Essays on modeling, hedging and pricing of insurance and financial products

Cette thèse est composée de trois articles abordant différentes problématiques en relation avec la modélisation, la couverture et la tarification des risques d’assurance et financiers. “A general class of distortion operators for pricing contingent claims with applications to CAT bonds” est un projet présentant une méthode générale pour dériver des opérateurs de distorsion compatibles avec la valorisation sans arbitrage. Ce travail offre également une nouvelle classe simple d’opérateurs de distorsion afin d’expliquer les primes observées dans le marché des obligations catastrophes. “Local hedging of variable annuities in the presence of basis risk” est un travail dans lequel une méthode de couverture des rentes variables en présence de risque de base est développée. La méthode de couverture proposée bénéficie d’une exposition plus élevée au risque de marché et d’une diversification temporelle du risque pour obtenir un rendement excédentaire et faciliter l’accumulation de capital. “Option pricing under regime-switching models : Novel approaches removing path-dependence” est un projet dans lequel diverses mesures neutres au risque sont construites pour les modèles à changement de régime de manière à générer des processus de prix d’option qui ne présentent pas de dépendance au chemin, en plus de satisfaire d’autres propriétés jugées intuitives et souhaitables.This thesis is composed of three papers addressing different issues in relation to the modeling, hedging and pricing of insurance and financial risks. “A general class of distortion operators for pricing contingent claims with applications to CAT bonds” is a project presenting a general method for deriving probability distortion operators consistent with arbitrage-free pricing. This work also offers a simple novel class of distortions operators for explaining catastrophe (CAT) bond spreads. “Local hedging of variable annuities in the presence of basis risk” is a work in which a method to hedge variable annuities in the presence of basis risk is developed. The proposed hedging scheme benefits from a higher exposure to equity risk and from time diversification of risk to earn excess return and facilitate the accumulation of capital. “Option pricing under regime-switching models: Novel approaches removing path-dependence” is a project in which various risk-neutral measures for hidden regime-switching models are constructed in such a way that they generate option price processes which do not exhibit path-dependence in addition to satisfy other properties deemed intuitive and desirable

Dynamic hedging using the realized minimum-variance hedge ratio approach - examination of the CSI 300 index futures

Includes bibliographical references (pages 26-29).Published as: Pacific-Basin Finance Journal, vol. 57, October 2019, 101048, https://doi.org/10.1016/j.pacfin.2018.08.002.This paper investigates the dynamic hedging performance of the high frequency data based realized minimum-variance hedge ratio (RMVHR) approach. We comprehensively examine a number of popular time-series models to forecast the RMVHR for the CSI 300 index futures, and evaluate the out-of-sample dynamic hedging performance in comparison to the conventional hedging models using daily prices, as well as the vector heterogeneous autoregressive model using intraday prices. Our results show that the dynamic hedging performance of the RMVHR-based methods significantly dominates that of the conventional methods in terms of both hedging effectiveness and tracking error volatility in the out-of-sample forecast period. Furthermore, the superiority of the RMVHR-based methods is robust in different market structures and different volatility regimes, including China's abnormal market fluctuations in 2015 and the US financial crisis in 2008

The mechanics and regulation of variable payout annuities

This paper discusses the mechanics and regulation of participating and unit-linked variable payout annuities. These annuities offer benefits that are not fixed in either nominal or real terms but depend on the performance of the fund or funds in which the underlying reserve assets are invested, their profit sharing features, and the treatment of longevity risk. The paper focuses on the treatment of investment and longevity risks by different types of these annuities and underscores the challenge of establishing a robust and effective framework of regulation and supervision for these products. The paper also addresses the exposure of annuitants to integrity risk and places special emphasis on the need for a high level of meaningful transparency.Debt Markets,Insurance&Risk Mitigation,Investment and Investment Climate,Pensions&Retirement Systems,Non Bank Financial Institutions

Volatility persistence in cryptocurrency markets under structural breaks.

