Dauguma šiuolaikinių finansų valdymo ir investicijų mokslinių darbų akcentuoja finansinės rizikos valdymo svarbą finansinių institucijų veiklai. Augančioje finansų rinkoje aktyviais dalyviais tampa įmonės, kurių ilgalaikei sėkmei įtakos turi finansinių lėšų valdymas. Magistro darbe išanalizuoti ir susisteminti įvairių Lietuvos ir užsienio autorių teoriniai ir praktiniai iždo rizikų valdymo aspektai, sukeliantys riziką veiksniai, rizikos rūšys, iždo rizikų įvertinimo ir valdymo metodai. Parodyta, kad pagrindinis rizikos valdymo tikslas nebūtinai yra jos išvengti, o suprasti kritinius rizikos veiksnius ir profesionaliai juos valdyti. Atlikus analizę AB „Mažeikių nafta“ nustatytos šios iždo rizikos: rinkos (valiutų kurso, palūkanų normos, biržinių prekių kainos kitimo), likvidumo, kredito, operacinė. Panaudojus rizikos vertės VaR@95% metodo skaičiavimus, įvertintas iždo rizikų poveikis bendrovės finansinių išteklių formavimui. Patvirtinta autorės suformuluota mokslinio tyrimo hipotezė, kad iždo rizikų strateginis valdymas įmonėje stabilizuoja įmonės pinigų srautus, sumažina įmonės nuostolius dėl finansų rinkos neigiamų pokyčių, pagerina pelningumo prognozavimą. / Most of today’s finance management and investment scientific papers emphasize the importance of finance risk management for the financial institution activities. The companies the long-term success of which comes from the funds management become the active participants in the growing financial market. The Master’s Thesis analyses and systemizes the theoretical and practical aspects of treasury risk management, factors causing risk, types of risks, methods of treasury risk evaluation and management as described by various Lithuanian and foreign authors. It identifies that the main goal of risk management is not necessarily to prevent the risk but to understand the critical risk factors and manage them in professional way. After the analysis has been made the following treasury risks were identified for AB Mažeikių Nafta: market (currency rate exchange, interest rate, commodity price fluctuation), liquidity, credit, operations. Using risk value VaR@95% method calculations the treasury risk impact to the formation of the company financial resources was evaluated. The scientific research hypothesis of the author stating that treasury risk strategy management in the Company stabilizes the Company’s cash flows, reduces loses resulted from negative changes in the finance market, improves the profitability forecasting was proved to be correct.

The supervising institutions do not give to commercial banks indications what models have to be used for stress testing. This research was done in order to find out which mathematical/statistical models are and can be used in credit risk stress testing. Credit risk is one of the biggest financial risks that every bank faces. Stress testing is a tool of credit risk assessment that helps to estimate the consequences of the events that have really small probability to happen but if they occur, banks can have significant losses. This study determined that the most plausible event is adverse macroeconomic conditions. For this reason, models that include macroeconomic impact were presented. Vector autoregression and vector error correction model were tested using the empirical data received from Swedish central bank, Swedish statistics and Eurostat. For financial stability it is worth using vector autoregression or vector error correction model as they describe the macroeconomic environment in the most suitable way and they are appropriate for shock analysis by showing how the impact of any factor can change the whole system. Structure: introduction, main part (credit risk, methods and empirical analysis), publication, conclusions, references. Thesis consists of: 50 p. text without appendices, 13 pictures, 11 tables, 26 bibliographical entries. Appendices included. / Kredito įstaigų priežiūros institucijos nepateikia komerciniams bankams kokius metodus jie turėtų naudoti testavime nepalankiomis sąlygomis. Tiriamasis darbas buvo atliktas tuo tikslu, kad būtų išsiaiškinta kokie matematiniai ir statistiniai metodai yra ir gali būti naudojami kredito rizikos vertinime testuojant nepalankiomis sąlygomis. Kredito rizika yra viena iš didžiausių finansinių rizikų su kuria bankai susiduria. Testavimas nepalankiomis sąlygomis yra kredito rizikos vertinimo įrankis, padedantis nustatyti įvykių, kurių realizavimosi tikimybės yra mažos, tačiau jiems įvykus, bankai patirtų reikšmingus nuostolius, pasekmes. Šis tyrimas nustatė, jog labiausiai tikėtinas įvykis gali būti ypatingai nepalankios ekonominės sąlygos. Dėl šios priežasties darbe yra pristatyti metodai, kurie įvertina makroekonominių veiksnių įtaką. Vektorinė autoregresija ir vektorinis paklaidų korekcijos modelis buvo patikrinti naudojant Švedijos centrinio banko, Švedijos statistikos departamento ir Eurostat empirinius duomenis. Finansinio stabilumo įvertinimui vertėtų naudoti vektorinį autoregresijos ar vektorinį paklaidų korekcijos modelius, nes šie modeliai geriausiai aprašo ekonominę aplinką bei yra labai tinkami šokų analizei, kadangi įvertina bet kurio veiksnio įtaką visai sistemai. Struktūra: įvadas, pagrindinė dalis (kredito rizika, metodai ir empirinė analizė), publikacija, išvados, literatūros sąrašas. Tiriamasis darbas sudarytas iš: 50 psl. teksto be priedų, 13 paveikslų, 11... [toliau žr. visą tekstą]

