Martingale Measure Research Articles

A numerical approach to pricing exchange options under stochastic volatility and jump-diffusion dynamics

We consider a method of lines (MOL) approach to determine prices of European and American exchange options when underlying asset prices are modeled with stochastic volatility and jump-diffusion dynamics. As with any other numerical scheme for partial differential equations (PDEs), the MOL becomes increasingly complex when higher dimensions are involved, so we first simplify the problem by transforming the exchange option into a call option written on the ratio of the yield processes of the two assets. This is achieved by taking the second asset yield process as the numéraire. Under the equivalent martingale measure induced by this change of numéraire, we derive the exchange option pricing integro-partial differential equations (IPDEs) and investigate the early exercise boundary of the American exchange option. We then discuss a numerical solution of the IPDEs using the MOL, its implementation using computing software and possible alternative boundary conditions at the far limits of the computational domain. Our analytical and numerical investigation shows that the near-maturity behavior of the early exercise boundary of the American exchange option is significantly influenced by the dividend yields and the presence of jumps in the underlying asset prices. Furthermore, with the numerical results generated by the MOL, we are able to show that key jump and stochastic volatility parameters significantly affect the early exercise boundary and exchange option prices. Our numerical analysis also verifies that the MOL performs more efficiently, than other finite difference methods or simulation approaches for American options, since the MOL integrates the computation of option prices, greeks and the early exercise boundary, and does so with the least error.

A quasi-sure optional decomposition and super-hedging result on the Skorokhod space

We prove a robust super-hedging duality result for path-dependent options on assets with jumps in a continuous-time setting. It requires that the collection of martingale measures is rich enough and that the payoff function satisfies some continuity property. It is a by-product of a quasi-sure version of the optional decomposition theorem, which can also be viewed as a functional version of Itô’s lemma that applies to non-smooth functionals (of càdlàg processes) which are concave in space and nonincreasing in time, in the sense of Dupire.

A unified framework for robust modelling of financial markets in discrete time

We unify and establish equivalence between the pathwise and the quasi-sure approaches to robust modelling of financial markets in finite discrete time. In particular, we prove a fundamental theorem of asset pricing and a superhedging theorem which encompass the formulations of Bouchard and Nutz [12] and Burzoni et al. [13]. In bringing the two streams of literature together, we examine and compare their many different notions of arbitrage. We also clarify the relation between robust and classical ℙ-specific results. Furthermore, we prove when a superhedging property with respect to the set of martingale measures supported on a set Omega of paths may be extended to a pathwise superhedging on Omega without changing the superhedging price.

Pricing of Index Options in Incomplete Markets

We characterize a set of risk-neutral measures associated with a comprehensive class of risk averse investors. From this set, we show how to construct option price bounds and recover the implied gamma: a parameter uniquely identifying the marginal investor pricing a given option. Empirically, we find that S&P 500 option prices are reconciled by heterogeneous marginal investors who differ in their assessment of tail risk. This heterogeneity is time-varying, decreases during financial crises, and provides novel insights into the skew patterns of index options. The recovered investors' preferences related to compensation for downside risk help predict future market returns.

Forward Backward Stochastic Model of Financial Asset Pricing with Idiosyncratic Noise

A new acceptable price approach to stochastic endpoint determination at given horizon accounting for the marginal investor beliefs and behaviour was proposed. Two-sided filtration with FBSDE defined stochastic dynamics was formulated for acceptable asset price under the risk-neutral probability measure, at that the target price distribution is characterized by the averaged over active market agent subset parameters. For the current price at market equilibrium, the acceptable price of risk distribution was found. The implied volatility dependencies for the equilibrium conditions and with predicted utility and liquidity premiums were determined. A generalized solution for the forward-backward stochastic problem and a partial solution for the formulated for options stochastic terminal conditions were found. The deep learning algorithm with simulated idiosyncratic noise was tested.

Pricing of index options in incomplete markets

We characterize a set of risk-neutral measures associated with a comprehensive class of risk averse investors. From this set, we show how to construct option price bounds and recover the implied γ: a parameter uniquely identifying the marginal investor pricing a given option. Empirically, we find that S&P 500 option prices are reconciled by heterogeneous marginal investors who differ in their assessment of tail risk. This heterogeneity is time-varying, decreases during financial crises, and provides novel insights into the skew patterns of index options. The recovered investors’ preferences related to compensation for downside risk help predict future market returns.

