Pricing options on mean reverting underliers

For mean reverting base probabilities, option pricing models are developed, using an explicit measure change induced by the selection of a terminal time and a terminal random variable. The models employed are the square root process and an OU equation driven by centred variance gamma shocks. VIX options are calibrated using the square root process. The OU equation driven by centred variance gamma shocks is applied in pricing options on the ratio of the stock price for J. P. Morgan Chase (JPM) to the Exchange Traded Fund for the financial sector with ticker XLF. For the purposes of calibrating the ratio option pricing model to market data, we indirectly infer the prices for stock options on JPM from the prices for options on the ratio, by hedging the conditional value of JPM options given XLF, using options on XLF. The implied volatilities for the options on the ratio are then indirectly observed to be fairly flat. This suggests that for JPM, the use XLF as a benchmark is a possibly good choice. It is shown to perform better than the use of the S&P 500 index. Furthermore, though the use of an unrelated stock price like Johnson and Johnson as a benchmark for JPM provides as a good fit as does the use of XLF, this comes at the cost of requiring a considerable smile for the implied volatilities on the ratio options and hence a more complex model for the implied distribution on the ratio.     

Option pricing when correlations are stochastic: an analytical framework

In this paper we develop a novel market model where asset variances–covariances evolve stochastically. In addition shocks on asset return dynamics are assumed to be linearly correlated with shocks driving the variance–covariance matrix. Analytical tractability is preserved since the model is linear-affine and the conditional characteristic function can be determined explicitly. Quite remarkably, the model provides prices for vanilla options consistent with observed smile and skew effects, while making it possible to detect and quantify the correlation risk in multiple-asset derivatives like basket options. In particular, it can reproduce and quantify the asymmetric conditional correlations observed on historical data for equity markets. As an illustrative example, we provide explicit pricing formulas for rainbow “Best-of” options.     

A pure martingale dual for multiple stopping

In this paper, we present a dual representation for the multiple stopping problem, hence multiple exercise options. As such, it is a natural generalization of the method in Rogers (Math. Finance 12:271–286, 2002) and Haugh and Kogan (Oper. Res. 52:258–270, 2004) for the standard stopping problem for American options. We term this representation a ‘pure martingale’ dual as it is solely expressed in terms of an infimum over martingales rather than an infimum over martingales and stopping times as in Meinshausen and Hambly (Math. Finance 14:557–583, 2004). For the multiple dual representation, we propose Monte Carlo simulation methods which require only one degree of nesting.     

Modeling a Multivariate Transaction Process

In this paper the dynamics of a joint transaction process areinvestigated. The transaction process is characterized by fourmarks: price changes, transaction volumes, bid–ask spreadsand intertrade durations. Based on a copula approach, a modelfor their joint density is proposed, which avoids forcing apriori assumptions on the instantaneous causality relationshipsbetween the four variables as necessary in decomposition models,where the joint density is decomposed into its conditional andunconditional densities. The price change process is treatedas a discrete process and specified with an integer count hurdlemodel and the transaction volumes, bid–ask spreads, andtrade durations processes are modeled along the lines of fractionallyintegrated autoregressive conditional models, which are suitedvery well to capture the high persistency, empirically observedin these processes. The model is applied to three stocks tradedat the New York Stock Exchange (NYSE) in May, 2001 and we investigateseveral market microstructure hypotheses in the empirical partof this paper.     

Approximate option pricing and hedging in the CEV model via path-wise comparison of stochastic processes

This paper presents a methodology of finding explicit boundaries for some financial quantities via comparison of stochastic processes. The path-wise comparison theorem is used to establish domination of the stock price process by a process with a known distribution that is relatively simple. We demonstrate how the comparison theorem can be applied in the constant elasticity of variance model to derive closed-form expressions for option price bounds, an approximate hedging strategy and a conditional value-at-risk estimate. We also provide numerical examples and compare precision of our method with the distribution-free approach.     

Solving an asset pricing model with hybrid internal and external habits,and autocorrelated Gaussian shocks

We derive an explicit formula for the price-dividend ratio of a generalized version of Abel’s asset pricing model. This model is generalized in two ways: first, consumption (dividend) growth is assumed to be an AR(1) process subject to Gaussian random shocks, and second, the investor’s preferences are allowed to be a convex combination of internal and external habits. With an internal habit weight, 50%, and a coefficient of risk aversion, 3.25, simulation results match the historic US equity premium and risk free interest rate.      

