A quasi-analytical interpolation method for pricing American options under general multi-dimensional diffusion processes

We present a quasi-analytical method for pricing multi-dimensional American options based on interpolating two arbitrage bounds, along the lines of Johnson in J Financ Quant Anal 18(1):141–148 (1983). Our method allows for the close examination of the interpolation parameter on a rigorous theoretical footing instead of empirical regression. The method can be adapted to general diffusion processes as long as quick and accurate pricing methods exist for the corresponding European and perpetual American options. The American option price is shown to be approximately equal to an interpolation of two European option prices with the interpolation weight proportional to a perpetual American option. In the Black-Scholes model, our method achieves the same efficiency as the quadratic approximation of Barone-Adesi and Whaley in J Financ 42:301–320 (1987), with our method being generally more accurate for out-of-the-money and long-maturity options. When applied to Heston’s stochastic volatility model, our method is shown to be extremely efficient and fairly accurate.     

A fast Fourier transform technique for pricing American options under stochastic volatility

This paper develops a non-finite-difference-based method of American option pricing under stochastic volatility by extending the Geske-Johnson compound option scheme. The characteristic function of the underlying state vector is inverted to obtain the vector’s density using a kernel-smoothed fast Fourier transform technique. The method produces option values that are closely in line with the values obtained by finite-difference schemes. It also performs well in an empirical application with traded S&P 100 index options. The method is especially well suited to price a set of options with different strikes on the same underlying asset, which is a task often encountered by practitioners.     

Optimal Hedging of Basket Barrier Options with Additive Models and Its Application to Equity Value Separation Problem

At the heart of optimal hedging with additive models in Yamada (Recent advances in financial engineering: proceedings of the KIER-TMU international workshop on financial engineering, World Scientific, pp 225–245, 2010; Proceedings of the 2011 American control conference, pp 3856–3861, 2011; Asia-Pac Financ Mark 19(2):149–179, 2012) is to replicate the payoff of European basket options using separate options as close as possible. In this paper, we extend their technique for the case of path-dependent barrier options, where the mean square error of the payoffs between the basket barrier option and the sum of options on the individual assets is minimized over any smooth payoff functions. To this end, we propose to represent the underlying assets using the Brownian bride decomposition and show that computations involving conditional expectations of basket barrier options boil down to those of unconditional expectations. This procedure enables us to provide an algorithm to compute the necessary and sufficient condition for the optimal hedging problem based on the Monte Carlo method. Then, we consider to apply our methodology to the Black–Cox type first passage time structural model, where a defaultable company possesses/runs multiple assets/projects and the default may occur the first time the asset value hits a certain lower threshold before the maturity. We formulate the equity value separation problem using additive models, in which individual equity values are introduced so that their sum approximates the total equity value as close as possible. It is also shown that any portion of total equity value may be assigned as an initial value of each individual equity when using the optimal smooth functions. Finally, we examine the contributions of individual equity values to default or survival by applying a certain normalization for conditional expectations via numerical experiments to illustrate our proposed methodology.     

Pricing Black–Scholes options with correlated interest rate risk and credit risk: an extension

This article provides a closed-form valuation formula for the Black–Scholes options subject to interest rate risk and credit risk. Not only does our model allow for the possible default of the option issuer prior to the option's maturity, but also considers the correlations among the option issuer's total assets, the underlying stock, and the default-free zero coupon bond. We further tailor-make a specific credit-linked option for hedging the default risk of the option issuer. The numerical results show that the default risk of the option issuer significantly reduces the option values, and the vulnerable option values may be remarkably overestimated in the case where the default can occur only at the maturity of the option.     

An Asymptotic Expansion Approach to Currency Options with a Market Model of Interest Rates under Stochastic Volatility Processes of Spot Exchange Rates

This paper proposes an asymptotic expansion scheme of currency options with a libor market model of interest rates and stochastic volatility models of spot exchange rates. In particular, we derive closed-form approximation formulas for the density functions of the underlying assets and for pricing currency options based on a third order asymptotic expansion scheme; we do not model a foreign exchange rate’s variance such as in Heston [(1993) The Review of Financial studies, 6, 327–343], but its volatility that follows a general time-inhomogeneous Markovian process. Further, the correlations among all the factors such as domestic and foreign interest rates, a spot foreign exchange rate and its volatility, are allowed. Finally, numerical examples are provided and the pricing formula are applied to the calibration of volatility surfaces in the JPY/USD option market.     

Efficient estimation of lower and upper bounds for pricing higher-dimensional American arithmetic average options by approximating their payoff functions

In this paper, we develop an efficient payoff function approximation approach to estimating lower and upper bounds for pricing American arithmetic average options with a large number of underlying assets. The crucial step in the approach is to find a geometric mean which is more tractable than and highly correlated with a given arithmetic mean. Then the optimal exercise strategy for the resultant American geometric average option is used to obtain a low-biased estimator for the corresponding American arithmetic average option. This method is particularly efficient for asset prices modeled by jump-diffusion processes with deterministic volatilities because the geometric mean is always a one-dimensional Markov process regardless of the number of underlying assets and thus is free from the curse of dimensionality. Another appealing feature of our method is that it provides an extremely efficient way to obtain tight upper bounds with no nested simulation involved as opposed to some existing duality approaches. Various numerical examples with up to 50 underlying stocks suggest that our algorithm is able to produce computationally efficient results.     