This paper deals with the analysis of volatility persistence in 12 main cryptocurrencies (Bitcoin, Bitshare, Bytecoin, Dash, Ether, Litecoin, Monero, Nem, Ripple, Siacoin, Stellar and Tether) taking into account the possibility of structural breaks. Using fractional integration methods, the results indicate that both absolute and squared returns display long memory features, with orders of integration confirming the long memory hypothesis. However, after accounting for structural breaks, we find a reduction in the degree of persistence in the cryptocurrency market. The evidence of persistence in volatility imply that market participants who want to make gains across trading scales need to factor the persistence properties of cryptocurrencies in their valuation and forecasting models since that will help improve long-term volatility market forecasts and optimal hedging decisions.pre-print532 K

Bet-hedging strategies in expanding populations

In ecology, species can mitigate their extinction risks in uncertain environments by diversifying individual phenotypes. This observation is quantified by the theory of bet-hedging, which provides a reason for the degree of phenotypic diversity observed even in clonal populations. Bet-hedging in well-mixed populations is rather well understood. However, many species underwent range expansions during their evolutionary history, and the importance of phenotypic diversity in such scenarios still needs to be understood. In this paper, we develop a theory of bet-hedging for populations colonizing new, unknown environments that fluctuate either in space or time. In this case, we find that bet-hedging is a more favorable strategy than in well-mixed populations. For slow rates of variation, temporal and spatial fluctuations lead to different outcomes. In spatially fluctuating environments, bet-hedging is favored compared to temporally fluctuating environments. In the limit of frequent environmental variation, no opportunity for bet-hedging exists, regardless of the nature of the environmental fluctuations. For the same model, bet-hedging is never an advantageous strategy in the well-mixed case, supporting the view that range expansions strongly promote diversification. These conclusions are robust against stochasticity induced by finite population sizes. Our findings shed light on the importance of phenotypic heterogeneity in range expansions, paving the way to novel approaches to understand how biodiversity emerges and is maintained.This study has been partially financed by the Consejería de Conocimiento, Investigación y Universidad, Junta de Andalucía and European Regional Development Fund (ERDF), ref. SOMM17/6105/UGR (to MAM)

Analysis of the fundamental predictability of prices in the British balancing market

This research analyses the non-linear and complex effects of drivers of system imbalance prices in the GB electricity market. Unlike day-ahead prices, the balancing settlement prices are comparatively under-researched, yet their importance is growing with greater market risks. The fundamental drivers of these prices are analysed over 2016-2019. The result of a nonlinear modelling approach reveals that system imbalance price exhibits a regime-switching behaviour, driven by weather and demand forecast errors, as well as other system effects. Surprisingly, balancing prices are predictable out of sample and a regime switching specification is more accurate than a linear model for prediction

Four essays on quantitative economics applications to volatility analysis in Emerging Markets and renewable energy projects

