Knowledge of consumers' willingness to pay (WTP) is a prerequisite to profitable price-setting. To gauge consumers' WTP, practitioners often rely on a direct single question approach in which consumers are asked to explicitly state their WTP for a product. Despite its popularity among practitioners, this approach has been found to suffer from hypothetical bias. In this paper, we propose a rigorous method that improves the accuracy of the direct single question approach. Specifically, we systematically assess the hypothetical biases associated with the direct single question approach and explore ways to de-bias it. Our results show that by using the de-biasing procedures we propose, we can generate a de-biased direct single question approach that is accu-rate enough to be useful for managerial decision-making. We validate this approach with two studies in this paper.

Over the last decade, dividends have become a standalone asset class instead of a mere side product of an equity investment. We introduce a framework based on polynomial jump-diffusions to jointly price the term structures of dividends and interest rates. Prices for dividend futures, bonds, and the dividend paying stock are given in closed form. We present an efficient moment based approximation method for option pricing. In a calibration exercise we show that a parsimonious model specification has a good fit with Euribor interest rate swaps and swaptions, Euro Stoxx 50 index dividend futures and dividend options, and Euro Stoxx 50 index options.

We introduce a prototype agent-based model of the macroeconomy, with a budgetary constraint at its core. The model is related to a class of constraint satisfaction problems, which has been thoroughly investigated in computer science. We identify three different regimes of our toy economy upon varying the amount of debt that each agent can accumulate before defaulting. In presence of a very loose constraint on debt, endogenous crises leading to waves of synchronized bankruptcies are present. In the opposite regime of very tight debt constraining, the bankruptcy rate is extremely high and the economy remains structure-less. In an intermediate regime, the economy is stable with very low bankruptcy rate and no aggregate-level crises. This third regime displays a rich phenomenology: the system spontaneously and dynamically self-organizes in a set of cheap and expensive goods (i.e. some kind of "speciation"), with switches triggered by random fluctuations and feedback loops. Our analysis confirms the central role that debt levels play in the stability of the economy.

Historically, the performance of the banking sector has been weak, characterized by weak asset quality, inadequate provisioning, and negative capitalization of state-owned banks. To overcome these problems, the initial phase of banking reform (1980-1990) focused on the promotion of private ownership and denationalization of nationalized commercial banks (SCBs). During the second phase of reform, Financial Sector Reform Project (FSRP) of World Bank was launched in 1990 with the focus on gradual deregulations of the interest rate structure, providing market-oriented incentives for priority sector lending and improvement in the debt recovery environment. Moreover, a large number of private commercial banks were granted licenses during the second phase of reforms. Bangladesh Bank adopted Basel-I norms in 1996 and Basel-II during 2010. Moreover, the Central Bank Strengthening Project initiated in 2003 focused on effective regulatory and supervisory system, particularly strengthening the legal framework of banking sector. This study evaluates how successfully the banking sector of Bangladesh has evolved over the past decades in light of financial reform measures undertaken to strengthen this sector.

Artificial neural networks (ANNs) have recently also been applied to solve partial differential equations (PDEs). In this work, the classical problem of pricing European and American financial options, based on the corresponding PDE formulations, is studied. Instead of using numerical techniques based on finite element or difference methods, we address the problem using ANNs in the context of unsupervised learning. As a result, the ANN learns the option values for all possible underlying stock values at future time points, based on the minimization of a suitable loss function. For the European option, we solve the linear Black-Scholes equation, whereas for the American option, we solve the linear complementarity problem formulation. Two-asset exotic option values are also computed, since ANNs enable the accurate valuation of high-dimensional options. The resulting errors of the ANN approach are assessed by comparing to the analytic option values or to numerical reference solutions (for American options, computed by finite elements).

