Switching Diffusions: Applications To Ecological Models, And Numerical Methods For Games In Insurance

Recently, a class of dynamic systems called ``hybrid systems containing both continuous dynamics and discrete events has been adapted to treat a wide variety of situations arising in many real-world situations. Motivated by such development, this dissertation is devoted to the study of dynamical systems involving a Markov chain as the randomly switching process. The systems studied include hybrid competitive Lotka-Volterra ecosystems and non-zero-sum stochastic differential games between two insurance companies with regime-switching. The first part is concerned with competitive Lotka-Volterra model with Markov switching. A novelty of the contribution is that the Markov chain has a countable state space. Our main objective is to reduce the computational complexity by using the two-time-scale formulation. Because the existence and uniqueness as well as continuity of solutions for Lotka-Volterra ecosystems with Markovian switching in which the switching takes place in a countable set are not available, such properties are studied first. The two-time scale feature is highlighted by introducing a small parameter into the generator of the Markov chain. When the small parameter goes to 0, there is a limit system or reduced system. It is established in this work that if the reduced system possesses certain properties such as permanence and extinction, etc., then the complex original system also has the same properties when the parameter is sufficiently small. These results are obtained by using the perturbed Lyapunov function methods. The second part develops an approximation procedure for a class of non-zero-sum stochastic differential games for investment and reinsurance between two insurance companies. Both proportional reinsurance and excess-of-loss reinsurance policies are considered. We develop numerical algorithms to obtain the approximation to the Nash equilibrium by adopting the Markov chain approximation methodology. We establish the convergence of the approximation sequences and the approximation to the value functions. Numerical examples are presented to illustrate the applicability of the algorithms

Numerical methods for problems arising in risk management and insurance

In this dissertation we investigate numerical methods for problems annuity purchasing and dividend optimization arising in risk management and insurance. We consider the models with Markov regime-switching process. The regime-switching model contains both continuous and discrete components in their evolution and is referred to as a hybrid system. The discrete events are used to model the random factors that cannot formulated by differential equations. The switching process between regimes is modulated as a finite state Markov chain. As is widely recognized, this regime-switching model appears to be more versatile and more realistic. However, because of the regime switching and the nonlinearity, it is virtually impossible to obtain closed-form or analytic solutions for our problems. Thus we are seeking numerical solutions by using Markov chain approximation methods. Focusing on numerical solutions of the regime-switching models in the area of actuarial science, and based on the theory of weak convergence of probability measures, the convergence of the approximating sequences is obtained. In fact, under very broad conditions, we prove that the sequences of approximating Markov chain, the cost functions, and the value functions all converge to that of the underlying original processes. The proofs are purely probabilistic. It need not appeal to regularity properties of or even explicitly use the Bellman equation. Moreover, the feasibility of regime-switching model and Markov chain approximation method are illustrated by the examples

Valuation and Risk Measurement of Guaranteed Annuity Options under Stochastic Environment

This thesis develops stochastic modelling frameworks for the accurate pricing and risk management of complex insurance products with option-embedded features. We propose stochastic models for the evolution of the two main risk factors, the interest rate and mortality rate, which could also have a correlation structure. For the valuation problem, a general framework is put forward where correlated interest and mortality rates are modelled as affine-diffusion processes. A new concept of endowment-risk-adjusted measure is introduced to facilitate the calculation of the GAO value. As a natural offshoot of addressing GAO valuation, we derive the convex-order upper and lower bounds of GAO values by employing the comonotonicity theory. As an alternative to affine structure, we construct a more flexible modelling framework that incorporate regime-switching dynamics of interest and mortality rates governed by a continuous-time Markov chain. The corresponding endowment-risk-adjusted measures are constructed and employed to obtain more efficient GAO pricing formulae. An extension of the previous modelling set-up is further developed by integrating the affine structure and regime-switching feature. Both interest and mortality risk factors follow correlated affine structure whilst their volatilities are modulated by a Markov chain process. The change of probability measure technique is again utilised to generate pricing expressions capable of significantly cutting down computing times. Finally, the risk management aspect of GAO is investigated by evaluating various risk measurement metrics. The bootstrap technique is used to quantify standard error for the estimates of risk measures under a stochastic modelling framework in which death is the only decrement

Annuity Product Valuation and Risk Measurement under Correlated Financial and Longevity Risks

Longevity risk is a non-diversifiable risk and regarded as a pressing socio-economic challenge of the century. Its accurate assessment and quantification is therefore critical to enable pension-fund companies provide sustainable old-age security and maintain a resilient global insurance market. Fluctuations and a decreasing trend in mortality rates, which give rise to longevity risk, as well as the uncertainty in interest-rate dynamics constitute the two fundamental determinants in pricing and risk management of longevity-dependent products. We also note that historical data reveal some evidence of strong correlation between mortality and interest rates and must be taken into account when modelling their joint dynamics. In this thesis, we model and examine the impact of nonlinearity and correlation on an annuity product. A regime-switching approach to address nonlinearity is embedded both in the Lee-Carter model for mortality rate modelling and prediction, and in the Vasicek model for capturing interest-rate movements. In the valuation and computation of risk measures for an annuity that are being carried out to satisfy regulatory requirements, the correlation structure between mortality and financial risks is explicitly modelled. Our proposed modelling framework is implemented on simulated data as well as actual data covering the South Korean population and Korean bond yields for the period 1980-2015. Our results demonstrate the significant effect of correlation on annuity and risk-metric values. Finally, we found that the use of regime-switching techniques for both mortality and interest rate modelling creates a greater latitude in obtaining accurate prices, based on models’ parameter estimates, and in setting capital adequacy that avoids substantial over-reserving or under-reserving

WARNING: Physics Envy May Be Hazardous To Your Wealth!