A deep learning approach for computations of exposure profiles for high-dimensional Bermudan options

In this paper, we propose a neural network-based method for approximating expected exposures and potential future exposures of Bermudan options. In a first phase, the method relies on the Deep Optimal Stopping algorithm (DOS) proposed by Becker, Cheridito, and Jentzen (2019), which learns the optimal stopping rule from Monte-Carlo samples of the underlying risk factors. Cashflow paths are then created by applying the learned stopping strategy on a new set of realizations of the risk factors. Furthermore, in a second phase the cashflow paths are projected onto the risk factors to obtain approximations of pathwise option values. The regression step is carried out by ordinary least squares as well as neural networks, and it is shown that the latter results in more accurate approximations.The expected exposure is formulated, both in terms of the cashflow paths and in terms of the pathwise option values and it is shown that a simple Monte-Carlo average yields accurate approximations in both cases. The potential future exposure is estimated by the empirical α-percentile.Finally, it is shown that the expected exposures, as well as the potential future exposures can be computed under either, the risk neutral measure, or the real world measure, without having to re-train the neural networks.

Premium Valuation of the Pension Benefit Guaranty Corporation with Regime Switching

The Pension Benefit Guaranty Corporation (PBGC) provides insurance coverage for single-employer and multiemployer pension plans in private sector. It has played an important role in protecting the retirement security for over 1.5 million people since it was established about half a decade ago. PBGC collects insurance premiums from employers that sponsor insured pension plans for its coverage and receives funds from pension plans that it takes over. To address the issue of underfunded plans that the PBGC has, this work studies how to evaluate risk-based premiums for the PBGC. Inspired by a couple of existing work in which the premature termination of pension fund and distress termination of sponsor assets are analyzed separately, our work examines the two types of terminations under one framework and considers the occurrence of each termination dynamically. Given that market regime might have a big impact on the dynamics of both pension fund and sponsor’s assets, we thus formulate our model using a continuous-time two-state Markov chain in which bull market and bear market are delineated. We thus formulate our model using a continuous-time two-state Markov Chain in which bull market and bear market are delineated. In other words, the pension fund and sponsor assets are market dependent in our work. Given that this additional uncertainty described by regime switching makes the market incomplete, we therefore utilize the Esscher transform to determine an equivalent martingale measure and apply the risk neutral pricing method to obtain the closed-form expressions for premium of PBGC. In addition, we carry out numerical analysis to demonstrate our results and observe that premium increases according to the retirement benefit irrespective of the type of terminations. In comparison to the case of early distress termination of sponsor assets, the premium goes up more quickly when premature termination of pension funds occurs first due to the fact that pension fund is the first venue of retirement security. Furthermore, we look at how the premium changes with respect to other key parameters as well and make some detailed observations in the section of numerical analysis.

Short-Term Interest Rate Estimation by Filtering in a Model Linking Inflation, the Central Bank and Short-Term Interest Rates

We consider the model of Antonacci, Costantini, D’Ippoliti, Papi (arXiv:2010.05462 [q-fin.MF], 2020), which describes the joint evolution of inflation, the central bank interest rate, and the short-term interest rate. In the case when the diffusion coefficient does not depend on the central bank interest rate, we derive a semi-closed valuation formula for contingent derivatives, in particular for Zero Coupon Bonds (ZCBs). By using ZCB yields as observations, we implement the Kalman filter and obtain a dynamical estimate of the short-term interest rate. In turn, by this estimate, at each time step, we calibrate the model parameters under the risk-neutral measure and the coefficient of the risk premium. We compare the market values of German interest rate yields for several maturities with the corresponding values predicted by our model, from 2007 to 2015. The numerical results validate both our model and our numerical procedure.