Pricing Exotic Options and American Options: A Multidimensional Asymptotic Expansion Approach

This paper introduces a new method for pricing exotic options whose payoff functions depend on several stochastic indices and American options in multidimensional models. This method is based on two ideas. One is an application of the asymptotic expansion method for the law of a multidimensional diffusion process. The other is the combination of the asymptotic expansion method and the method called backward induction. The author applies the method to the problems of pricing call options on the maximum of two assets in the CEV model, average strike options in the Black–Scholes model and American options in the Heston model. Numerical examples show practical effectiveness of the proposed method.     

Analytical pricing of American options

By using the homotopy analysis method, we derive a new explicit approximate formula for the optimal exercise boundary of American options on an underlying asset with dividend yields. Compared with highly accurate numerical values, the new formula is shown to be valid for up to 2?years of time to maturity, which is ten times longer than existing explicit approximate formulas. The option price errors computed with our formula are within a few cents for American options that have moneyness (the ratio between stock and strike prices) from 0.8 to 1.2, strike prices of 100 dollars and 2?years to maturity.     

Return predictability and shareholders’ real options

This study re-interprets the properties of the residual income model by highlighting the shareholders’ abandonment (liquidation or adaptation) option. We estimate the value of this real option as an explicit component of abnormal earnings in the residual income model and test the improvement in valuation after incorporating it into the model. Relative to the traditional specification of the residual income model, this real options model has a stronger predictive power for future abnormal stock returns. We also find that the superior return predictability of the real options model is pronounced in the set of firms with a high probability of exercising liquidation options (for example, those with low profitability, low growth opportunities, high underlying asset volatility, and low intangible assets), which is consistent with the importance of shareholders’ abandonment option in equity valuation. The results are robust to extensive sensitivity checks.     

Re-examining the investment-uncertainty relationship in a real options model

The main purpose of this paper is to re-examine the investment-uncertainty relationship in a real options model, and demonstrates that the Sarkar (J Econ Dyn Control 24:219–225, 2000) model is a special case of our model. This paper uses a general dynamic process, which incorporates mean reversion and jumps in a firm’s project earnings. We further derive a quasi-analytical form solution for the critical investment value and investment probability of a firm’s projects. From the simulation results, we find that an increase in uncertainty can always lead to an increase in the probability of investment, and thus has a positive impact on investment. These results, which differ from the findings of Sarkar (J Econ Dyn Control 24:219–225, 2000), could be explained by the mean-reversion and jump effects on a firm’s earnings.     

Stickiness of Rental Rates and Developers’ Option Exercise Strategies

In this study we incorporate sticky rents into a real options model to rationalize the widely documented overbuilding puzzle in real estate markets. Given the assumption that developers’ objective function is to maximize total revenue by selecting an optimal occupancy level, our model provides a better explanation of the phenomena we observed in the real world than the traditional market-clearance based real options models. We also show that developers’ exercise strategies can be affected by the size and the type of property markets. In other words, developers’ exercise strategies could differ among markets and under different conditions. Submitted to Cambridge—Maastricht 2005 Symposium.     

Analytical approximations for the critical stock prices of American options: a performance comparison

Many efficient and accurate analytical methods for pricing American options now exist. However, while they can produce accurate option prices, they often do not give accurate critical stock prices. In this paper, we propose two new analytical approximations for American options based on the quadratic approximation. We compare our methods with existing analytical methods including the quadratic approximations in Barone-Adesi and Whaley (J Finance 42:301–320, 1987) and Barone-Adesi and Elliott (Stoch Anal Appl 9(2):115–131, 1991), the lower bound approximation in Broadie and Detemple (Rev Financial Stud 9:1211–1250, 1996), the tangent approximation in Bunch and Johnson (J Finance 55(5):2333–2356, 2000), the Laplace inversion method in Zhu (Int J Theor Appl Finance 9(7):1141–1177, 2006b), and the interpolation method in Li (Working paper, 2008). Both of our methods give much more accurate critical stock prices than all the existing methods above.     

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.     

Pricing vulnerable options with correlated jump-diffusion processes depending on various states of the economy

In this paper, we use a Markov-modulated regime switching approach to model various states of the economy, and study the pricing of vulnerable European options when the dynamics of the underlying asset value and the asset value of the counterparty follow two correlated jump-diffusion processes under regime switching. The correlation is modelled by both the diffusion parts and the pure jump parts which describe the uncertainty of the value of the risky assets. We develop a method to determine an equivalent martingale measure and a parsimonious representation of the risk-neutral density is provided. Based on this, we derive an analytical pricing formula for vulnerable options via two-dimensional Laplace transforms, and implement the formula through numerical Laplace inversion.     