Interacting particle systems for the computation of rare credit portfolio losses

In this paper, we introduce the use of interacting particle systems in the computation of probabilities of simultaneous defaults in large credit portfolios. The method can be applied to compute small historical as well as risk-neutral probabilities. It only requires that the model be based on a background Markov chain for which a simulation algorithm is available. We use the strategy developed by Del Moral and Garnier in (Ann. Appl. Probab. 15:2496–2534, 2005) for the estimation of random walk rare events probabilities. For the purpose of illustration, we consider a discrete-time version of a first passage model for default. We use a structural model with stochastic volatility, and we demonstrate the efficiency of our method in situations where importance sampling is not possible or numerically unstable.      

Biometric Solvency Risk for Portfolios of General Life Contracts. I. The Single-Life Multiple Decrement Case

Abstract

Solvency II splits life insurance risk into seven risk classes consisting of three biometric risks (mortality risk, longevity risk, and disability/morbidity risk) and four nonbiometric risks (lapse risk, expense risk, revision risk, and catastrophe risk). The best estimate liabilities for the biometric risks are valued with biometric life tables (mortality and disability tables), while those of the nonbiometric risks require alternative valuation methods. The present study is restricted to biometric risks encountered in traditional single-life insurance contracts with multiple causes of decrement. Based on the results of quantitative impact studies, process risk was deemed to be not significant enough to warrant an explicit calculation. It was therefore assumed to be implicitly included in the systematic/parameter risk, resulting in a less complex standard formula. For the purpose of internal models and improved risk management, it appears important to capture separately or simultaneously all risk components of biometric risks. Besides its being of interest for its own sake, this leads to a better understanding of the standard approach and its application extent. Based on a total balance sheet approach we express the liability risk solvency capital of an insurance portfolio as value-at-risk and conditional value-at-risk of the prospective liability risk understood as random present value of future cash flows at a given time. The proposed approach is then applied to determine the biometric solvency capital for a portfolio of general life contracts. Using the conditional mean and variance of a portfolio’s prospective liability risk and a gamma distribution approximation we obtain simple solvency capital formulas as well as corresponding solvency capital ratios. To account for the possibility of systematic/parameter risk, we propose either to shift the biometric life tables or to apply a stochastic biometric model, which allows for random biometric rates. A numerical illustration for a cohort of immediate life annuities in arrears reveals the importance of process risk in the assessment of longevity risk solvency capital.     

Dual pricing of multi-exercise options under volume constraints

In this paper, we study the pricing problem of multi-exercise options under volume constraints. The volume constraint is modelled by an adapted process with values in the positive integers, which describes the maximal number of rights to be exercised at a given time. We derive a representation of the marginal value of an additional nth right as a standard single stopping problem with a modified cash-flow process. This representation then leads to a dual pricing formula, which generalizes a result by Meinshausen and Hambly (Math. Finance 14:557–583, 2004) from the standard multi-exercise option (with at most one right per time step) to general constraints. We also state an explicit Monte Carlo algorithm for computing confidence intervals for the price of multi-exercise options under volume constraints and present numerical results for the pricing of a swing contract in an electricity market.     

Discussions on the spurious hyperbolic memory in the conditional variance and a new model

This paper studies the spurious hyperbolic memory in the conditional variance caused by the Markov Regime-Switching GARCH (MRS-GARCH) process. We firstly propose an illustrative cause of this spuriousness and provide simulation evidence. An MRS Hyperbolic GARCH (MRS-HGARCH) model is then developed to successfully address it. Related statistical properties including the stationarity conditions and asymptotic behaviours of the maximum likelihood estimators of the MRS-HGARCH process are also investigated. An empirical study of the S&P 500 and TOPIX indexes returns is then conducted which demonstrates that our MRS-HGARCH model can provide a more reliable estimator of the hyperbolic-memory parameter and outperform both the HGARCH and MRS-GARCH models.     

Asymptotic Analysis of Multivariate Tail Conditional Expectations

Abstract

Tail conditional expectations refer to the expected values of random variables conditioning on some tail events and are closely related to various coherent risk measures. In the univariate case, the tail conditional expectation is asymptotically proportional to Value-at-Risk, a popular risk mea-sure. The focus of this paper is on asymptotic relations between the multivariate tail conditional expectation and Value-at-Risk for heavy-tailed scale mixtures of multivariate distributions. Explicit tail estimates of multivariate tail conditional expectations are obtained using the method of regular variation. Examples involving multivariate Pareto and elliptical distributions, as well as application to risk allocation, are also discussed.     

There is a risk-return trade-off after all

This paper studies the intertemporal relation between the conditional mean and the conditional variance of the aggregate stock market return. We introduce a new estimator that forecasts monthly variance with past daily squared returns, the mixed data sampling (or MIDAS) approach. Using MIDAS, we find a significantly positive relation between risk and return in the stock market. This finding is robust in subsamples, to asymmetric specifications of the variance process and to controlling for variables associated with the business cycle. We compare the MIDAS results with tests of the intertemporal capital asset pricing model based on alternative conditional variance specifications and explain the conflicting results in the literature. Finally, we offer new insights about the dynamics of conditional variance.     

