Assessing the Least Squares Monte-Carlo Approach to American Option Valuation

A detailed analysis of the Least Squares Monte-Carlo (LSM) approach to American option valuation suggested in Longstaff and Schwartz (2001) is performed. We compare the specification of the cross-sectional regressions with Laguerre polynomials used in Longstaff and Schwartz (2001) with alternative specifications and show that some of these have numerically better properties. Furthermore, each of these specifications leads to a trade-off between the time used to calculate a price and the precision of that price. Comparing the method-specific trade-offs reveals that a modified specification using ordinary monomials is preferred over the specification based on Laguerre polynomials. Next, we generalize the pricing problem by considering options on multiple assets and we show that the LSM method can be implemented easily for dimensions as high as ten or more. Furthermore, we show that the LSM method is computationally more efficient than existing numerical methods. In particular, when the number of assets is high, say five, Finite Difference methods are infeasible, and we show that our modified LSM method is superior to the Binomial Model.     

Lock-in situations in supply chains: A social exchange theoretic study of sourcing arrangements in buyer–supplier relationships

Social exchange theory is used to gain a better understanding of the relationship between a buyer and a supplier that is characterized by lock-in situations. We begin by reviewing the theoretical foundations of social exchange theory. Next, we use an illustrative multinational business example from a Danish Business Group to demonstrate the complexities of the lock-in situation. Conjectures related to lock-in behaviors are initially developed and then examined by means of a game-theoretic model. The analysis provides a basis for the development of propositions which are examined employing a behavioral experiment. The results indicate that the optimal pricing strategy of the supplier is to lower the price with increasing demand and the optimal investment intensity of the buyer decreases with increasing demand. The paper concludes by presenting directions for future research.     

Yes We Can (Price Derivatives on Survivor Indices)

We propose a simulation approach to value derivatives when the underlying dynamics are estimated using the survivor indices directly. Our results show that survivor forward and swap premiums increase with maturity and with the market price of risk. Our results also confirm that taking the optionality into consideration is important from a pricing perspective, for both U.S. women and men. We compare our results to what is obtained using an alternative modeling approach in which a Lee–Carter model is used to indirectly model the survivor index. Compared to this method, our estimated premiums and prices are higher for all longevity products. Moreover, comparing American‐style with European‐style options we find that, although the early exercise option has value when using survivor indices directly, the relative value of the early exercise option is significantly less than when the Lee–Carter model is used to indirectly model the survivor index. It follows that the assumed mortality dynamics have important implications for the term structure of forward and swap premiums and for the effect that changes in the market price of risk has on them.     

Refining the least squares Monte Carlo method by imposing structure

The least squares Monte Carlo method of Longstaff and Schwartz has become a standard numerical method for option pricing with many potential risk factors. An important choice in the method is the number of regressors to use and using too few or too many regressors leads to biased results. This is so particularly when considering multiple risk factors or when simulation is computationally expensive and hence relatively few paths can be used. In this paper we show that by imposing structure in the regression problem we can improve the method by reducing the bias. This holds across different maturities, for different categories of moneyness and for different types of option payoffs and often leads to significantly increased efficiency.     

Impact of relational norms, supplier development and trust on supplier performance

Supplier relationship management and supplier development initiatives assume a fundamental role in enterprise supply chain management. An important aspect of effective supplier relationship management is the role of trust. This paper seeks to understand whether supplier relationship management or supplier development initiative should be emphasized as a firm strives to achieve superior supplier performance. The analysis and discussion draws upon sourcing strategy literature and is based on empirical survey-data of mid to upper level managers with responsibility for supply management initiatives in their respective organizations in Denmark and in the USA. It examines the interrelationships among “relational norms”, “trust”, “supplier development initiatives” and ensuing “supplier performance”. The data analysis shows that firms must emphasize relation and trust building activities before investing in supplier development initiative. Supplier perception audits must be routinely performed to gauge the level of trust and strength of relational norms.     

Measuring Longevity Risk: An Application to the Royal Canadian Mounted Police Pension Plan

An employer that sets up a defined benefit pension plan promises to periodically pay a certain sum to each participant starting at some future date and continuing until death. Although both the future beneficiary and the employer can be asked to finance the plan throughout the beneficiary's career, any shortcoming of funds in the future is often the employer's responsibility. It is therefore essential for the employer to be able to predict with a high degree of confidence the total amount that will be required to cover its future pension obligations. Applying mortality forecasting models to the case of the Royal Canadian Mounted Police pension plan, we illustrate the importance of mortality forecasting to value a pension fund's actuarial liabilities. As future survival rates are uncertain, pensioners may live longer than expected. We find that such longevity risk represents approximately 2.8 percent of the total liability ascribable to retired pensioners (as measured by the relative value at risk at the 95th percentile) and 2.5 percent of the total liabilities ascribable to current regular contributors. Longevity risk compounds the model risk associated with not knowing what is the true mortality model, and we estimate that model risk represents approximately 3.2 percent of total liabilities. The compounded longevity risk therefore represents almost 6 percent of the pension plan's total liabilities.     

The value of multivariate model sophistication: An application to pricing Dow Jones Industrial Average options

We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their specification of the conditional variance, conditional correlation, innovation distribution, and estimation approach. All of the models belong to the dynamic conditional correlation class, which is particularly suitable because it allows consistent estimations of the risk neutral dynamics with a manageable amount of computational effort for relatively large scale problems. It turns out that increasing the sophistication in the marginal variance processes (i.e., nonlinearity, asymmetry and component structure) leads to important gains in pricing accuracy. Enriching the model with more complex existing correlation specifications does not improve the performance significantly. Estimating the standard dynamic conditional correlation model by composite likelihood, in order to take into account potential biases in the parameter estimates, generates only slightly better results. To enhance this poor performance of correlation models, we propose a new model that allows for correlation spillovers without too many parameters. This model performs about 60% better than the existing correlation models we consider. Relaxing a Gaussian innovation for a Laplace innovation assumption improves the pricing in a more minor way. In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances.