure, including Markovian risk processes, and Sparre-Andersen risk processes when both inter claim times and claim sizes are phase-type. We apply the Erlangization method to this risk process in order to obtain an accurate approximation of the finite time ruin probability. In addition, we recognize a repeating structure in the probability matrices we work with. Numerical examples illustrate the use of the model.

This article investigates the key determinants of corporate bond performance in Ghana, distilling a comprehensive thesis into a concise analysis. Using an explanatory research design with panel data from 12 listed firms (2015-2019), the work examines how macroeconomic conditions and firm-specific factors influence corporate bond outcomes. Regression and cointegration techniques reveal that inflation rate, interest rate, exchange rate, government expenditure, firm size, corporate tax rate, and total debt levels all significantly shape corporate bond performance. Notably, stable inflation and interest rates, a favorable tax environment, and larger firm size are associated with improved corporate bond metrics, explaining about 88% of the variation in performance. These findings underscore the importance of macroeconomic stability and prudent corporate financial management in developing Ghana's corporate bond market. The article offers fresh, policy-relevant insights: regulators should ensure a stable economic environment (particularly price and exchange-rate stability) and strengthen market infrastructure, while firms and investors can leverage these drivers for better financing outcomes. We conclude with recommendations for policymakers, market participants, and academics to foster a more vibrant corporate bond market in Ghana.

In the modern business era where the financial services integrate environmental, social, and governance (ESG) models and investment decisions influence sustainability, social bonds are a recent emerging financial tool. A financial instrument that allows private funders to upfront capital for public ventures that deliver societal and environmental outcomes. As a twig of green bond and sustainable category, social bonds are proving their separate nature of equal viability in terms of both issuers and investors. Despite being recently developed categories; social bonds have experienced a noteworthy track in the international financial markets. Oman recently has strategies of sustainability and weighs legislative plans to enable issuance of green, blue, and social bonds. As a part of the country's incessant growth efforts to improve and expand the capital market, these fundraising tools are being issued to meet the issuers and investors' needs. While still in its developmental years and a late entry to the capital market, a few social bond draft regulations have already been set up in the Sultanate of Oman. Hence, this paper attempts to present some related concepts of social bonds and their international experiences of growth in the initial years globally. Also tries to present the opportunities and challenges of social bond mechanism and issue process in the Sultanate of Oman.

As the increasing application of AI in finance, this paper will leverage AI algorithms to examine tail risk and develop a model to alter tail risk to promote the stability of US financial markets, and enhance the resilience of the US economy. Specifically, the paper constructs a multivariate multilevel CAViaR model, optimized by gradient descent and genetic algorithm, to study the tail risk spillover between the US stock market, foreign exchange market and credit market. The model is used to provide early warning of related risks in US stocks, US credit bonds, etc. The results show that, by analyzing the direction, magnitude, and pseudo-impulse response of the risk spillover, it is found that the credit market's spillover effect on the stock market and its duration are both greater than the spillover effect of the stock market and the other two markets on credit market, placing credit market in a central position for warning of extreme risks. Its historical information on extreme risks can serve as a predictor of the VaR of other markets.

The rapid advancement of data-driven strategies has significantly transformed disease mitigation and global health preparedness. Leveraging artificial intelligence (AI), machine learning (ML), big data analytics, and digital health technologies, modern epidemiological frameworks enable real-time disease surveillance, predictive modeling, and targeted intervention strategies. This study explores emerging innovations in data-driven public health responses, focusing on the integration of diverse data sources, computational modeling, and decision support systems for pandemic preparedness and infectious disease control. The proposed framework consists of four core components: (1) Data Acquisition and Integration, which consolidates structured and unstructured data from electronic health records (EHRs), mobile health (mHealth) platforms, wearable devices, and genomic databases; (2) Predictive Analytics and Modeling, where AI and ML algorithms process vast datasets to identify outbreak patterns, transmission dynamics, and risk factors; (3) Digital Health and Decision Support Systems, which leverage cloud computing and Internet of Things (IoT) technologies to enhance early warning mechanisms and resource allocation; and (4) Policy Implementation and Global Health Strategies, which focus on improving real-time response, health equity, and international collaboration in disease prevention efforts. Recent applications of AI-driven disease surveillance in COVID-19, Ebola, and antimicrobial resistance management demonstrate the effectiveness of integrating multi-source data and computational epidemiology to guide strategic public health interventions. Despite the potential of data-driven solutions, challenges such as data privacy, interoperability, algorithmic biases, and ethical considerations must be addressed to ensure equitable healthcare outcomes. Future research should focus on refining predictive models, integrating real-time genomic surveillance, and enhancing the resilience of global health infrastructures. This study concludes that data-driven approaches play a crucial role in strengthening disease mitigation strategies, improving response efficiency, and fostering global health security. By harnessing AI, ML, and big data analytics, health systems can proactively detect emerging threats, optimize resource distribution, and improve pandemic resilience. Future advancements in computational epidemiology and digital health solutions will be critical in addressing evolving global health challenges.