[ES]Las decisiones financieras se pueden dividir en decisiones de inversión y decisiones de financiación. En lo que respecta a las decisiones de inversión, la incertidumbre acerca de la dinámica futura de las variables económicas y de las financieras tiene un rol fundamental. Eso, se explica porque los retornos esperados por las empresas y por los inversionistas se pueden ver afectados por los movimientos adversos en los mercados financieros y por los altos niveles de volatilidad. Como consecuencia, resulta crucial realizar un adecuado análisis y modelación de la volatilidad para el proceso de toma de decisiones financieras, por parte de las empresas y el diseño de estrategias de inversión y cobertura por parte de los inversionistas. En este sentido, el estudio de la volatilidad se ha convertido en uno de los temas más interesantes de la investigación en finanzas. Lo anterior ha cobrado mayor relevancia en los últimos años, teniendo en cuenta el escenario de alta volatilidad e incertidumbre que afrontan los mercados a nivel global. Este documento tiene como objetivo abordar cuatro cuestiones centrales, las cuales están relacionadas con la volatilidad financiera como campo de investigación. Esas cuestiones son, la transmisión y spillovers de volatilidad en mercados emergentes, la calibración de la superficie de volatilidad para proyectos de energía renovable y el pronóstico de los rendimientos de activos energéticos y spillovers de volatilidad a través de técnicas de machine learning. En el primer capítulo del documento, se examinan los efectos de transmisión de volatilidad entre un índice de energía y un índice financiero para los Mercados Emergentes. En consecuencia, mediante el uso de un modelo DCC, se muestra que los efectos de transmisión de volatilidad entre los índices empleados para la crisis subprime y la crisis del COVID-19 fueron diferentes. Lo anteriormente dicho, considerando que la primera crisis se originó en el sector financiero y luego se extendió al resto de la economía, mientras que la segunda se originó en el sector real y posteriormente afectó al resto de la economía. Teniendo en cuenta que la relación entre la volatilidad de los mercados es cambiante en el tiempo, en el segundo capítulo se llevó a cabo un análisis dinámico de los spillovers de volatilidad entre materias primas, Bitcoin y un índice de Mercados Emergentes. Así, empleando la metodología propuesta por Diebold y Yilmaz (2012), se concluyó que los efectos de los spillovers de volatilidad entre los activos analizados no son constantes en dirección e intensidad a través del tiempo. En particular, para períodos de crisis como el de la pandemia del COVID-19, hay reversiones en la dirección de los spillovers de volatilidad debido al sector en el que se originó la crisis. Además, en este capítulo se explota la naturaleza dinámica de los spillovers de volatilidad. Por lo tanto, se planteó que el índice de spillovers de volatilidad propuesto por Diebold y Yilmaz puede ser usado como una medida para pronosticar periodos de alta turbulencia. Lo anterior se desarrolló a través de modelos econométricos tradicionales y de técnicas de machine learning. En el tercer capítulo del documento, se propone un modelo que predice los retornos de los precios del carbono y del petróleo. En este sentido, se desarrolló un modelo híbrido, el cual combina las proyecciones obtenidas a partir de diferentes técnicas de machine learning y modelos econométricos tradicionales, obteniéndose resultados los cuales muestran las ventajas de emplear modelos híbridos que incorporan técnicas de machine learning, exclusivamente, para pronosticar variables financieras. Finalmente, en el capítulo cuatro, se presenta una metodología para la estimación de la volatilidad en la valoración de proyectos de energías renovables mediante opciones reales. En esta metodología, la cual es una extensión del enfoque de volatilidad implícita empleada para las opciones financieras, la volatilidad de un proyecto es la volatilidad implícita obtenida a partir de la superficie de la volatilidad de empresas comparables, según una determinada fecha de valoración y dada la relación deuda-capital de un proyecto de energía renovable. En este análisis, se utilizó el modelo estocástico 'alfa-beta-rho' para calibrar la superficie de la volatilidad para la valoración mediante opciones reales. Por último, al final del documento se presentan las conclusiones derivadas de los capítulos mencionados, así como algunas recomendaciones para las futuras investigaciones. [EN]Financial decisions can be divided in investment and financing decisions. Concerning investment decisions, the uncertainty about the future dynamics of financial and economic variables has a central role, considering that the returns expected by firms and investors can be affected by the adverse movements in financial markets and their high volatility. In consequence, the adequate volatility analysis and modeling is crucial for the firm’s financial decision-making process and the design of investing and hedging strategies by investors. In this regard, the study of volatility has become one of the most interesting topics in finance research. The foregoing has become more relevant in recent years considering the scenario of high volatility and uncertainty faced by markets globally. This document aims to address four central issues related to financial volatility as a research area. These are, volatility transmission and spillovers in Emerging Markets, the calibration of the volatility surface for renewable energy projects and the forecast of energy assets returns and volatility spillovers through machine learning techniques. In the first chapter of the document, the volatility transmission effects between an energy index and a financial index for Emerging Markets are examined. Then, by using a DCC model, it is shown that the volatility transmission effects between the employed indices for the subprime crisis and the COVID-19 pandemic were different. This, considering that the former crisis originated in the financial sector and spread to the rest of the economy, while the second originated in the real sector and trasmitted to the rest of the economy posteriorly. Considering that the relationship between markets volatility is time-varying, in the second chapter, a dynamic analysis of volatility spillovers between commodities, Bitcoin and an Emerging Markets index is developed. Employing the methodology proposed by Diebold and Yilmaz (2012), it is concluded that the volatility spillovers effects between the analyzed assets is not constant in direction and intensity over time. In particular, for periods of crisis such as the COVID-19 pandemics, there are reversals in the direction of volatility spillovers due to the sector in which the crises originate. In addition, in this chapter the dynamic nature of volatility spillovers is exploited. Hence, the volatility spillover index proposed by Diebold and Yilmaz is forecasted to be used as a measure to anticipate high turbulence periods. This, through both traditional econometric models and machine learning techniques. In the third chapter, a model for the prediction of carbon and oil prices is proposed. In this sense, a hybrid model that ensembles the forecasts obtained from different machine learning techniques and traditional econometric models is developed, obtaining results that show the advantages of employing hybrid models which combine machine learning techniques, exclusively, to forecast financial variables. In Chapter four, a methodology for the estimation of volatility in renewable energy projects valuation through real options is presented. In this methodology, which is an extension of the implied volatility approach employed for financial options, the volatility of the project is the implied volatility obtained from the volatility surface of comparable firms for a certain valuation date and given debt-to-equity relation of a renewable energy project. In this analysis, the stochastic ‘alpha-beta-rho’ model is utilized to calibrate the volatility surface for real option valuation purposes. Finally, the conclusions derived from the mentioned chapters are presented at the end of the document as well as some recommendations for future research