信用連結債券的價值主要取決於所連結資產池內的資產違約狀況，使得原始信用風險債券在到期時的本金償付受到其他債券的信用風險影響，因此如何準確且客觀的估計資產池內違約機率便一個很重要的課題，而過去文獻常以給定參數的方式，並且假設資產間的違約狀況彼此獨立下進行評價，對於聯合違約機率的捕捉並不明顯，因此本文延伸Factor Copula模型，建立信用連結債券之評價模型，該模型考慮了資產間的違約相關程度，以期達到符合市場的效果，同時配合統計之因素分析法，試圖找出影響商品價格背後的市場因子。 本研究利用延伸的評價模型以及Copula法，對實際商品做一訂價探討，結果發現，不管是使用樣本內或樣本外的資料去評價時，本研究的評價模型表現都優於Copula法，表示說評價時額外加入市場因子的考慮，對於評價是有正向的幫助；而在因子選取方面，我們選取18項因子後，經由因素分析共可萃取出三大類因素，藉由觀察期望價格與市場報價的均方根誤差，發現國家因素以及產業因素均對於商品價格有所影響，而全球因素對於商品不但沒有顯著影響，同時加入後還會使得計算出的商品期望價格更偏離市場報價，代表說並不是盲目的加入許多因子就能使得模型計算出的價格貼近市場報價，則是要視加入的因子對於資產的影響程度而定。 對於後續研究的建議：由於本研究的實證中存在一些假設，使得評價過程中並不完全符合現實市場現況，若能得到市場上的真實數據，或是改以隨機的方式來計算，相信結果會更貼近市場報價；同時，藉由選取不同的因子來評價，希望能找出國家因素、產業因素以外的其他影響因子，可助於我們更了解此項商品背後的影響因素，使得投資人能藉由觀察市場因子數據來判斷商品未來價格走勢。 / Value of the credit-linked notes depend on the pool of assets whether default or not, so the promised payoff of credit-linked notes is affected by other risky underlying assets. Therefore, how to estimate the probability of default asset pool accurately and objectively will be a very important issue. In the past literature, researchers usually use given parameters, and assume assets probability of default are independent from each other under valuation. Furthermore, it is not obvious to capture the joint probability of default. Thus, this article extends the Factor Copula Model to provide a new methodology of pricing credit-linked notes, which consider the default correlation between the extent of assets in order to achieve result in line with market and with Factor Analysis method added, trying to figure out the impact of commodity price factor behind the market. In the empirical analysis, pricing the actual commodity issued by LB Baden-Wuerttemberg using extend model and Copula model, we found that no matter choose in-the-sample or out-the-sample data to valuation, the models in this article are superior to Copula model by compare the root-mean-square deviation(RMSE). It means add the market factors into our valuation is beneficial. In terms of selection factors, we select eighteen factors prepared by Morgan Stanley Capital International, and three categories of factors may be extracted from Factor Analysis method. By observing RMSE, both national factors and industry factors will influence on the commodity, but world factors not only did not significantly impact on the commodity, but also add it to calculate the expected price further from the market price. Representative said not blind join the many factors can make the model to calculate the price close to the market price, it is a factor depending on the degree of influence of the added asset. For the suggestion of future research. The fact that the presence of empirical assumptions in this study, result in the evaluation process is not entirely realistic to market situation. We suggest to get the real data on the market or use random way to calculate, we believe that the outcome will be closer to the market price. Meanwhile, by selecting different factors to evaluate, trying to discover further factors which significantly impact on the commodity; it will help us better to understand the factors behind the commodity, so investors can predict commodity future prices by observing the market data.

