9,132 research outputs found

    Designing capital-ratio triggers for Contingent Convertibles

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    Contingent Convertible (CoCo) bonds represent a novel category of debt financial instruments, recently introduced into the financial landscape. Their primary role is to bolster financial stability by maintaining healthy capital levels for the issuing entity. This is achieved by converting the bond principal into equity or writing it down once the minimum capital ratios are violated. CoCos aim to recapitalize the bank before it is on the brink of collapse, to avoid a state bailout at a huge cost to the taxpayer. Under normal circumstances, CoCo bonds operate as ordinary coupon-paying bonds, which only in case of insufficient capital ratios are converted into equity of the issuer. However, the CoCo market has struggled to expand over the years, and the recent tumult involving Credit Suisse and its enforced CoCo write-off has underscored these challenges. The focus of this research work is on the first hand to understand the reasons for this failure, and, on the other hand, to modify its underlying design in order to restore its intended purpose: to act as a liquidity buffer, strengthening the capital structure of the issuing firm. The cornerstone of the proposed work is the design of a self-adaptive model for leverage. This model features an automatic conversion that does not hinge on the judgment of regulatory authorities. Notably, it allows the issuer's debt-to-assets ratio to remain within predetermined boundaries, where the likelihood of default on outstanding liabilities remains minimal. The pricing of the proposed instruments is difficult as the conversion is dynamic. We view CoCos essentially as a portfolio of different financial instruments. This treatment makes it easier to analyze their response to different market events that may or may not trigger their conversion to equity. We provide evidence of the model's effectiveness and discuss it implications of its implementation, in light of the regulatory environment and best market practices.Skilyrt breytanleg (e. Contingent Convertible, skammstafað CoCo) skuldabréf eru nýstárleg gerð af fjármálagerningum sem nýlega komu fram á sjónarsvið fjármálamarkaða. Helsta hlutverk þeirra er að e a fjármálastöðugleika með því að viðhalda hæfilegum eiginfjárgrunni fyrir útgefendur þeirra. Þetta er gert með því að umbreyta höfuðstól skuldabréfs í hlutafé eða með því færa þau niður þegar krafa um eiginfjárhlutföll eru rofin. CoCo hefur það markmið að endurfjármagna bankann áður en hann fellur og þar með koma í veg fyrir björgunaraðgerðir af hálfu ríkisins, sem hefur í för með sér mikinn kostnað fyrir skattgreiðendur. Undir venjulegum kringumstæðum virka CoCo skuldabréf eins og hefðbundin arðgreiðslu- skuldabréf, sem einungis er breytt í hlutafé þegar eiginfjárhlutföll útgefanda þeirra eru ekki nægjanleg. Eigi að síður hefur markaður fyrir CoCo átt erfitt uppdráttar í gegnum tíðina og hefur nýlegur titringur í kringum Credit Suisse og þvingaðar afskriftir þeirra á CoCo skuldabréfum ýtt enn frekar undir erfiðleikana. Helsti tilgangur þessarar rannsóknar er tvíþættur. Annars vegar er ætlunin að skilja hvers vegna CoCo hefur ekki átt meiri velgengni að fagna en raun ber vitni. Hins vegar er henni ætlað að breyta grundvallarhönnun CoCo í þeim tilgangi að endurheimta upprunalegan tilgang þeirra: sem er að vera stuðpúði lausafés sem styrkir fjármagnsskipan útgáfu fyrirtækisins. Hornsteinn verkefnisins er hönnun á líkani með sjálfaðlögunarhæfni með tilliti til skuldsetningarhlutfalls. Líkanið býr yfir sjálfvirkri umbreytingu sem ræðst því ekki af reglum eftirlitsyfirvalda. Það gerir útgefanda því kleift að viðhalda hlutfalli skulda á móti eignum innan fyrirfram skilgreindra marka, þar sem líkur á vanskilum vegna útistandandi skuldbindinga haldast í lágmarki. Verðlagning gerninganna sem lagðir eru til í rannsókninni er þó vandasöm þar sem umbreytingin er dýnamísk. Í meginatriðum verður litið á CoCos sem safn ólíkra fjármálagerninga. Með þessari aðferð er hægt að greina viðbrögð þeirra við mismunandi markaðsatburðum sem geta mögulega hrint af stað umbreytingu yfir í hlutafé. Sýnt verður fram á skilvirkni líkansins ásamt því að álykta um innleiðingu þess með tilliti til regluverks og bestu markaðsvenja.RU Research Fund Icelandic Research Fun