Using self-organizing maps to adjust for intra-day seasonality

The existence of an intra-day seasonality component in financial market variables (volatility, volume, activity, etc.) has been highlighted in many previous studies. To remove this cyclical component from raw data, many researchers use the intra-day average observations model (IAOM) and/or some smoothing techniques (e.g. the kernel method). When the seasonality is related to the first moment (the conditional expectation) and involves only a deterministic component, the IAOM method succeeds in estimating the periodicity almost perfectly. However, when seasonality affects the first or the second moment (the conditional variance) of the data and contains both deterministic and stochastic components, both IAOM and the kernel method fail to capture it. We introduce self-organizing maps (SOM) as a solution. SOM are based on neural network learning and nonlinear projections. Their flexibility allows seasonality to be captured even in the presence of stochastic cycles.     

An analysis of a three-factor model proposed by the Danish Society of Actuaries for forecasting and risk analysis

This paper provides the explicit solution to the three-factor diffusion model recently proposed by the Danish Society of Actuaries to the Danish industry of life insurance and pensions. The solution is obtained by use of the known general solution to multidimensional linear stochastic differential equation systems. With offset in the explicit solution, we establish the conditional distribution of the future state variables which allows for exact simulation. Using exact simulation, we illustrate how simulation of the system can be improved compared to a standard Euler scheme. In order to analyze the effect of choosing the exact simulation scheme over the traditional Euler approximation scheme frequently applied by practitioners, we carry out a simulation study. We show that due to its recursive nature, the Euler scheme becomes computationally expensive as it requires a small step size in order to minimize discretization errors. Using our exact simulation scheme, one is able to cut these computational costs significantly and obtain even better forecasts. As probability density tail behavior is key to expected investment portfolio performance, we further conduct a risk analysis in which we compare well-known risk measures under both schemes. Finally, we conduct a sensitivity analysis and find that the relative performance of the two schemes depends on the chosen model parameter estimates.     

Asian options and meromorphic Lévy processes

One method to compute the price of an arithmetic Asian option in a Lévy driven model is based on an exponential functional of the underlying Lévy process: If we know the distribution of the exponential functional, we can calculate the price of the Asian option via the inverse Laplace transform. In this paper, we consider pricing Asian options in a model driven by a general meromorphic Lévy process. We prove that the exponential functional is equal in distribution to an infinite product of independent beta random variables, and its Mellin transform can be expressed as an infinite product of gamma functions. We show that these results lead to an efficient algorithm for computing the price of the Asian option via the inverse Mellin–Laplace transform, and we compare this method with some other techniques.     

Testing For Threshold Nonlinearity in Short-Term Interest Rates

This article addresses some empirical problems in the term structureof interest rates using a threshold autoregressive frameworkwith GARCH errors. This framework provides a parsimonious representationof some stylized features of interest rate data and facilitatesstatistical inference in the presence of high persistence andconditional heteroskedasticity. We propose a bootstrap-basedLM test for linearity in the conditional mean and variance functions.The empirical results indicate a presence of threshold nonlinearitiesin the AR and GARCH representations of the conditional momentsof short-term rate. The explicit modeling of these nonlinearitiesappears to improve the stability properties of the process forspot rate. The article also reports that allowing for thresholdnonlinearities in conditional mean and variance leads to significantforecast improvements. The economic significance of these findingsis evaluated by the term structure implications of the estimatedTAR-GARCH model.     

Consumption in incomplete markets

We develop a method to find approximate solutions, and their accuracy, to consumption–investment problems with isoelastic preferences and infinite horizon, in incomplete markets where state variables follow a multivariate diffusion. We construct upper and lower contractions; these are fictitious complete markets in which state variables are fully hedgeable, but their dynamics is distorted. Such contractions yield pointwise upper and lower bounds for both the value function and the optimal consumption of the original incomplete market, and their optimal policies are explicit in typical models. Approximate consumption–investment policies coincide with the optimal one if the market is complete or utility is logarithmic.     

Valuation of default-sensitive claims under imperfect information

We propose a valuation method for financial assets subject to default risk, where investors cannot observe the state variable triggering the default but observe a correlated price process. The model is sufficiently general to encompass a large class of structural models and can be seen as a generalization of the model of Duffie and Lando (Econometrica 69:633–664, [2001]). In this setting we prove that the default time is totally inaccessible in the market’s filtration and derive the conditional default probabilities and the intensity process. Finally, we provide pricing formulas for default-sensitive claims and illustrate in particular examples the shapes of the credit spreads.