This doctoral thesis consists of 4 self-contained chapters: Sovereign Credit Default Swap Premia. This comprehensive review of the literature on sovereign CDS spreads highlights current academic debates and contrasts them with contradictory statements from the popular press.  Real Economic Shocks and Sovereign Credit Risk. New empirical evidence highlights that global macroeconomic risk unspanned by global financial risk bears some responsibility for the strong co-movement in sovereign spreads. A model with only two global macroeconomic state variables rationalizes the existence of time-varying risk premia as a compensation for exposure to common U.S. business cycle risk. The Term Structure of CDS Spreads and Sovereign Credit Risk. The term structure of CDS spreads is an informative signal about the relative importance of global and country-specific risk factors for the time variation of sovereign credit spreads. An empirically validated model illustrates how local risk matters relatively more when the slope is negative, while systematic risk bears more responsibility when the slope is positive. Squeezed Everywhere - Disentangling Types of Liquidity and Testing Limits-to-Arbitrage. The CDS-Bond basis is used as a laboratory to disentangle different types of liquidity and to test limits-of-arbitrage. While asset-specific liquidity is cross-correlated in both the cash and derivative market, funding and market liquidity matter only for the former. The tests find strong evidence in favor of margin-based asset pricing and flight-to-quality effects. / <p>Diss. Stockholm : Handelshögskolan, 2013. Sammanfattning jämte 4 uppsatser</p>

Šis magistrinis darbas aprašo plačiausiai naudojamus dirbtinio intelekto metodus ir galimybes juos taikyti kredito rizikos, kuri yra viena svarbiausių sričių bankininkystėje ir finansuose, vertinime. Pagrindinė problema yra rizikos, atsirandančios kreditoriui išduodant kreditą tam tikram individui ar bendrovei, vertinimas, naudojant įvairius matematinius, statistinius ar kitus metodus. Ši rizika atsiranda tada, kai skolininkas negali laiku grąžinti skolos kreditoriui, kas reiškia papildomus nuostolius. Ji gali pasireikšti, priklausomai nuo skolininko tipo (individas, bendrovė ar užsienio vyriausybė) bei finansinio instrumento tipo ar su juo atliekamo veiksmo (skolos teikimas, finansinių derivatyvų tranzakcijos ir kt.), todėl finansinės institucijos jos įvertinimui bei valdymui naudoja įvairius metodus nuo vertinimo balais bei skirtingų faktorių, tokių kaip valdymo bei veiklos strategijos bei politika, įvertinimo iki klasifikavimo pagal įvairius kriterijus, naudojant modernius ir sudėtingus metodus, tiek matematinius, tiek dirbtinio intelekto. Ši sritis plačiai tiriama ir daug naujų metodų bei sprendimų pastoviai randama. Šio darbo tyrimas sukoncentruotas į atraminių vektorių mašinų (angl.Support Vector Machines, sutr. SVM) metodų, kuris yra viena populiariausių dirbtinio intelekto bei mašininio mokymo metodų ir kurio efektyvumas daugeliu atveju įrodytas. Šiuo tyrimo tikslas yra ištirti galimybes pritaikyti SVM metodą čia aprašomai problemai bei realizuoti sistemą, naudojančią... [toliau žr. visą tekstą] / This master work describes the most widely used artificial intelligence methods and the possibilities to apply them in credit risk evaluation which is one of the most important fields in banking and in finance. The main problem here is to evaluate the risk arising when a creditor gives a credit to a particular individual or an enterprise, using various mathematical, statistical or other methods and techniques. This risk arises when the debtor isn’t able to pay for the loan to the creditor in time which means additional loss. It can appear in many forms depending on the type of debtor (individ-ual, enterprise, government of an abroad country) and type of financial instrument or action that is done with it (giving of a loan, transactions of financial derivatives, etc.), this is the reason why fi-nancial institutions and for it’s evaluation and management use various different methodologies which comprise a lot of methods and techniques from credit scoring (evaluating by a particular formula, usually linear) and evaluating different factors, like management and business strategies or policies, to classification by various criterions by using modern and sophisticated methods, either algebraic, either artificial intelligence and machine learning. This field is widely researched and many new techniques are being found. The research here is concentrated mainly on Support Vector Machines (abbr. SVM) which is one of the most popular artificial intelligence and machine learning... [to full text]

