The origin of market value has not been sufficiently explored in the social sciences. While there is a tendency among economists and sociologists to see value as imported to the market from external sources (e.g. culture, internal preferences), I argue that shifts in market value are often endogenous to the market setting. Perceptions of value, or collective beliefs that specific sets of assets will yield benefits for the owner, are most malleable when markets are unstable. Instability is caused by intense competition and rapid technological change, both of which upset firms' abilities to make consistent profits and retain their market position. Instability amplifies general uncertainty about the best ways to create value.Perceptions of value emerge in unstable markets as firms monitor and mimic their peers, who act as information proxies about the future value of assets. I look at acquisitions within the communications industries from 1997 to 2002 to assess this claim. I expect that firms acquire target assets in the same segments as their closest competitors and market leaders. Unstable market conditions amplify the extent to which firms use their peers to guide their acquisition choices. The collective flow of acquisitions caused by this mimicry creates perceptions of value that become reflected in concrete, standard measures of market value. Investors and other third-party observers use peer behavior as an interpretive frame for estimating value creation. They assume the collective acquisitions are social proof that value is being created and this is reflected in their investment behavior, which in turn drives up the stock prices of acquiring firms.Regression findings support these propositions; although there is weak evidence that market value gains from peer mimicry are long-term. Instead, I find that using peers to frame acquisition value tends to lead to initial overvaluation, which is subsequently corrected through a long-term value discount. I suggest that unstable market conditions tend to lead to speculative behavior and inefficient market pricing.

This research documents the societal worth of riparian resources in the Southwestern United States. Two case studies are developed for this inquiry, the first being Aravaipa Canyon Wilderness in Southern Arizona, an area containing one of the last perennial streams in the Sonoran Desert bioregion. A hiking use value per visitor-day is estimated via the Travel Cost Method at $25.06 and $17.31 (2003 dollars) respectively for two access sites. I hypothesize the value discrepancy to indicate a premium for remote recreation. These valuation results compare well with other published recreational use value estimates, though it is the only valuation study associated with instream recreation in the Sonoran Desert of which I am aware. Indeed the environmental valuation literature is thin for the desert region in any respect.The second case study values public restoration preferences for the Albuquerque reach of the Rio Grande in Central New Mexico. A Choice Experiment and Contingent Valuation are employed within an original survey instrument to estimate human values for various restoration strategies planned for the region. Through focus groups and stakeholder interactions four restoration attributes were defined: vegetation density; tree type; fish and wildlife population; and natural river processes. Quantified values for Albuquerque area households were estimated for each restoration attribute level of change, allowing construction of total benefits anticipated for various restoration scenarios considered for the region. This research is at the vanguard of quantifying human benefit for saltcedar control, and this particular restoration characteristic was the most highly valued of all, at $59.03 per household per year. Full restoration was valued at $156.60 per household per year. These results have meaning beyond the study area since river restoration efforts are increasing across the Western US, with many focusing on controlling saltcedar, an exotic invasive plant.The final phase of this research integrates riparian valuation concepts within a dynamic simulation framework to guide systems-level riparian management. Control variables are combined with known valuation pathways to predict riparian investment funding optimal in benefit-cost ratio. The model is built for the Middle Rio Grande in Albuquerque, however it was designed for easy adaptation to other Southwestern riparian areas. A detailed forest module is included, through which seven defined forest stocks may be managed through thinning, clearing, and revegetation. River management may occur through environmental river flow releases, reconstructing stream-overbank connections, and wetland construction. Recreational amenities may be improved through the four infrastructure categories of trails, toilet facilities, picnic areas, and parking areas. Benefits and costs are estimated through original research and region benefit transfer, and tracked for different investment scenarios to predict the highest-return strategies over a 100 year planning horizon. A sensitivity analysis is used to suggest areas of future research.