The rapid advancement of data-driven strategies has significantly transformed disease mitigation and global health preparedness. Leveraging artificial intelligence (AI), machine learning (ML), big data analytics, and digital health technologies, modern epidemiological frameworks enable real-time disease surveillance, predictive modeling, and targeted intervention strategies. This study explores emerging innovations in data-driven public health responses, focusing on the integration of diverse data sources, computational modeling, and decision support systems for pandemic preparedness and infectious disease control. The proposed framework consists of four core components: (1) Data Acquisition and Integration, which consolidates structured and unstructured data from electronic health records (EHRs), mobile health (mHealth) platforms, wearable devices, and genomic databases; (2) Predictive Analytics and Modeling, where AI and ML algorithms process vast datasets to identify outbreak patterns, transmission dynamics, and risk factors; (3) Digital Health and Decision Support Systems, which leverage cloud computing and Internet of Things (IoT) technologies to enhance early warning mechanisms and resource allocation; and (4) Policy Implementation and Global Health Strategies, which focus on improving real-time response, health equity, and international collaboration in disease prevention efforts. Recent applications of AI-driven disease surveillance in COVID-19, Ebola, and antimicrobial resistance management demonstrate the effectiveness of integrating multi-source data and computational epidemiology to guide strategic public health interventions. Despite the potential of data-driven solutions, challenges such as data privacy, interoperability, algorithmic biases, and ethical considerations must be addressed to ensure equitable healthcare outcomes. Future research should focus on refining predictive models, integrating real-time genomic surveillance, and enhancing the resilience of global health infrastructures. This study concludes that data-driven approaches play a crucial role in strengthening disease mitigation strategies, improving response efficiency, and fostering global health security. By harnessing AI, ML, and big data analytics, health systems can proactively detect emerging threats, optimize resource distribution, and improve pandemic resilience. Future advancements in computational epidemiology and digital health solutions will be critical in addressing evolving global health challenges.

The market's risk neutral probability distribution for the value of an asset on a future date can be extracted from the prices of a set of options that mature on that date, but two key technical problems arise. In order to obtain a full well-behaved density, the option market prices must be smoothed and interpolated, and some way must be found to complete the tails beyond the range spanned by the available options. This paper develops an approach that solves both problems, with a combination of smoothing techniques from the literature modified to take account of the market's bid-ask spread, and a new method of completing the density with tails drawn from a Generalized Extreme Value distribution. We extract twelve years of daily risk neutral densities from S&P 500 index options and find that they are quite different from the lognormal densities assumed in the Black-Scholes framework, and that their shapes change in a regular way as the underlying index moves. Our approach is quite general and has the potential to reveal valuable insights about how information and risk preferences are incorporated into prices in many financial markets.

This paper considers a family of distributions constructed by a stochastic mixture of the order statistics of a sample of size two. Various properties of the proposed model are studied. We apply the model to extend the exponential and symmetric Laplace distributions. An extension to the bivariate case is considered.

The fixed income trading landscape has undergone significant transformation due to technological advancements, globalization, and evolving regulatory frameworks. This paper explores innovative strategies for reshaping fixed income markets, including algorithmic trading, artificial intelligence, machine learning, and decentralized finance. These innovations have enhanced market liquidity, efficiency, and transparency, while also introducing challenges such as cybersecurity risks, ethical considerations, and regulatory complexities. By analyzing emerging trends and their implications, the paper highlights the transformative potential of these strategies and their impact on global financial markets. It also provides strategic recommendations for traders, regulators, and investors to navigate this dynamic environment effectively. Collaborative efforts and a balanced approach between innovation and regulation are essential to ensure sustainable growth and equitable market practices.