本文針對金融海嘯時期,信用風險大幅擴張的階段,藉由大股東持股比例、 銀行持股比例、政府持股比例分析其對於各家大型金融機構的信用風險之影響, 本文主要採用 Bloomberg 資料庫以及 Bankscope 資料庫,期間涵蓋 2003 年至 2013 年全球資本排名前 60 的大型金融機構的季資料,並透過縱橫資料的技術來分析 股權結構與信用風險之間的相關性。 結論顯示在危機發生的期間時,政府對於大型金融機構的信用風險存在顯著 的抑制效果,本文接著依照過去文獻對投資人的股權保護程度加以分類後,發現 與過去分析總風險和股權結構之間的關係之結果並不相同,以往的結果顯示,保 護程度比較差的國家中,政府對於公司的風險以及公司決策會存在比較大的影響 力,並會進而降低公司的風險,然而本文得到的結果是投資人股權保護程度比較 強的區域裡面,政府對於降低大型金融機構的信用風險會有著比較顯著的影響 性。 / In this thesis, we investigate the level of credit risk taking concerning the ownership structure in large complex financial institutions during the sub-prime crisis period. We use quarterly data of the top 60 large financial institutions based on Bloomberg and Bankscope, covering the interval from 2003 to 2013. From our results, we show that there is negative relationship between the level of risk and government ownership on banks. Furthermore, if we categorize the banks regarding its investor protection, our findings support that government ownership on banks has a significant effect in countries where it provides stronger protection to investors during financial crisis. Different from previous works, which states that in country with less investor protection, government ownership on banks has more influence power, here we arrive at an opposite result. We conclude that increasing government ownership can significantly reduce the level of credit risk on banks.

Dopo una delle peggiori crisi economica e finanziaria mondiale , gli studi sulla previsione delle insolvenze sono diventato uno degli argomenti più dibattuti tra gli studiosi e ricercatori. Al fine di soddisfare le esigenze sia di valutazione interna sia degli investitori professionali , lo studio riscopre il modello "Z - score" di Altman nella sua forma originale , nota per la sua semplicità. Il modello, ancora largamente utilizzato nei mercati statunitensi, è per sua natura poco utilizzato nell’analisi di società europee. La tesi analizza e descrive le caratteristiche dello Z -score, valutandone i risultati come strumento per la previsione di insolvenza nel mercato europeo. Lo studio è condotto su 568 società , prese dagli indici azionari di 7 mercati europei , tra il 2000 e il 2010 . I risultati del test evidenziano una grande variabilità di risultato tra i diversi settori industriali. Il modello risulta semplice ed efficace, ma sostanzialmente incapace di prevedere il rischio di default in Europa, se utilizzato nella sua forma originale . La seconda parte della ricerca studia pertanto come i risultati del modello possano essere valutati da una nuova prospettiva per i mercati europei, concentrandosi su singoli settori industriali. Lo Z score viene testato su un campione di imprese in buona salute ed un altro di aziende insolventi, per 3 gruppi industriali diversi. La ricerca cerca anche di valutare elementi qualitativi accanto a quelli quantitativi, al fine di analizzare in maniera completa il rischio di insolvenza. / After one of the worst world economic and financial crisis, the insolvency prediction has become one of the most debatable topics among scholars. In order to satisfy both the professional investors’ needs and the internal evaluation process, the Thesis rediscovers the original Altman “Z-score” model, known for its convenience. This model is still largely used in the US equity markets but, also for its origin, has hardly been applied to the European equity index. The Thesis investigates and describes the operating characteristics of Altman’s Z-score, evaluating its performance as a tool for insolvency prediction in today's European market. The base model capability is tested examining 568 companies, listed in the main stock indexes of 7 European markets, between 2000 and 2010. A large variability among different industries arises from the analysis conducted. The Thesis results prove that the model is user-friendly but a substantial inability to predict the risk of default in Europe if used in its original form. The second research question try to analyse how could the model be useful for the European markets, testing the Z score over good heath and insolvent firms from 3 industrial groups. The research studies how the model’s results could be evaluated from a new perspective, focusing on individual industrial sectors results. The research also tries to evaluate qualitative elements alongside the quantitative ones, in order to give a harmonized and comprehensive estimation of the insolvency risk.

