Valuation (logic)

About: Valuation (logic) is a research topic. Over the lifetime, 1943 publications have been published within this topic receiving 22449 citations.

Bond pricing and the term structure of interest rates: a new methodology for contingent claims valuation

Abstract: This paper presents a unifying theory for valuing contingent claims under a stochastic term structure of interest rates. The methodology, based on the equivalent martingale measure technique, takes as given an initial forward rate curve and a family of potential stochastic processes for its subsequent movements. A no arbitrage condition restricts this family of processes yielding valuation formulae for interest rate sensitive contingent claims which do not explicitly depend on the market prices of risk. Examples are provided to illustrate the key results.

A meta-analysis of hypothetical bias in stated preference valuation

Abstract: Individuals are widely believed to overstate their economic valuation of a good by a factor of two or three. This paper reports the results of a meta-analysis of hypothetical bias in 28 stated preference valuation studies that report monetary willingness-to-pay and used the same mechanism for eliciting both hypothetical and actual values. The papers generated 83 observations with a median ratio of hypothetical to actual value of only 1.35, and the distribution has severe positive skewness. We find that a choice-based elicitation mechanism is important in reducing bias. We provide some evidence that the use of student subjects may be a source of bias, but since this variable is highly correlated with group experimental settings, firm conclusions cannot be drawn. There is some weak evidence that bias increases when public goods are being valued, and that some calibration methods may be effective at reducing bias. However, results are quite sensitive to model specification, which will remain a problem until a comprehensive theory of hypothetical bias is developed.

A Closed-Form GARCH Option Valuation Model

Abstract: This paper develops a closed-form option valuation formula for a spot asset whose variance follows a GARCH(p, q) process that can be correlated with the returns of the spot asset. It provides the first readily computed option formula for a random volatility model that can be estimated and implemented solely on the basis of observables. The single lag version of this model contains Heston's (1993) stochastic volatility model as a continuous-time limit. Empirical analysis on S&P500 index options shows that the out-of-sample valuation errors from the single lag version of the GARCH model are substantially lower than the ad hoc Black-Scholes model of Dumas, Fleming and Whaley (1998) that uses a separate implied volatility for each option to fit to the smirk/smile in implied volatilities. The GARCH model remains superior even though the parameters of the GARCH model are held constant and volatility is filtered from the history of asset prices while the ad hoc Black-Scholes model is updated every period. The improvement is largely due to the ability of the GARCH model to simultaneously capture the correlation of volatility, with spot returns and the path dependence in volatility. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

Imaging valuation models in human choice

Abstract: To make a decision, a system must assign value to each of its available choices. In the human brain, one approach to studying valuation has used rewarding stimuli to map out brain responses by varying the dimension or importance of the rewards. However, theoretical models have taught us that value computations are complex, and so reward probes alone can give only partial information about neural responses related to valuation. In recent years, computationally principled models of value learning have been used in conjunction with noninvasive neuroimaging to tease out neural valuation responses related to reward-learning and decision-making. We restrict our review to the role of these models in a new generation of experiments that seeks to build on a now-large body of diverse reward-related brain responses. We show that the models and the measurements based on them point the way forward in two important directions: the valuation of time and the valuation of fictive experience.