This report analyzes a bond issued by Apple Inc., applying fixed-income valuation principles and theoretical concepts. The focus is on understanding the bond's credit profile, comparing it with a hypothetical preferred share issued by Apple, and conducting calculations for bond pricing and yield to maturity. The report also evaluates the bond's sensitivity to interest rate changes, providing insights into the associated risks and opportunities. Through this analysis, the aim is to advise a portfolio manager on the suitability of this fixed-income asset for a diversified investment portfolio, as well as applying the knowledge and techniques studied in the Equity and Fixed Income module.

Modern portfolio management frequently employs duration-targeting strategies to optimize the trade-off between risk and return. These strategies maintain a stable portfolio duration through periodic rebalancing, preserving a consistent exposure to interest rate fluctuations. Practical implementations include index-tracking and ladder portfolios. As demonstrated by Martin L. Leibowitz and his co-authors, a key feature of duration targeting is its ability to concentrate return distributions around a minimum-variance point, which occurs when the portfolio's duration is approximately half the investment horizon. In this study, we analyze stochastic interest rate models in the context of constant-duration portfolios. The analysis explores cases where the yield curve is both flat and of an arbitrary shape.

In this paper we derive an analytical formula for the variance of the hedging error for European options when hedging discretely with equal time steps. The result is further generalized to a portfolio of options with different maturities and strikes.

A tanulmány a Győri Járműipari Körzet, mint a térségi fejlesztés új iránya és eszköze, TÁMOP-4.2.2.A-11/1/KONV-2012-0010 projekt"Jövőt alakító kezdeményezések (stratégiák, források)" című résztémája keretében, annak támogatásával készült. Metadata, citation and similar papers at core.ac.uk

Sustainability-linked bonds represent one of the newest weapons of choice for firms raising money to reach their sustainability targets. On a sample of 252 corporate sustainability-linked bonds, we employ cross-sectional regressions to inspect the impact of a combination of factors on the cost of debt financing for the issuer, proxied by the yield at issuance. The results unveil that firms with higher step-up clauses in their sustainability-linked bonds do not benefit from reduced debt costs. In contrast, firms exhibit a lower cost of debt when the coupon step-up-related cash flows are paid for a longer period in case of corporate sustainability target failure. On a different note, market actors critically eye the ambitiousness of the targets, with companies experiencing higher yields at issuance when issuing sustainability-linked bonds with outdated sustainability benchmarks and easy-to-reach targets. This study is among the first to underscore the importance of the interplay between financial and qualitative factors in properly designing sustainabilitylinked bonds with financially material, ambitious, and time-sensitive sustainability targets to reduce the cost of debt financing.

In multivariate data modeling, the statistical analyst can desire to construct a multivariate distribution with correlated variables. For this reason, there is a need to generalize univariate distributions, but this generalization is not easy. Many methods have been presented for construction of continuous multivariate families with univariate distributions. Some of these methods are based on a single baseline, while others are based on more than one baseline, so that their variables are dependent. Some authors were interested in expanding a univariate transmuted family into multivariate case. Some suggestions were made about extension of univariate quadratic transmuted (QT) family to bivariate ones, and another modification was made to this family by replacing the (c.d.f.) with exponentiated (c.d.f.). Another construction of bivariate family is based on probability distribution of paired order Statistics for a sample size two drawn from quadratic ranked transmuted (QRT) margin, and this bivariate family allows for positive and negative dependence between variables. Another family proposed an extension of univariate mixture of standard continuous uniform, with decreasing densities to a bivariate case. Our proposed (CT2) reduces to a bivariate quadratic transmuted(QT2) family if the cubic transmutation parameters equal tozero. (CT2) family can be used for modeling positive andnegative correlated variables. Some statistical properties of (CT2) family have been studied which comprise joint,marginal and conditional (c.d.f., p.d.f), joint, marginal and conditional moments, data generation and dependence coefficients. It is seen that (joint, marginal and conditional) moments depend on raw moments of (baseline variables and largest order statistics of samples sizes 2 and 3). The Egyptian bivariate economic data are fitted by (CT2Fr),(FGMFr), (T2Fr) and (DSASFr). The (CT2Fr) is the fit towhich has smallest (AIC) and (BIC) criteria.