Public-Private-Partnership (P3) is gaining momentum as the delivery method for the development of public infrastructure. These projects, however, are exposed to economic risks. If the private parties are not comfortable with the level of the risks, they would not participate in the project and, as a result, the infrastructure will most likely not be realized. As an incentive for participation in the P3 project, private parties are sometimes offered guarantees against unfavorable economic risks. Therefore, the valuation of these guarantees is essential for deciding whether or not to participate in the project. While previous works focused on the valuation of guarantees, the incorporation of credit risk in the value of the P3 projects and the guarantees has been neglected. The effect of credit risk can be taken into account by using the rigorous Credit Value Adjustment method (CVA). CVA is a computationally demanding method that the valuation methods currently in the literature are not capable of handling. This research offers a novel approach for the valuation of guarantees and P3 projects which is computationally superior to the existing methods. Because of this computational efficiency, CVA can be implemented to account for credit risk. For the development of this method, a continuous stochastic differential equation (SDE) is derived from the forecasted curve of an economic risk. Using the SDE, the partial differential equation (PDE) governing the value of the guarantees will be derived. Then, the PDE will be solved using Finite Difference Method (FDM). A new feature for this method is that it obtains exercise strategies for the Australian guarantees. The present work extends the literature by providing a valuation method for the cases that multiple risks affect P3 projects. It also presents an approach for the valuation of the Asian style guarantee, a contract which reimburses the private party based on the average of risk factor. Finally, a hypothetical case study illustrates the implementation of the FDM-based valuation method and CVA to obtain the value of the P3 project and the guarantees adjusted for the counterparty credit risk.

Public-Private-Partnership (P3) is gaining momentum as the delivery method for the development of public infrastructure. These projects, however, are exposed to economic risks. If the private parties are not comfortable with the level of the risks, they would not participate in the project and, as a result, the infrastructure will most likely not be realized. As an incentive for participation in the P3 project, private parties are sometimes offered guarantees against unfavorable economic risks. Therefore, the valuation of these guarantees is essential for deciding whether or not to participate in the project. While previous works focused on the valuation of guarantees, the incorporation of credit risk in the value of the P3 projects and the guarantees has been neglected. The effect of credit risk can be taken into account by using the rigorous Credit Value Adjustment method (CVA). CVA is a computationally demanding method that the valuation methods currently in the literature are not capable of handling. This research offers a novel approach for the valuation of guarantees and P3 projects which is computationally superior to the existing methods. Because of this computational efficiency, CVA can be implemented to account for credit risk. For the development of this method, a continuous stochastic differential equation (SDE) is derived from the forecasted curve of an economic risk. Using the SDE, the partial differential equation (PDE) governing the value of the guarantees will be derived. Then, the PDE will be solved using Finite Difference Method (FDM). A new feature for this method is that it obtains exercise strategies for the Australian guarantees. The present work extends the literature by providing a valuation method for the cases that multiple risks affect P3 projects. It also presents an approach for the valuation of the Asian style guarantee, a contract which reimburses the private party based on the average of risk factor. Finally, a hypothetical case study illustrates the implementation of the FDM-based valuation method and CVA to obtain the value of the P3 project and the guarantees adjusted for the counterparty credit risk.

This work undertakes the first comprehensive theoretical assessment of syndicated loans. It is shown that syndicated and bilateral (single lender) loans should be good substitutes in meeting a borrower's financing requirements, but that syndicated loans are more complex and impose additional risks to the parties in the way they are arranged. The existing explantions of loan syndication - that they are hybrids of private bank loans and public debt instruments, that syndication is a portfolio management tool, and that loans are syndicated where they are too large to be provided bilaterally - are unable to substantially explain both the nature of syndicated loans and practice in the loan markets. A rigorous new explanation is developed, which shows that syndication reduces the rate of lending costs, so that the return to the loan originator is greater, and the borrower's cost of financing is lower, where a loan is syndicated rather than provided bilaterally. This explanation is shown to hold in competitive loan markets and to be consistent with the observation that syndicated loans are generally larger than other loans. Incidental to this new explanation, new expressions of the return to a bank from providing a loan on a bilateral basis and from originating a syndicated loan are also developed. New algorithms are also developed for determining the distribution of the commitments from syndicate participants and thus the originator's final hold, the amount it must lend itself, where the loan is underwritten. This provides, for the first time, a rigorous basis for assessing the expected return, and the risk, for the originator of a given syndicated loan. Finally, empirical testing finds that a bank's observed lending history is significant to its decision to participate in a new syndicated loan but that predictions of participation, which are fundamental inputs into the final hold algorithms, based on this information have relatively little power. It follows that there is competitive advantage to loan originators that have access to other, private information on potential participants' lending intentions.