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Title: Empirical studies in corporate credit modelling : liquidity premia, factor portfolios & model uncertainty
Author: van Loon, Paul Rene Frank
ISNI:       0000 0004 7227 2101
Awarding Body: Heriot-Watt University
Current Institution: Heriot-Watt University
Date of Award: 2017
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Insurers match the cash flows of typically illiquid insurance liabilities, such as in-force annuities, with government and corporate bonds. As they intend to buy corporate bonds and hold them to maturity, they can capture the value attached to liquidity, without running the market liquidity risk that is associated with having to sell bonds in the open market. During the long consultation period dedicated to the mark-to-market valuation of insurance assets and liabilities for the Solvency II regulatory framework, CEIOPS noted the importance of the accurate breakdown of the credit spread into its components, most notably the credit and non-credit (i.e. liquidity) components. In this thesis we review many modelling efforts to isolate the liquidity premium and propose a reduced-form modelling approach that relies on a new, relative liquidity proxy. Challenging the status quo when it comes to active and passive investment strategies, products and funds, Exchange Traded Funds and `smart-beta' products provide investors with straightforward ways to strategically expose a portfolio to risk drivers, raising the bar for traditional investment funds and managers. In this thesis, we investigate how traditional sources of equity outperformance (alpha), such as small caps, low volatility and value, translate to UK corporate bonds. For automated trading strategies in corporate bonds, and those with specific factor exposure requirements in particular, transaction costs, rebalancing and an optimal turnover strategy are crucial; these aspects of building factor portfolios are explored for the UK market. Since the financial crisis, mathematical models used in finance have been subject to a fair amount of criticism. More than ever has this highlighted the need of better risk management of financial models themselves, leading to a surge in `model validation' roles in industry and an increased scrutiny from regulatory bodies. In this thesis we look at stochastic credit models that are commonly used by insurers to project forward credit-risky bond portfolios and the model uncertainty and parameter risk that arises as a result of relying on published credit migration matrices. Specifically, our investigation focuses on two violations of the Markovian process that credit transitions are assumed to follow and statistical uncertainty of the migration matrix.
Supervisor: Cairns, Andrew Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available