Dans l'étude de la fiabilité d'un système, il est fréquent que les paramètres aléatoires du modèle de fiabilité soient dépendantes. L'évaluation de la probabilité de défaillance du système nécessite alors de connaître la loi jointe de ces paramètres, qui est souvent supposée inaccessible. La dépendance est alors décrite par une matrice de corrélation (linéaire ou des rangs), et une procédure algorithmique fournit ensuite une estimation de cette probabilité. Dans le cas de la méthode analytique FORM (First Order Reliability Method), cette procédure prend le nom de transformation de Nataf, (Nataf (1962) et Liu et al. (1986)). Après une rapide présentation de la théorie des copules, nous analysons la transformation de Nataf à la lumière de cette théorie, puis proposons une extension de cette transformation aux copules elliptiques. L'intérêt majeur de cette analyse est de montrer les conséquences de l'utilisation de la transformation de Nataf en terme de modélisation...

After Black and Scholes's groundbreaking work, the literature concerning pricing options has become a very important area of research. Numerous option valuation methods have been developed. This paper shows how one can compute option prices using Sign RCA-GARCH models for the dynamics of the volatility. Option pricing obtained from Sign RCA-GARCH models, the Black and Scholes's valuation and other selected GARCH option pricing models are compared with the market prices. This approach was illustrated by the valuation of the European call options on the WIG20 index. The empirical results indicated that RCA-GARCH and Sign RCA-GARCH models can be successfully used for pricing options. However none of the models can be indicated as the best one for the option valuations for every period and every time to maturity of the options.

After Black and Scholes's groundbreaking work, the literature concerning pricing options has become a very important area of research. Numerous option valuation methods have been developed. This paper shows how one can compute option prices using Sign RCA-GARCH models for the dynamics of the volatility. Option pricing obtained from Sign RCA-GARCH models, the Black and Scholes's valuation and other selected GARCH option pricing models are compared with the market prices. This approach was illustrated by the valuation of the European call options on the WIG20 index. The empirical results indicated that RCA-GARCH and Sign RCA-GARCH models can be successfully used for pricing options. However none of the models can be indicated as the best one for the option valuations for every period and every time to maturity of the options.

Pour analyser un jeu de données, un statisticien dispose de plusieurs outils. L'un des plus répandus est la régression, où l'on établit un lien entre une variable réponse et une ou plusieurs variables explicatives. Les types du lien peut-être paramétrique une famille de courbes possibles est choisit, puis les paramétrés sont estimés ou non paramétriques on choisit un espace de fonction plus vaste dans lequel on souhaite obtenir une courbe très lisse. Dans ce livre, nous focalisons notre intérêt sur la régression non paramétrique. Différents modèles non paramétriques ont été introduits , tels que le modèle de poisson non paramétrique pour modéliser l'octroi de crédits et le modèle GARCH non paramétrique pour modéliser la variabilité de Bitcoin.

Objective: The purpose of this paper is to demonstrate the effectiveness of the nonparametric GARCH model for the prediction of future Bitcoin prices. Methodology: The use of parametric GARCH models to characterize the volatility of Bitcoin returns is widely used in the empirical literature. Alternatively, we consider a non-parametric approach to model and forecast the volatility of Bitcoin returns. Results: We show that the volatility forecast of the nonparametric GARCH model yields superior performance compared to an extended class of parametric GARCH models. Originality / relevance: The improved accuracy of forecasting the volatility of Bitcoin returns based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to commonly used GARCH parametric models..

L'objectif de ce papier est d'étudier la dynamique des volatilités et des corrélations entre le rendements du marché boursier tunisien et les prix des matières premières au cours de la période 2006-2016. Nous avons utilisé le modèle DCC-GARCH pour déterminer la meilleure stratégie de couverture d'un portefeuille contre le risque d'une évolution défavorable des prix sur le marché. Les résultats empiriques montrent que la volatilité du marché tunisien peut être influencée non seulement par ses propres valeurs passées ou par les chocs intérieurs mais aussi par les chocs passés provenant des marchés des matières premières internationaux. En plus nous avons conclue que, le portefeuille sélectionné par un investisseur tunisien doit être composé principalement des actifs boursiers plutôt que des matières premières. Mots clé : volatilité, dynamique des corrélations, DCC-GARCH, GARCH multivarié, stratégie de couverture, poids de portefeuille optimal.