Homeownership as a Constraint on Asset Allocation

Personal preferences and financial incentives make homeownership desirable for most families. Once a family purchases a home they find it impractical (costly) to frequently change their ownership of residential real estate. Thus, by deciding how much home to buy, a family constrains their ability to adjust their asset allocation between residential real estate and other assets. To analyze the impact of this constraint on consumption, welfare, and post-retirement wealth, we first investigate an individual’s optimal asset allocation decisions when they are subject to a “homeownership constraint.” Next, we perform a “thought experiment” where we assume the existence of a market where a homeowner can sell, without cost, a fractional interest in their home. Now the housing choice decision does not constrain the individual’s asset allocations. By comparing these two cases, we estimate the differences in post-retirement wealth and the welfare gains potentially realizable if asset allocations were not subject to a homeownership constraint. For realistic parameter values, we find that the homeowner would require a substantial increase in total net worth to achieve the same level of utility as would be achievable if the choice of a home could be separated from the asset allocation decision. The robustness of the analysis is evaluated with respect to the model’s parameters and initial state variables. We find that changes in the values of the constraint (i.e., the value of the home) and the expected real rate of home value appreciation are the only state variables or parameter that is associated with a large change in asset allocation and/or the burden imposed by the housing constraint. This finding suggests the importance of a detailed examination of the impact of inter-regional differences in home prices and expected rates of appreciation on asset allocation and post-retirement wealth.     

Momentum in Residential Real Estate

This paper examines whether there is return momentum in residential real estate in the U.S. Case and Shiller (American economic review 79(1):128–137, 1989) document evidence of positive return correlation in four U.S. cities. Similar to Jegadeesh and Titman’s (Journal of finance 56:699–720, 1993) stock market momentum paper, we construct long-short zero cost investment portfolios from more than 380 metropolitan areas based on their lagged returns. Our results show that momentum of returns in the U.S. residential housing is statistically significant and economically meaningful during our 1983 to 2008 sample period. On average, zero cost investment portfolios that buy past winning housing markets and short sell past losing markets earn up to 8.92% annually. Our results are robust to different sub-periods and more pronounced in the Northeast and West regions. While zero cost portfolios of residential real estate indices is not a tradable strategy, the implications of our results can be useful for builders, potential home owners, mortgage originators and traders of real estate options.     

Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models

In this paper we propose two efficient techniques which allow one to compute the price of American basket options. In particular, we consider a basket of assets that follow a multi-dimensional Black–Scholes dynamics. The proposed techniques, called GPR Tree (GRP-Tree) and GPR Exact Integration (GPR-EI), are both based on Machine Learning, exploited together with binomial trees or with a closed form formula for integration. Moreover, these two methods solve the backward dynamic programing problem considering a Bermudan approximation of the American option. On the exercise dates, the value of the option is first computed as the maximum between the exercise value and the continuation value and then approximated by means of Gaussian Process Regression. The two methods mainly differ in the approach used to compute the continuation value: a single step of the binomial tree or integration according to the probability density of the process. Numerical results show that these two methods are accurate and reliable in handling American options on very large baskets of assets. Moreover we also consider the rough Bergomi model, which provides stochastic volatility with memory. Despite that this model is only bidimensional, the whole history of the process impacts on the price, and how to handle all this information is not obvious at all. To this aim, we present how to adapt the GPR-Tree and GPR-EI methods and we focus on pricing American options in this non-Markovian framework.     

An alternative approach to the valuation of American options and applications

In this paper we examine the structure of American option valuation problems and derive the analytic valuation formulas under general underlying security price processes by an alternative but intuitive method. For alternative diffusion processes, we derive closed-form analytic valuation formulas and analyze the implications of asset price dynamics on the early exercise premiums of American options. In this regard, we introduce useful and interesting diffusion processes into American option-pricing literature, thus providing a wide range of choices of pricing models for various American-type derivative assets. This work offers a useful analytic framework for empirical testing and practical applications such as the valuation of corporate securities and examining the impact of options trading on market micro-structure.     

Exchange option pricing under stochastic volatility: a correlation expansion

Efficient valuation of exchange options with random volatilities while challenging at analytical level, has strong practical implications: in this paper we present a new approach to the problem which allows for extensions of previous known results. We undertake a route based on a multi-asset generalization of a methodology developed in Antonelli and Scarlatti (Finan Stoch 13:269–303, 2009) to handle simple European one-asset derivatives with volatility paths described by Ito’s diffusive equations. Our method seems to adapt rather smoothly to the evaluation of Exchange options involving correlations among all the financial quantities that specify the model and it is based on expanding and approximating the theoretical evaluation formula with respect to correlation parameters. It applies to a whole range of models and does not require any particular distributional property. In order to test the quality of our approximation numerical simulations are provided in the last part of the paper.