    Essays in empirical asset pricing

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    Essays in empirical asset pricing

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    Assessing the Role and Regulatory Impact of Digital Assets in Decentralizing Finance

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    This project will explore the development of decentralized financial (DeFi) markets since the first introduction of digital assets created through the application of a form of distributed ledger technology (DLT), known as blockchain, in 2008. More specifically, a qualitative inquiry of the role of digital assets in relation to traditional financial markets infrastructure will be conducted in order to answer the following questions: (i) can the digital asset and decentralized financial markets examined in this thesis co-exist with traditional assets and financial markets, and, if so, (ii) are traditional or novel forms of regulation (whether financial or otherwise) needed or desirable for the digital asset and decentralized financial markets examined herein? The aim of this project will be to challenge a preliminary hypothesis that traditional and decentralized finance can be compatible; provided, that governments and other centralized authorities approach market innovations as an opportunity to improve existing monetary infrastructure and delivery of financial services (both in the public and private sector), rather than as an existential threat. Thus, this thesis seeks to establish that, through collaborating with private markets to identify the public good to which DeFi markets contribute, the public sector can foster an appropriate environment which is both promotive and protective of the public interest without unduly stifling innovation and progress

    Essays on Risk Creation in the Banking Sector

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    This thesis consists of four essays exploring risk creation in the banking sector. The essays examine how conflicting interests can compromise the objectivity, judgment, and decision making of economic agents. Consequently, they may prioritize their personal or institutional interests over the best interests of others or the entire financial system. Chapter 2 delves into the conflict of interest that arises when a bank serves as an investor in the stock market. Chapter 3 revisits the discussion of the potential misalignment between sovereign incentives and the collective interests of the currency union, particularly in the bond market. Chapter 4 draws attention to a situation where regulations in the banking sector may be advantageous for a government in the sovereign bond market. Finally, Chapter 5 looks at the flip side of the coin, examining how banks may be susceptible to moral hazard concerns in their FX lending decisions, given that they do not fully bear the consequences of their actions

    Driving venture capital funding efficiencies through data driven models. Why is this important and what are its implications for the startup ecosystem?

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    This thesis aims to test whether data models can fit the venture capital funding process better, and if they do fit, can they help improve the venture capital funding efficiency? Based on the reported results, venture capitalists can only see returns in 20% of their investments. The thesis argues that it is essential to help venture capital investment as it can help drive economic growth through investments in innovation. The thesis considers four startup scenarios and the related investment factors. The scenarios are a funded artificial intelligence startup seeking follow-on funding, a new startup seeking first funding, the survivability of a sustainability-focused startup, and the importance of patents for exit. Patents are a proxy for innovation in this thesis. Through quantitative analysis using generalized linear models, logit regressions, and t-tests, the thesis can establish that data models can identify the relative significance of funding factors. Once the factor significance is established, it can be deployed in a model. Building the machine learning model has been considered outside the scope of this thesis. A mix of academic and real-world research has been used for the data analysis of this thesis. Accelerators and venture capitalists also used some of the results to improve their own processes. Many of the models have shifted from a prediction to factor significance. This thesis implies that it could help venture capitalists plan for a 10% efficiency improvement. From an academic perspective, this study focuses on the entire life of a startup, from the first funding stage to the exit. It also links the startup ecosystem with economic development. Two additional factors from the study are the regional perspective of funding differences between Asia, Europe, and the US and that this study would include the recent economic sentiment. The impact of the funding slowdown has been measured through a focus on first funding and longitudinal validations of the data decision before the slowdown. Based on the results of the thesis, data models are a credible alternative and show significant correlations between returns and factors. It is advisable for a venture capitalist to consider these