The function of financing is one of the primary functions of each company. It consists of making three fundamental decisions: decisions about investing, financing decision and the decision on the distribution of dividends. Financing of companies differs by maturity, by the origin of the source of funding, according to the owner of the use of financial resources and in accordance with the different accounting treatment of business events according to International Accounting Standards and the Croatian Financial Reporting Standards. This paper presents the specifics of financing sources by short maturities, and the treatment for short term financing through loans, factoring, issuing commercial papers and spontaneous financing. The funds raised by short term financing are commonly used to perform operations or to settle outstanding obligations. Short term financing is used because of the benefits arising from the timing factors. The basic advantages of short term financing are the ease of loan approval, the cost of capital and flexibility.

The function of financing is one of the primary functions of each company. It consists of making three fundamental decisions: decisions about investing, financing decision and the decision on the distribution of dividends. Financing of companies differs by maturity, by the origin of the source of funding, according to the owner of the use of financial resources and in accordance with the different accounting treatment of business events according to International Accounting Standards and the Croatian Financial Reporting Standards. This paper presents the specifics of financing sources by short maturities, and the treatment for short term financing through loans, factoring, issuing commercial papers and spontaneous financing. The funds raised by short term financing are commonly used to perform operations or to settle outstanding obligations. Short term financing is used because of the benefits arising from the timing factors. The basic advantages of short term financing are the ease...

Az esztergáló-, fúró-és marólapkák üzemi teljesítőképessége és gazdasági hatékonysága a forgácsolóképesség fogalmával írható le. Ez a komplex fogalom több fő és kiegészítő jellemzőből tevődik össze, ezért egyetlen mérőszámmal nem lehet leírni. A cikk az esztergalapkák teljesítőképességének mérésére és összehasonlítására szolgáló olyan új módszert ismertet, amely egyetlen mérőszámmal jellemzi a négy legfontosabb kiegészítő jellemzőt, és lehetővé teszi a szerszám optimális felhasználásának körülményeinek kiválasztását. Az eljárás rövid ideig tartó műszeres vizsgálatokra épül, így nem igényel tetemes anyag-és költségráfordítást. A másik mutató a komplex hatékonyság, amely egyetlen számadattal fejezi ki a lapkák sokoldalú minősítését. A felhasználhatóság eltérő mértékét súlyszámokkal adjuk meg, így definiáljuk a korrigált komplex hatékonyság fogalmát. Ennek segítségével össze lehet hasonlítani a különböző élkialakítású szerszámokat, sőt az innovációs lapkafejlesztés eredményességét is. A kidolgozott eljárást már több vezető gyártó cég különböző rendeltetésű, kialakítású és anyagú szerszámánál alkalmaztuk eredményesen.

This pedagogical paper should help enrich the derivatives course that are delivered to both graduate and undergraduate students. We provide pricing formulae and payoff diagrams for some of the more popular second generation or exotic options. In spile of their growing popularity. cu rrent textbooks usually provide only cursory definitions. often without supporting mathematical equations, payoff diagrams and, most imponantiy. examples of their managerial application. This paper presents valuation equations. payoff diagrams and managerial applications for binary. lookback. choo~er, compound, Bermuda. ASian, barrier and forward stan options.

The concept of univariate Range Value-at-Risk, presented by , is extended in the multidimensional setting. Traditional risk measures are not well suited when dealing with heavy-tail distributions and infinite tail expectations. The multivariate definitions of robust truncated tail expectations are provided to overcome this problem. Robustness and other properties as well as empirical estimators are derived. Closed-form expressions and special cases in the extreme value framework are also discussed. Numerical and graphical examples are provided to examine the accuracy of the empirical estimators.

The concept of univariate Range Value-at-Risk, presented by Cont et al. (2010), is extended in the multidimensional setting. Traditional risk measures are not well suited when dealing with heavy-tail distributions and infinite tail expectations. The multivariate definitions of robust truncated tail expectations are provided to overcome this problem. Robustness and other properties as well as empirical estimators are derived. Closed-form expressions and special cases in the extreme value framework are also discussed. Numerical and graphical examples are provided to examine the accuracy of the empirical estimators.

This paper examines the effectiveness of using futures as hedging instruments using alternative models for measuring the hedging effectiveness of Indices. For this purpose, daily data of futures and the spot prices of two major indices, NIFTY 50 and NIFTY NEXT 50 were used to analyze hedging effectiveness. The data used for analysis was 6 years spot prices and futures with near month, next to near month, and far month contracts. For the estimation of static hedge ratios, the researchers have used OLS, MVHR, ECM, and VECM. The result indicates that VECM performs well than OLS, MVHR, and VAR model, meaning that VECM gives the more stable hedging effectiveness so that both the indices will be the benchmark for risk managers in reducing the risk.

