Copulas in finance and insurance

Copulas provide a potential useful modeling tool to represent the dependence structure among variables and to generate joint distributions by combining given marginal distributions. Simulations play a relevant role in finance and insurance. They are used to replicate efficient frontiers or extremal values, to price options, to estimate joint risks, and so on. Using copulas, it is easy to construct and simulate from multivariate distributions based on almost any choice of marginals and any type of dependence structure. In this paper we outline recent contributions of statistical modeling using copulas in finance and insurance. We review issues related to the notion of copulas, copula families, copula-based dynamic and static dependence structure, copulas and latent factor models and simulation of copulas. Finally, we outline hot topics in copulas with a special focus on model selection and goodness-of-fit testing

Copulas in finance and insurance

Copulas provide a potential useful modeling tool to represent the dependence structure among variables and to generate joint distributions by combining given marginal distributions. Simulations play a relevant role in finance and insurance. They are used to replicate efficient frontiers or extremal values, to price options, to estimate joint risks, and so on. Using copulas, it is easy to construct and simulate from multivariate distributions based on almost any choice of marginals and any type of dependence structure. In this paper we outline recent contributions of statistical modeling using copulas in finance and insurance. We review issues related to the notion of copulas, copula families, copula-based dynamic and static dependence structure, copulas and latent factor models and simulation of copulas. Finally, we outline hot topics in copulas with a special focus on model selection and goodness-of-fit testing.Dependence structure, Extremal values, Copula modeling, Copula review

Multiple regression approach to fit suitable model for all share price index with other important related factors

This study investigates the effects of some important sectors indices such as: Bank Finance & Insurance, Manufacturing, Trading, Hotels & Travels and Services on All Share Price Index (ASPI) in emerging Sri Lankan stock market using monthly data for the period from January 2005 to December 2014. It is aimed to find a suitable multiple regression model for Bank Finance & Insurance, Manufacturing, Trading, Hotels & Travels and Services on ASPI. In this paper, multiple linear regression model, multiple log linear model and multiple first difference of log linear models are analyzed using appropriate statistical tests. The first difference of log linear model is more appropriate to fit ASPI and other important related factors

FX Smile in the Heston Model

The Heston model stands out from the class of stochastic volatility (SV) models mainly for two reasons. Firstly, the process for the volatility is non-negative and mean-reverting, which is what we observe in the markets. Secondly, there exists a fast and easily implemented semi-analytical solution for European options. In this article we adapt the original work of Heston (1993) to a foreign exchange (FX) setting. We discuss the computational aspects of using the semi-analytical formulas, performing Monte Carlo simulations, checking the Feller condition, and option pricing with FFT. In an empirical study we show that the smile of vanilla options can be reproduced by suitably calibrating three out of five model parameters.Comment: Chapter prepared for the 2nd edition of Statistical Tools for Finance and Insurance, P.Cizek, W.Haerdle, R.Weron (eds.), Springer-Verlag, forthcoming in 201

Extremes and Robustness: A Contradiction?

Stochastic models play an important role in the analysis of data in many different fields, including finance and insurance. Many models are estimated by procedures that lose their good statistical properties when the underlying model slightly deviates from the assumed one. Robust statistical methods can improve the data analysis process of the skilled analyst and provide him with useful additional information. For this anniversary issue, we discuss some aspects related to robust estimation in the context of extreme value theory (EVT). Using real data and simulations, we show how robust methods can improve the quality of EVT data analysis by providing information on influential observations, deviating substructures and possible mis-specification of a model while guaranteeing good statistical properties over a whole set of underlying distributions around the assumed on

A Note on Asymptotic Normality of a Copula Function in Regression Model

Over the last decade, there has been significant and rapid development of the theory of copulas. Much of the work has been motivated by their applications to stochastic processes, economics, risk management, finance, insurance, the environment (hydrology, climate, etc.), survival analysis, and medical sciences. In many statistical models. The copula approach is a way to solve the difficult problem of finding the whole bivariate or multivariate distribution. In this paper, we give the asymptotic normality of the copulas function in a regression model

