Gurupdesh S. Pandher

Risk and Return in the U.S. Housing Market: A Cross-Sectional Asset Pricing Approach

This article carries out an asset-pricing analysis of the U.S. metropolitan housing market. We use ZIP code-level housing data to study the cross-sectional role of volatility, price level, stock market risk and idiosyncratic volatility in explaining housing returns. While the related literature tends to focus on the dynamic role of volatility and housing returns within submarkets over time, our risk-return analysis is cross-sectional and covers the national U.S. metropolitan housing market. The study provides a number of important findings on the asset-pricing features of the U.S. housing market. Specifically, we find (i) a positive relation between housing returns and volatility, with returns rising by 2.48% annually for a 10% rise in volatility, (ii) a positive but diminishing price effect on returns and (iii) that stock market risk is priced directionally in the housing market. Our results on the return-volatility-price relation are robust to (i) metropolitan statistical area clustering effects and (ii) differences in socioeconomic characteristics among submarkets related to income, employment rate, managerial employment, owner-occupied housing, gross rent and population density. Copyright 2006 American Real Estate and Urban Economics Association

Forecasting Multivariate Time Series with Linear Restrictions Using Constrained Structural State-Space Models

This paper presents a methodology for modelling and forecasting multivariate time series with linear restrictions using the constrained structural state-space framework. The model has natural applications to forecasting time series of macroeconomic/financial identities and accounts. The explicit modelling of the constraints ensures that model parameters dynamically satisfy the restrictions among items of the series, leading to more accurate and internally consistent forecasts. It is shown that the constrained model offers superior forecasting efficiency. A testable identification condition for state space models is also obtained and applied to establish the identifiability of the constrained model. The proposed methods are illustrated on Germanys quarterly monetary accounts data. Results show significant improvement in the predictive efficiency of forecast estimators for the monetary account with an overall efficiency gain of 25% over unconstrained modelling. Copyright

Valuation of Stock Option Grants Under Multiple Severance Risks

Grants of stock options as part of total employee compensation are now commonplace, but how such options should be valued and reported in firm financial statements is still an unsettled issue. The Black-Scholes (BS) model is the best-known and most widely accepted approach to option valuation as a benchmark for accounting and legal purposes, so it is seems like a natural place to begin. However, employee stock options (ESOs) are subject to several additional contingencies that are not in the BS equation, but significantly alter the possible payoffs. Specifically, severance with or without cause, including by death of the option holder, typically alters the payoff on an ESO. For example, termination with cause may also entail forfeiture of the employee’s options; termination without cause may simply advance the option’s maturity date and require the departing employee to exercise immediately, or not at all. In this article, Pandher presents a risk-neutral valuation approach for pricing ESOs under a realistic set of severance possibilities and examines the impact on valuation and on expected exercise dates. He shows that proper treatment of these additional features can make a substantial difference in the ESO value relative to Black-Scholes.

ESTIMATION OF EXCESS RETURNS FROM DERIVATIVE PRICES AND TESTING FOR RISK NEUTRAL PRICING

This paper develops an econometric framework for i) estimating excess returns of the security process from high frequency derivative prices, ii) testing for risk-neutral pricing and iii) measuring premiums outside the no-arbitrage pricing model. The estimator is constructed by applying quasi-likelihood and Feynman-Kac theory to the risk neutral contingent claims pricing model to generate the optimal orthogonality restriction. The strong consistency and asymptotic normality of the estimator is established in the context of a non-stationary underlying state process. These results further imply that the estimator is robust to distributional assumptions on the underlying asset process. The proposed approach is applicable to any arbitrary derivative security, does not require estimation of the risk-neutral probability measure and has application to spot-rate bond pricing models. A controlled diagnostic study based on generating the S&P500 index and calls verifies the ability of the estimators to correctly estimate security excess returns and test for risk neutral pricing. The estimator is invariant to call strikes and larger samples constructed by cycling over shorter maturity options can be used to reduce its variance.

Contextualising site factors for feasibility analysis

This paper explores the utility of site analysis as one factor in determining the feasibility of a proposed development in relation to organisational objectives. Feasibility analysis models frequently include site analysis as one factor in the broader study. However, site analysis for site planning and design is generally presented under the assumptions of a more advanced stage of planning than can be admitted by the constraints imposed by a feasibility analysis in the pre-start up phase of a proposed development. Site analysis in the context of feasibility analysis requires a model that emphasises its capacity for making a ‘go/no go’ decision on a proposed development programme based on uncertainty, limited resources and multiple stakeholder interests. From the multiple criteria decision-making literature a method is developed and applied to determine the fitness of a site for supporting a proposed tourism development. Moreover, the proposed site analysis matrix and coding scheme provides practitioners with parameters that can inform subsequent site planning actions. While application of the concept bears limitations in quantitative measurement and spatial representation, the results suggest the proposed method for site analysis is beneficial and useful in the context of feasibility analysis.