This study focuses on valuation of natural resources and particularly valuation of the quality of a natural resource. The aim is to value an improvement of water quality, measured as sight depth, in the bay Himmerfjärden in the Stockholm archipelago. It is desirable to attain a value of a one metre sight depth improvement in Himmerfjärden and to analyse how a sight depth improvement affects the demand for travelling to Himmerfjärden. A condtional logit model is applied to obtain these values from survey data. The benefits of a one metre sight depth improvement in Himmerfjärden, will yield 162 260-1 599 420 SEK per year, adjusted to the price level of 2007. The estimated value depends on how travel time is treated and whether a commute variable is included or not. Furthermore will a one per cent sight depth improvement in Himmerfjärden increase the demand for travelling to Himmerfjärden with approximately 0.13-0.18 trips on a given choice occasion. These results indicate that there are values attained to the quality of a natural resource.

The orgin of brand value creation is consumer attitudes towards the brand, and the result of owning powerfull brand is the increased shareholders value. In vast majority of cases these two aspects are analysed sepatately, however, we see a need of looking at that as undivided. This is due to the fact that brand value does not depend only on the consumer’s behaviour. It has much to do with a market situation and the owners’ behaviour, therefore, it is crucial to conceptualize brand value creation process and develop adequate quantitative methods of measuring brand value.

The objective of research is to build a business valuation model based on the analysis of business value drivers, which would incorporate the classification of business value drivers and the evaluation (determination) of their impact on business value into the process of business valuation, and would enable to provide the final dimension of business value.

This thesis studies the impacts of credit risk, or the risk of default, on the pricing of fixed income securities. It consists of three essays. The first essay extends the classical corporate debt pricing model in Merton (1974) to incorporate stochastic volatility (SV) in the underlying firm asset value and derive a closed-form solution for the price of corporate bond. Simulation results show that the SV specification for firm asset value greatly increases the resulting credit spread levels. Therefore, the SV model addresses one major deficiency of the Merton-type models: namely, at short maturities the Merton model is unable to generate credit spreads high enough to be compatible with those observed in the market. In the second essay, we develop a two-factor affine model for the credit spreads on corporate bonds. The first factor can be interpreted as the level of the spread, and the second factor is the volatility of the spread. Our empirical results show that the model is successful at fitting actual corporate bond credit spreads. In addition, key properties of actual credit spreads are better captured by the model. Finally, the third essay proposes a model of interest rate swap spreads. The model accommodates both the default risk inherent in swap contracts and the liquidity difference between the swap and Treasury markets. The default risk and liquidity components of swap spreads are found to behave very differently: first, the default risk component is positively related to the riskless interest rate, whereas the liquidity component is negatively correlated with the riskless interest rate; second, although default risk accounts for the largest share of the levels of swap spreads, the liquidity component is much more volatile; and finally, while the default risk component has been historically positive, the liquidity component was negative for much of the 1990s and has become positive since the financial market turmoil in 1998.