The quantitative aspirations of economists and financial analysts have for many years been based on the belief that it should be possible to build models of economic systems - and financial markets in particular - that are as predictive as those in physics. While this perspective has led to a number of important breakthroughs in economics, "physics envy" has also created a false sense of mathematical precision in some cases. We speculate on the origins of physics envy, and then describe an alternate perspective of economic behavior based on a new taxonomy of uncertainty. We illustrate the relevance of this taxonomy with two concrete examples: the classical harmonic oscillator with some new twists that make physics look more like economics, and a quantitative equity market-neutral strategy. We conclude by offering a new interpretation of tail events, proposing an "uncertainty checklist" with which our taxonomy can be implemented, and considering the role that quants played in the current financial crisis.Comment: v3 adds 2 reference

Critical Market Crashes

This review is a partial synthesis of the book ``Why stock market crash'' (Princeton University Press, January 2003), which presents a general theory of financial crashes and of stock market instabilities that his co-workers and the author have developed over the past seven years. The study of the frequency distribution of drawdowns, or runs of successive losses shows that large financial crashes are ``outliers'': they form a class of their own as can be seen from their statistical signatures. If large financial crashes are ``outliers'', they are special and thus require a special explanation, a specific model, a theory of their own. In addition, their special properties may perhaps be used for their prediction. The main mechanisms leading to positive feedbacks, i.e., self-reinforcement, such as imitative behavior and herding between investors are reviewed with many references provided to the relevant literature outside the confine of Physics. Positive feedbacks provide the fuel for the development of speculative bubbles, preparing the instability for a major crash. We demonstrate several detailed mathematical models of speculative bubbles and crashes. The most important message is the discovery of robust and universal signatures of the approach to crashes. These precursory patterns have been documented for essentially all crashes on developed as well as emergent stock markets, on currency markets, on company stocks, and so on. The concept of an ``anti-bubble'' is also summarized, with two forward predictions on the Japanese stock market starting in 1999 and on the USA stock market still running. We conclude by presenting our view of the organization of financial markets.Comment: Latex 89 pages and 38 figures, in press in Physics Report

Managing the Managed Float in China

Despite the promising reform objectives announced on 21 July 2005, there remain much uncertainty and controversy surrounding China’s managed floating regime and its future. Hence, this research aims to provide a comprehensive analysis of the key issues raised over the course of the post-reform era. We begin by investigating whether the flexibility of RMB has increased following the reform announcement. A daily-based flexibility indicator is developed to more accurately detect the extent to which the Chinese currency is market-driven. This indicator is then utilized in a Markov switching model. The subsequent results suggest that the RMB flexibility has switched between two distinctive regimes, confirming that RMB flexibility did increase after the 2005 reform, while the so-called Fear of Floating was also apparent. Additionally, we discuss possible driving factors underlying the evolution of the RMB flexibility. Next, we consider another crucial aspect of the current managed floating regime, the equilibrium exchange rate level for RMB. The NATREX approach is selected, as we argue that it represents the most suitable solution for the purpose of our research. The empirical findings reveal not only the exogenous fundamental factors that have impacted the real exchange rate of RMB in the manner predicted by the NATREX model, but also evidence that the presumed portfolio channel did not work effectively for China in the sample period, which is contrary to the findings of previous studies. Looking ahead, we argue that the Reference Rate system could be a promising option for China. From the managerial aspect, we propose an optimal exchange rate management for China, which takes the presence of heterogeneous agents into consideration. We demonstrate that this strategy, once adopted, offers the optimal trade-off between the cost of intervention and the cost of no intervention

Estimation of Hidden Markov Models and Their Applications in Finance

Movements of financial variables exhibit extreme fluctuations during periods of economic crisis and times of market uncertainty. They are also affected by institutional policies and intervention of regulatory authorities. These structural changes driving prices and other economic indicators can be captured reasonably by models featuring regime-switching capabilities. Hidden Markov models (HMM) modulating the model parameters to incorporate such regime-switching dynamics have been put forward in recent years, but many of them could still be further improved. In this research, we aim to address some of the inadequacies of previous regime-switching models in terms of their capacity to provide better forecasts and efficiency in estimating parameters. New models are developed, and their corresponding filtering results are obtained and tested on financial data sets. The contributions of this research work include the following: (i) Recursive filtering algorithms are constructed for a regime-switching financial model consistent with no-arbitrage pricing. An application to the filtering and forecasting of futures prices under a multivariate set-up is presented. (ii) The modelling of risk due to market and funding liquidity is considered by capturing the joint dynamics of three time series (Treasury-Eurodollar spread, VIX and S\&P 500 spread-derived metric), which mirror liquidity levels in the financial markets. HMM filters under a multi-regime mean- reverting model are established. (iii) Kalman filtering techniques and the change of reference probability-based filtering methods are integrated to obtain hybrid algorithms. A pairs trading investment strategy is supported by the combined power of both HMM and Kalman filters. It is shown that an investor is able to benefit from the proposed interplay of the two filtering methods. (iv) A zero-delay HMM is devised for the evolution of multivariate foreign exchange rate data under a high-frequency trading environment. Recursive filters for quantities that are functions of a Markov chain are derived, which in turn provide optimal parameter estimates. (v) An algorithm is designed for the efficient calculation of the joint probability function for the occupation time in a Markov-modulated model for asset returns under a general number of economic regimes. The algorithm is constructed with accessible implementation and practical considerations in mind