The paper elaborates on the relationship between ICT utilisation and enterprise performance using unique data collected within the survey of 1000 Polish companies in the first half of 2015. The analysis refers to the conceptual model derived from the paper of Milgrom and Roberts (1990) that focuses on the role of complementary (organisational) changes in efficient and productive implementation of Information and Communication Technologies in enterprises. The study shows that companies which invest in ICT and widely utilise these technologies achieve better results (in terms of efficiency, productivity, market image). However, it appears that the holistic approach to ICT utilisation in Polish companies is not very popular-only 1/3 of surveyed enterprises combined ICT implementation with organisational change. It implies that there is still large unexploited potential as for productivity enhancement, especially in micro and small companies.

Au terme de cette étape de mon travail, je voudrais remercier toutes les personnes qui m'ont permis de le mener à bien. Je souhaite d'abord exprimer ma profonde gratitude à mon directeur de thèse, le Professeur Paul Deheuvels pour son soutien constant tout au long de la préparation de ce travail. Je veux lui témoigner toute mon estime parce qu'il sait créer une ambiance propice à la recherche scientifique : ses encouragements aux initiatives personnelles, ses orientations, ses conseils, son respect profond d'une grande liberté de pensée et d'action. Enfin je tiens à le remercier pour m'avoir inculqué pendant ces années certaines valeurs comme la rigueur intellectuelle, le souci du détail ou le respect du travail d'autrui. Qu'il trouve ici l'expression de ma reconnaissance. Je remercie également les Professeurs Jean-David Fermanian et Bruno Rémillard d'avoir accepté le rôle de rapporteurs de cette thèse malgré un emploi du temps que je devine chargé. Je leur suis très reconnaissant du temps qu'ils ont consacré à l'expertise de ce travail. Je suis honoré que les professeurs Gérard Biau, Michel Broniatowski et Arthur Charpentier, si dynamiques scientifiquement, aient accepté de faire partie du jury. Je leur adresse mes sincères remerciements pour l'intérêt qu'ils ont accordé à mon travail. Je tiens également à exprimer un merci tout spécial envers Salim Bouzebda avec lequel le partage d'idées a été florissant, sans oublier l'aide mutuelle que nous nous sommes accordée au cours de ces dernières années donnant naissance à un article publié et d'autres travaux en cours. Je voudrais remercier très chaleureusement Louise Lamart, Anne Durrande et Pascal Epron qui font preuve chaque jour de leur gentillesse et patience. Je voudrais adresser un salut amical aux membres du laboratoire et d'ailleurs : aux professeurs

This study investigates the determinants of trading activity in the U.S. corporate bond market, focusing on the effects of Seasonal Affective Disorder (SAD) and macroeconomic announcements. Employing the General-to-Specific (Gets) Autometrics methodology, we identify distinct behavioral responses between retail and institutional investors to SAD, noting a significant impact on retail trading volumes but not on institutional trading or bond returns. This discovery extends the understanding of behavioral finance within the context of bond markets, diverging from established findings in equity and Treasury markets. Additionally, our analysis delineates the influence of macroeconomic announcements on trading activities, offering new insights into the market's reaction to economic news. This study's findings contribute to the broader literature on market microstructure and behavioral finance, providing empirical evidence on the interplay between psychological factors and macroeconomic information flow within corporate bond markets. By addressing these specific aspects with rigorous econometric techniques, our research enhances the comprehension of trading dynamics in less transparent markets, offering valuable perspectives for academics, investors, risk managers, and policymakers.

The hypothesis that futures rates are unbiased and efficient predictors of future spot interest rates has been one of the most controversial topics in the empirical literature on market efficiency. The first part of this article concentrates on the question of whether the hypothesis that rates are unbiased holds for the 3-month Euribor futures market. The empirical analysis differs from usually applied tests in its employment of a panel estimation approach, which enables the use of all daily futures rates from December 1998 to December The second part of this article analyzes the impact of ECB monetary policy news on the volatility and the prediction error of the Euribor futures rates. As interest rate futures are regarded as the market's expectations of future interest rates, the day-to-day volatility of the Euribor futures rates can be used to draw conclusions as to whether ECB monetary policy decisions are fully anticipated by market participants. The analysis of the change of the prediction error between two days serves the detection whether the ECB monetary announcements improves or worsens in general the interest rate forecast.