Financier Search and Boundaries of the Angel and VC Markets

This paper studies how critical entrepreneurial finance outcomes such as the investment return and equity division are shaped by venture characteristics, financier risk preferences, and competitive searching. Our analysis uses a double-hazard agency model in which financiers determine the equity division to maximize the expected utility of their investment return while entrepreneurs search for the best deal. Model results provide new theoretical insights on the venture funding cycle, the coexistence of angels/venture capitalists (VCs) with heterogeneous risk aversion, and risk separation in the entrepreneurial finance market. The model predicts that financiers with higher funding capacity and advisory capabilities (e.g., VC firms) will prefer to fund at later stages as their expected investment return rises with the venture’s initial value and financier productivity. Competitive searching by entrepreneurs enables financiers with a diverse set of risk preferences to coexist profitably by reducing the advantage (disadvantage) of lower (higher) risk aversion financiers and making investment returns more similar. Further, the model shows the emergence of a risk separation cutoff beyond which only angels/VCs with lower levels of risk aversion can profitably fund riskier ventures.

Entrepreneurial Finance: Equity Division & Expected Investment Returns with Risk-Averse Financiers and Search by Entrepreneurs

This paper studies the role of heterogeneity in financier risk preferences and competitive searching in venture finance deals. We analyze a double-hazard agency model in which entrepreneurs search for the best deal from financiers who maximize the expected utility of their investment return to determine equity shares that induce optimal efforts. Model results generate several novel insights on characteristics influencing expected investment returns and equity shares and on the coexistence of angels/VCs and the venture funding cycle. Without search, lower risk-aversion financiers have an advantage as their expected investment return is higher, the equity share given lower, and they can profitably fund higher risk projects. With search, an equilibrium emerges that reduces the advantage (disadvantage) of lower (higher) risk-aversion financiers. Since VCs can reasonably be associated with lower risk-aversion financiers relative to angels, the model predicts that angels/VCs with diverse risk preferences can coexist profitably in an industry that would otherwise favor VCs. The model further predicts that larger financiers (VCs) will prefer to finance at a later stage because their expected return rises with the venture’s initial value and financier productivity. Hence, the model theoretically justifies the pattern of funding activity by angels/VCs in the venture finance industry.This paper studies how critical entrepreneurial finance outcomes such as the investment return and equity division are shaped by venture characteristics, financier risk preferences and competitive searching. Our analysis uses a double-hazard agency model in which financiers determine the equity division to maximize the expected utility of their investment return while entrepreneurs search for the best deal. Model results provide new theoretical insights on the venture funding cycle, the coexistence of angels/VCs with heterogeneous risk aversion, and risk separation in the entrepreneurial finance market. The model predicts that financiers with higher funding capacity and advisory capabilities (e.g. VC firms) will prefer to fund at later stages as their expected investment return rises with the venture’s initial value and financier productivity. Competitive searching by entrepreneurs enables financiers with a diverse set of risk preferences to coexist profitably by reducing the advantage (disadvantage) of lower (higher) risk-aversion financiers and making investment returns more similar. Further, the model shows the emergence of a risk separation cutoff beyond which only angels/VCs with lower levels of risk-aversion can profitably fund riskier ventures.

Estimation of Generalized Diffusions from Option Prices

This paper develops option-based estimators of the diffusion using the Estimating Function approach. The resulting estimators have a generic structure that applies to a wide class of state-time separable diffusions found in option pricing models. Our methodology differs from the related literature in a number of ways. First, inferences regarding the diffusion are made jointly from option and asset prices and Estimating Function theory identifies the optimal estimating equation for the estimators. Second, the method is distribution-free in the sense that estimation of the diffusions transition density is not required. Lastly, the proposed option diffusion estimators are robust to distributional assumptions on the underlying asset prices (e.g. log-normality) as their asymptotic convergence and normality is established under conditional first and second moment assumptions. Monte-Carlo analysis verifies the accuracy and efficiency of the option diffusion estimators and resolves important sample design issues. Applications of the proposed option diffusion estimators to empirical option pricing, quantifying divergence between option and asset prices, and investment strategies are discussed.