We study neural networks as nonparametric estimation tools for the hedging of options. To this end, we design a network, named HedgeNet, that directly outputs a hedging strategy. This network is trained to minimise the hedging error instead of the pricing error. Applied to end-of-day and tick prices of S&P 500 and Euro Stoxx 50 options, the network is able to reduce the mean squared hedging error of the Black-Scholes benchmark significantly. We illustrate, however, that a similar benefit arises by simple linear regressions that incorporate the leverage effect. Finally, we show how a faulty training/test data split, possibly along with an additional 'tagging' of data, leads to a significant overestimation of the outperformance of neural networks.

Although wage inequality has evolved in advanced countries over recent decades, it is unknown the extent to which the evolution of wage inequality is attributed to observed factors such as capital and labor quantities or unobserved factors such as labor-augmenting technology. To examine this issue, we estimate aggregate production functions extended to allow for capital-skill complementarity and skill-biased technological change using cross-country panel data and the shift-share instrument. Our results indicate that most of the changes in the skill premium are attributed to observed factors including ICT equipment in some major countries.

We investigate the problem of learning undirected graphical models under Laplacian structural constraints from the point of view of financial market data. We show that Laplacian constraints have meaningful physical interpretations related to the market index factor and to the conditional correlations between stocks. Those interpretations lead to a set of guidelines that users should be aware of when estimating graphs in financial markets. In addition, we propose algorithms to learn undirected graphs that account for stylized facts and tasks intrinsic to financial data such as non-stationarity and stock clustering.

The purpose of the study is to propose a methodology for evaluation and ranking of risky investment projects.An investment certainty equivalence approach dual to the conventional separation of riskless and risky contributions based on cash flow certainty equivalence is introduced. Proposed ranking of investment projects is based on gauging them with the Omega measure, which is defined as the ratio of chances to obtain profit/return greater than some critical (minimal acceptable) profitability over the chances to obtain the profit/return less than the critical one.Detailed consideration of alternative riskless investment is presented. Various performance measures characterizing investment projects with a special focus on the role of reinvestment are discussed. Relation between the proposed methodology and the conventional approach based on utilization of risk-adjusted discount rate (RADR) is discussed. Findings are supported with an illustrative example.The methodology proposed can be used to rank projects of different nature, scale and lifespan. In contrast to the conventional RADR approach for investment project evaluation, in the proposed method a risk profile of a specific project is explicitly analyzed in terms of appropriate performance measure distribution. No ad-hoc assumption about suitable risk-premium is made.

In this paper, we consider a simple discrete-time optimal betting problem using the celebrated Kelly criterion, which calls for maximization of the expected logarithmic growth of wealth. While the classical Kelly betting problem can be solved via standard concave programming technique, an alternative but attractive approach is to invoke a Taylor-based approximation, which recasts the problem into quadratic programming and obtain the closed-form approximate solution. The focal point of this paper is to fill some voids in the existing results by providing some interesting properties when such an approximate solution is used. Specifically, the best achievable betting performance, positivity of expected cumulative gain or loss and its associated variance, expected growth property, variance of logarithmic growth, and results related to the so-called survivability (no bankruptcy) are provided.

We study the stochastic dynamics of a renewable resource harvested by a monopolist facing a downward sloping demand curve. We introduce a framework where harvesting sequentially affects the resource's potential to regenerate, resulting in an endogenous ecological regime shift. In a multi-period setting, the firm's objective is to find the profit-maximizing harvesting policy while simultaneously detecting in the quickest time possible the change in regime. Solving analytically, we show that a negative regime shift induces an aggressive extraction behaviour due to shorter detection periods, creating a sense of urgency, and higher markup in prices. Precautionary behaviour can result due to decreasing resource rent. We study the probability of extinction and show the emergence of catastrophe risk which can be both reversible and irreversible.

We introduce time-inhomogeneous stochastic volatility models, in which the volatility is described by a nonnegative function of a Volterra type continuous Gaussian process that may have extremely rough sample paths. The drift function and the volatility function are assumed to be time-dependent and locally $\omega$-continuous for some modulus of continuity $\omega$. The main results obtained in the paper are sample path and small-noise large deviation principles for the log-price process in a Gaussian model under very mild restrictions. We use these results to study the asymptotic behavior of binary up-and-in barrier options and binary call options.