Measuring Systemic Risk in the Finance and Insurance Sectors

A significant contributing factor to the Financial Crisis of 2007–2009 was the apparent interconnectedness among hedge funds, banks, brokers, and insurance companies, which amplified shocks into systemic events. In this paper, we propose five measures of systemic risk based on statistical relations among the market returns of these four types of financial institutions. Using correlations, cross-autocorrelations, principal components analysis, regime-switching models, and Granger causality tests, we find that all four sectors have become highly interrelated and less liquid over the past decade, increasing the level of systemic risk in the finance and insurance industries. These measures can also identify and quantify financial crisis periods. Our results suggest that while hedge funds can provide early indications of market dislocation, their contributions to systemic risk may not be as significant as those of banks, insurance companies, and brokers who take on risks more appropriate for hedge funds

Statistical Inference and Pricing for Regime Switching Models in Finance and Insurance

This thesis studies the estimation, goodness-of-fit testing, pricing and sampling problems for regime switching models, which are popularly used in financial markets. Specifically, we consider such models whose distributions are characterized by their characteristic functions, for example, Levy processes. The thesis contains the following contents: Chapter 1 introduces regime switching models and Levy processes. Then we present the problems we would like to address in the following chapters and our main contributions to these problems. Chapter 2 studies the estimation problem for regime switching Levy processes. We extend an existing estimation method that is based on characteristic functions to our models. Meanwhile, we compare the estimation results obtained by the proposed estimation method with those obtained by the expectation-maximization (EM) algorithm. We also address several computational challenges within the proposed estimation method. Chapter 3 studies the goodness-of-fit testing problem for regime switching models, where we extend two existing goodness-of-fit tests. Both of the proposed tests are based on characteristic functions. Chapter 4 applies the estimation and testing methods proposed in Chapters 2 and 3 to a set of S&P 500 real data. Chapter 5 studies the pricing problem for regime switching Levy processes. We propose a numerical pricing method that provides a unified pricing framework. The proposed method is illustrated by pricing European and Bermudan options and ratchet equity-index annuities (EIAs) with surrender risk. Chapter 6 studies the problem of sampling conditioned processes of regime switching models, where we propose an algorithm to sample paths from conditioned processes for a two-regime switching Black-Scholes model. Then we apply the proposed algorithm to the problems of pricing and static hedging of path-dependent options, where we use an Asian call option for illustrations. Chapter 7 lists several topics for future research

Computational Finance

With the availability of new and more comprehensive financial market data, making headlines of massive public interest due to recent periods of extreme volatility and crashes, the field of computational finance is evolving ever faster thanks to significant advances made theoretically, and to the massive increase in accessible computational resources. This volume includes a wide variety of theoretical and empirical contributions that address a range of issues and topics related to computational finance. It collects contributions on the use of new and innovative techniques for modeling financial asset returns and volatility, on the use of novel computational methods for pricing, hedging, the risk management of financial instruments, and on the use of new high-dimensional or high-frequency data in multivariate applications in today’s complex world. The papers develop new multivariate models for financial returns and novel techniques for pricing derivatives in such flexible models, examine how pricing and hedging techniques can be used to assess the challenges faced by insurance companies, pension plan participants, and market participants in general, by changing the regulatory requirements. Additionally, they consider the issues related to high-frequency trading and statistical arbitrage in particular, and explore the use of such data to asses risk and volatility in financial markets

Risk pricing practices in finance, insurance and construction

A review of current risk pricing practices in the financial, insurance and construction sectors is conducted through a comprehensive literature review. The purpose was to inform a study on risk and price in the tendering processes of contractors: specifically, how contractors take account of risk when they are calculating their bids for construction work. The reference to mainstream literature was in view of construction management research as a field of application rather than a fundamental academic discipline. Analytical models are used for risk pricing in the financial sector. Certain mathematical laws and principles of insurance are used to price risk in the insurance sector. construction contractors and practitioners are described to traditionally price allowances for project risk using mechanisms such as intuition and experience. Project risk analysis models have proliferated in recent years. However, they are rarely used because of problems practitioners face when confronted with them. A discussion of practices across the three sectors shows that the construction industry does not approach risk according to the sophisticated mechanisms of the two other sectors. This is not a poor situation in itself. However, knowledge transfer from finance and insurance can help construction practitioners. But also, formal risk models for contractors should be informed by the commercial exigencies and unique characteristics of the construction sector