    Essays on Asset Pricing Factor Models

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    This dissertation investigates the utilization of factor models to measure performance in corporate bond markets, identifies an optimal factor model for corporate bond returns, and finally provides a comprehensive analysis of factor pricing and market integration across asset classes. Chapter 1 introduces the main concepts and delivers an overview of the following chapters. Chapter 2 seeks to answer the question "which factor model do investors in corporate bonds use?" by tracking investors' decisions to invest in actively managed corporate bond mutual funds with a revealed preference approach. The main result is that all bond factor models are dominated by the simple Sharpe ratio and Morningstar ratings. For all major corporate bond mutual fund styles, the Sharpe ratio explains fund flows better than alphas from bond factor models. Since the Sharpe ratio (and to some extent also Morningstar ratings) can be easily manipulated in bond markets, these findings have potentially severe implications for all market participants. Going a step further, Chapter 3 addresses the following important questions, from both an academic and a practitioner's perspective: What are important drivers of corporate bond returns? What should be a benchmark model for pricing and investing in corporate bond markets? The central finding is that factors related to carry, duration, equity momentum, and the term structure are the most important risk factors in corporate bond markets. From a large set of factor candidates for corporate bond returns, we condense an optimal model with a two-step approach. First, we filter out factors that do not systematically move bond prices. Second, we use a Bayesian model selection approach to determine the optimal, parsimonious model. Many prominent factors do not move prices, or are redundant. We document the new model's good performance compared to that of existing models in time-series and cross-sectional tests and analyze the economic drivers of the factors. While Chapter 2 and Chapter 3 focus on corporate bonds, the study conducted in Chapter 4 extends the understanding to a bigger picture of factor pricing and market integration across asset classes. Factor models specializing in one asset class have limited pricing power for other asset classes. Thus, we reject perfect market integration. However, an optimal integrated factor model across asset classes can effectively characterize the returns of multiple asset classes and provide a useful benchmark for multi-asset, multi-factor investing. The optimal model includes several equity and corporate bond factors, suggesting the presence of multiple systematic return drivers. Despite this, there appears to be some degree of cross-market linkages, as the optimal model does not require factors from all asset classes. Finally, Chapter 5 concludes and outlines possible future directions for research

    Spectrum auctions: designing markets to benefit the public, industry and the economy

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    Access to the radio spectrum is vital for modern digital communication. It is an essential component for smartphone capabilities, the Cloud, the Internet of Things, autonomous vehicles, and multiple other new technologies. Governments use spectrum auctions to decide which companies should use what parts of the radio spectrum. Successful auctions can fuel rapid innovation in products and services, unlock substantial economic benefits, build comparative advantage across all regions, and create billions of dollars of government revenues. Poor auction strategies can leave bandwidth unsold and delay innovation, sell national assets to firms too cheaply, or create uncompetitive markets with high mobile prices and patchy coverage that stifles economic growth. Corporate bidders regularly complain that auctions raise their costs, while government critics argue that insufficient revenues are raised. The cross-national record shows many examples of both highly successful auctions and miserable failures. Drawing on experience from the UK and other countries, senior regulator Geoffrey Myers explains how to optimise the regulatory design of auctions, from initial planning to final implementation. Spectrum Auctions offers unrivalled expertise for regulators and economists engaged in practical auction design or company executives planning bidding strategies. For applied economists, teachers, and advanced students this book provides unrivalled insights in market design and public management. Providing clear analytical frameworks, case studies of auctions, and stage-by-stage advice, it is essential reading for anyone interested in designing public-interested and successful spectrum auctions

    Carbon default swap – disentangling the exposure to carbon risk through CDS

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    Using Credit Default Swap spreads, we construct a forward-looking, market-implied carbon risk factor and show that carbon risk affects firms’ credit spread. The effect is larger for European than North American firms and varies substantially across industries, suggesting the market recognizes where and which sectors are better positioned for a transition to a low-carbon economy. Moreover, lenders demand more credit protection for those borrowers perceived to be more exposed to carbon risk when market-wide concern about climate change risk is elevated. Lenders expect that adjustments in carbon regulations in Europe will cause relatively larger policy-related costs in the near future
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