Option pricing under stochastic volatility models with latent volatility

An important challenge regarding the pricing of derivatives is related to the latent nature of volatility. Most studies disregard the uncertain nature of volatility when pricing options; the few authors who account for it typically consider the risk-neutral posterior distribution of the latent volatility. As the latter distribution differs from its physical measure counterpart, this leads to at least two issues: (1) it generates some unwanted path dependence and (2) it oftentimes requires to simultaneously track the physical and risk-neutral distributions of the latent volatility. This article presents pricing approaches purging such a path-dependence issue. This is achieved by modifying conventional pricing approaches (e.g. the Girsanov transform) to formally recognize the uncertainty about the latent volatility during the pricing procedure. The two proposed risk-neutral measures circumventing the aforementioned undesired path-dependence feature are based on the extended Girsanov principle and the Esscher transform. We also show that such pricing approaches are feasible, and we provide numerical implementation schemes.

The closed-form option pricing formulas under the sub-fractional Poisson volatility models

A new fractional process called the sub-fractional Poisson process NH(t) is proposed, which has continuous sample paths, long- memory, leptokurtosis and heavy tail distribution, is of convenience to price options and simulate the variance process of risk asset return. Based on the sub-fractional Poisson process NH(t) the new fractional variance processes have been proposed, which may capture the skewness and the long-memory as well as mean-reverting in the stock price volatilities. In particular, the characteristic function method for option pricing is given, and the analytical formulas for European option price C(t,St) have been obtained under the risk-neutral probability measure.

Implied Equity Premium and Market Beta

We extend the ex-ante mean-variance (SVIX) models of Martin (2017) and Martin-Wagner (2019) to a mean-variance-asymmetry (AVIX) framework for incorporating higher-moment and co-moment risk in asset pricing. AVIX is a risk-neutral measure of the left-tail asymmetries in return that corrects the SVIX approach's downside bias. The options implied market beta of equity is a weighted sum of the beta of SVIX and that of AVIX. Empirically, the implied betas possess significant predictability of risk/return relationship and the hedging ability against bear/crashing markets. We develop an investible portfolio MKT* that mimics realized outcomes on the implied market index adjusted for volatility-asymmetry.

Option pricing under a Markov-modulated Merton jump-diffusion dividend

In this paper, the valuation of European option is investigated when the discrete dividends are described by Markov-modulated Merton jump-diffusion process. According to the dividend discount theory, we regard the stock price as the net present value of all future dividends. The regime switching Esscher transform is applied to determine a risk-neutral measure. The closed form solution of European option is obtained under the condition that the dividend payments are announced in advance. Numerical simulations for the European call option prices are provided.

Kelly trading and option pricing

AbstractIn this paper we show that a Kelly trader is indifferent to trade a derivative if and only if the no‐arbitrage price is uniquely given by the minimal martingale measure price, thus providing a natural selection mechanism for option pricing in incomplete markets. We also show that the unique Kelly indifference price results in market equilibrium in the sense that no Kelly trader can improve the magnitude of his instantaneous Sharpe ratio, by trading the derivative, given the actions of the other market participants.

On the stability of the martingale optimal transport problem: A set-valued map approach

Continuity of the value of the martingale optimal transport problem on the real line w.r.t. its marginals was recently established in Backhoff-Veraguas and Pammer (2019) and Wiesel (2019). We present a new perspective of this result using the theory of set-valued maps. In particular, using results from Beiglböck et al. (2021), we show that the set of martingale measures with fixed marginals is continuous, i.e., lower- and upper hemicontinuous, w.r.t. its marginals. Moreover, we establish compactness of the set of optimizers as well as upper hemicontinuity of the optimizers w.r.t. the marginals.

Modeling Price Dynamics, Optimal Portfolios, and Option Valuation for Cryptoassets

The authors demonstrate the construction of an optimal dynamic portfolio of cryptoassets that minimizes either return variance or conditional value at risk. One can view such a portfolio as a minimum-risk index for this asset class. They carefully backtested the dynamic portfolio model and developed a fair valuation model for options based on a dynamic pricing model for the underlying cryptoasset index. They obtain the valuation by passing from the natural world to the equivalent martingale measure via the Esscher transform. The work underscores the need for a cryptoasset index–based exchange-traded fund, the development of derivatives, particularly for cryptoportfolio insurance purposes, and the development of (nearly) riskless rates for this asset class. <b>TOPICS:</b>Currency, options, statistical methods, VAR and use of alternative risk measures of trading risk <b>Key Findings</b> ▪ This article provides a methodology for constructing an optimal dynamic portfolio of major cryptoassets that minimizes either return variance or conditional value at risk. ▪ The authors develop a fair valuation model for options based on a dynamic pricing model for the underlying cryptoindex. ▪ The work emphasizes the need for a cryptoasset index–based exchange-traded fund and the development of derivatives, in particular for cryptoportfolio insurance purposes.