Modelling the Dynamics of Credit Spreads of European Corporate Bond Indices

Credit spreads are important financial tools, since they are used as indicators of economic progression, investment decisions, trading and hedging, as well as pricing credit derivatives. Their role has become more significant for the European fixed income markets since the introduction of the Euro, which reshaped the mechanics of the financial environment. The introduction of single currency provided the means for a pan-European economic growth and cross-border development, liberalized a vast inflow of capital which was once fragmented into different currencies, and provided the dynamics of cross-border investments around a unified legislative framework. Thus, the main subject of the thesis is to provide further insight into and investigate the nature and the dynamics of credit spreads of European corporate bond indices during the credit crisis period. Traditional quantitative credit risk models assume that changes in spreads are normally distributed but empirical evidence shows that they are likely to be skewed and fat-tailed, and if they are ignored then the calculation of loss probabilities will be seriously compromised. Therefore, the first area of investigation aims to provide further insight into the dynamics of higher moments and regime shifts in credit spread changes by applying a GARCH-type model that allows for time-varying volatility, skewness and kurtosis, as well as a Markov regime-switching GARCH specification to capture the structural changes in the volatility of credit spreads. Furthermore, a comparison of the different specifications is undertaken in order to assess which model better fits the empirical distribution of the data and produces best Value-at-Risk estimates. The results presented have significant implications for risk management, as well as in the pricing of credit derivatives. The second area of investigation is to assess and evaluate time-varying correlation of credit spreads. Different multivariate GARCH models, such as Orthogonal-GARCH, the Constant and Dynamic Correlation GARCH models, Risk Metrics and Diagonal-BEKK, are applied to examine the behaviour and dynamics of time-varying correlation. Additionally, the performance of the proposed models is examined by determining whether they produce accurate VaR estimates. The study finds evidence in support of time-varying correlation coefficients between credit spreads which appears to be market dependent and has implications for pricing of derivatives, portfolio selection, trading and hedging activities, as well as risk management. Finally, the impact of economic determinants of credit spreads such as the risk-free rate, inflation, as well as equity and commodity indices and volatilities, are investigated over different market conditions using regime switching models. The results highlight how the effect of the determinants on credit spreads varies across different market conditions and point to the existence of non-linear relationship between the determinants and credit spread changes. The study reveals that the regime dependent determinants have significant explanatory power only in the high volatility regime. Finally, it is shown that the feed-forward neural network model out-performs the other specifications applied in this study in terms of estimating out-of-sample mean forecasts