This dissertation is in the form of three essays on the topic of component and long memory GARCH models. The unifying feature of the thesis is the focus on investigating European index option evaluation using these models. / The first essay presents a new model for the valuation of European options. In this model, the volatility of returns consists of two components. One of these components is a long-run component that can be modeled as fully persistent. The other component is short-run and has zero mean. The model can be viewed as an affine version of Engle and Lee (1999), allowing for easy valuation of European options. The model substantially outperforms a benchmark single-component volatility model that is well established in the literature. It also fits options better than a model that combines conditional heteroskedasticity and Poisson normal jumps. While the improvement in the component model's performance is partly due to its improved ability to capture the structure of the smirk and the path of spot volatility, its most distinctive feature is its ability to model the term structure. This feature enables the component model to jointly model long-maturity and short-maturity options. / The second essay derives two new GARCH variance component models with non-normal innovations. One of these models has an affine structure and leads to a closed-form option valuation formula. The other model has a non-affine structure and hence, option valuation is carried out using Monte Carlo simulation. We provide an empirical comparison of these two new component models and the respective special cases with normal innovations. We also compare the four component models against GARCH(1,1) models which they nest. All eight models are estimated using MLE on S&P500 returns. The likelihood criterion strongly favors the component models as well as non-normal innovations. The properties of the non-affine models differ significantly from those of the affine models. Evaluating the performance of component variance specifications for option valuation using parameter estimates from returns data also provides strong support for component models. However, support for non-normal innovations and non-affine structure is less convincing for option valuation. / The third essay aims to investigate the impact of long memory in volatility on European option valuation. We mainly compare two groups of GARCH models that allow for long memory in volatility. They are the component Heston-Nandi GARCH model developed in the first essay, in which the volatility of returns consists of a long-run and a short-run component, and a fractionally integrated Heston-Nandi GARCH (FIHNGARCH) model based on Bollerslev and Mikkelsen (1999). We investigate the performance of the models using S&P500 index returns and cross-sections of European options data. The component GARCH model slightly outperforms the FIGARCH in fitting return data but significantly dominates the FIHNGARCH in capturing option prices. The findings are mainly due to the shorter memory of the FIHNGARCH model, which may be attributed to an artificially prolonged leverage effect that results from fractional integration and the limitations of the affine structure.

Most recent empirical option valuation studies build on the affine square root (SQR) stochastic volatility model. The SQR model is a convenient choice, because it yields closed-form solutions for option prices. However, relatively little is known about the empirical shortcomings of this model. In the first essay, we investigate alternatives to the SQR model, by comparing its empirical performance with that of five different but equally parsimonious stochastic volatility models. We provide empirical evidence from three different sources. We first use realized volatilities to assess the properties of the SQR model and to guide us in the search for alternative specifications. We then estimate the models using maximum likelihood on a long sample of S& P500 returns. Finally, we employ nonlinear least squares on a time series of cross sections of option data. In the estimations on returns and options data, we use the particle filtering technique to retrieve the spot volatility path. The three sources of data we employ all point to the same conclusion: the SQR model is misspecified. Overall, the best of alternative volatility specifications is a model we refer to as the VAR model, which is of the GARCH diffusion type. / In the second essay, we estimate the Constant Elasticity of Variance (CEV) model in order to study the level of nonlinearity in the volatility dynamic. We also estimate a CEV process combined with a jump process (CEVJ) and analyze the effects of the jump component on the nonlinearity coefficient. Estimation is performed using the particle filtering technique on a long series of S&P500 returns and on options data. We find that both returns data and returns-and-options data favor nonlinear specifications for the volatility dynamic, suggesting that the extensive use of linear models is not supported empirically. We also find that the inclusion of jumps does not affect the level of nonlinearity and does not improve the CEV model fit. / The third essay provides an empirical comparison of two classes of option valuation models: continuous-time models and discrete-time models. The literature provides some theoretical limit results for these types of dynamics, and researchers have used these limit results to argue that the performance of certain discrete-time and continuous-time models ought to be very similar. This interpretation is somewhat contentious, because a given discrete-time model can have several continuous-time limits, and a given continuous-time model can be the limit for more than one discrete-time model. Therefore, it is imperative to investigate whether there exist similarities between these specifications from an empirical perspective. Using data on S&P500 returns and call options, we find that the discrete-time models investigated in this paper have the same performance in fitting the data as selected continuous-time models both in and out-of-sample.

The objective of this study was to determine what impact, if any, interest rate subsidies have on the price of farmland in Canada. The basic capitalization model is used as a starting point for the development of several models. These econometric models are then estimated, using data from four provinces: Quebec, New Brunswick, Manitoba and Saskatchewan. The time period studied is 1972 to 1991. The findings indicate that interest subsidies do in fact affect land values, however the effect is relatively small. There are large differences between provinces in terms of subsidy amount and consequently in terms of the effect of the subsidy programs on the value of land in each province.

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