Risk premia in electricity derivatives markets

This study examines the prices of options contingent on electricity futures traded on the European Energy Exchange, with the aim to recover the probability density functions and risk premia. After we extract the risk-neutral probability density functions from prices of such options, we transform the risk-neutral densities into real-world densities using both parametric and non-parametric statistical calibration methods and investigate the evolution of risk premia and pricing kernels. We find that both risk-neutral and real-world option-implied densities accurately forecast realized futures electricity prices. Positively skewed densities suggest that there is an inverse (or positive) leverage effect in the electricity market, meaning that a higher probability of large price increases in electricity has been incorporated in the traded option prices. In addition, we find that the state price densities are mostly increasing, implying that investors are more risk-averse to high electricity prices. Over a period of 15 years, our results provide evidence of negative market price of risk and risk premia in this new market.

Risk‐neutral pricing techniques and examples

AbstractTractability and flexibility are among the two most attractive features of models in mathematical finance. For the pricing of derivative products, to avoid arbitrage opportunities, the fundamental theorem of asset pricing requires the existence of an equivalent martingale measure under which the discounted price process is a (local) martingale. In the semimartingale setting, the traditional approach of risk‐neutral valuation uses a change of measure invoking Girsanov's theorem. This approach often destroys the tractability of the process which is undesirable for quantitative finance applications. To overcome these limitations, we employ a transformation based on the concept of intertwining relationships that allows us to convert Markovian semigroups into a pricing semigroup while keeping its tractability. In order to illustrate the usefulness of this approach, we apply it to exponential Lévy, positive self‐similar, and generalized CIR processes that all fall within the class of polynomial processes introduced by Cuchiero et al. (2012). Furthermore, for the jump CIR class, relying on recent work of Patie and Savov (2019), we provide an eigenvalues expansion of the pricing semigroup and carry out some numerical experiments.

LOCALIZED RADIAL BASIS FUNCTIONS FOR NO-ARBITRAGE PRICING OF OPTIONS UNDER STOCHASTIC ALPHA–BETA–RHO DYNAMICS

AbstractClosed-form explicit formulas for implied Black–Scholes volatilities provide a rapid evaluation method for European options under the popular stochastic alpha–beta–rho (SABR) model. However, it is well known that computed prices using the implied volatilities are only accurate for short-term maturities, but, for longer maturities, a more accurate method is required. This work addresses this accuracy problem for long-term maturities by numerically solving the no-arbitrage partial differential equation with an absorbing boundary condition at zero. Localized radial basis functions in a finite-difference mode are employed for the development of a computational method for solving the resulting two-dimensional pricing equation. The proposed method can use either multiquadrics or inverse multiquadrics, which are shown to have comparable performances. Numerical results illustrate the accuracy of the proposed method and, more importantly, that the computed risk-neutral probability densities are nonnegative. These two key properties indicate that the method of solution using localized meshless methods is a viable and efficient means for price computations under SABR dynamics.

Hedging strategy for unit-linked life insurance contracts with self-exciting jump clustering

&lt;p style='text-indent:20px;'&gt;This paper studies the hedging problem of unit-linked life insurance contracts in an incomplete market presence of self-exciting (clustering) effect, which is described by a Hawkes process. Applying the local risk-minimization method, we manage to obtain closed-form expressions of the locally risk-minimizing hedging strategies for both pure endowment and term insurance contracts. Besides, we demonstrate the existence of the minimal martingale measure and perform numerical analyses. Our numerical results indicate that jump clustering has a significant impact on the optimal hedging strategies.&lt;/p&gt;