Optimal dealer pricing under transactions and return uncertainty

The paper examines the optimal behavior of a single dealer who is faced with a stochastic demand to trade (modeled by a continuous time Poisson jump process) and facing return risk on his stock and on the rest of his portfolio (modeled by diffusion processes). Using stochastic dynamic programming, we derive the optimal bid and ask prices that maximize the dealer's expected utility of terminal wealth as a function of the state in which he finds himself. The relationship of the bid and ask prices to inventory of the dealer, instantaneous variance of return, stochastic arrival of transactions and other variables is examined.     

Hedge fund pricing and model uncertainty

This article uses Bayesian model averaging to study model uncertainty in hedge fund pricing. We show how to incorporate heteroscedasticity, thus, we develop a framework that jointly accounts for model uncertainty and heteroscedasticity. Relevant risk factors are identified and compared with those selected through standard model selection techniques. The analysis reveals that a model selection strategy that accounts for model uncertainty in hedge fund pricing regressions can be superior in estimation/inference. We explore potential impacts of our approach by analysing individual funds and show that they can be economically important.     

Model uncertainty and the pricing of American options

The virtue of an American option is that it can be exercised at any time. This right is particularly valuable when there is model uncertainty. Yet almost all the extensive literature on American options assumes away model uncertainty. This paper quantifies the potential value of this flexibility by identifying the supremum on the price of an American option when we do not impose a model, but rather consider the class of all models which are consistent with a family of European call prices. The bound is enforced by a hedging strategy involving these call options which is robust to model error.     

Admissible uncertainty in the intertemporal asset pricing model

We embed the Sharpe-Lintner, two-parameter asset pricing theory in an intertemporal general equilibrium model. The investment opportunity set changes stochastically over time; in general the short-term and long-term interest rates and the distribution of the rate of return of the market portfolio are non-stationary. This non-stationarity, which is admissible in the Sharpe-Lintner model, has two implications: First, it may bias econometric methods which fail to explicitly take into account the non-stationarity. Second, the sequential application of the Sharpe-Lintner model in the discounting of stochastic cash flows becomes computationally complex and of little practical use.     

Political power,local policy uncertainty and IPO pricing

Greater partisan alignment among lawmakers enhances their ability to respond rapidly to adverse shocks, but it also undermines the quality of checks and balances and encourages excessive governmental intervention in local areas aligned with the ruling party. We investigate how this form of local policy risk affects IPO underpricing. One standard-deviation increase in political alignment between local politicians and the federal government translates into an extra $1.58 million being left on the table, which corresponds to 5.39% of the average valuation discount. This effect is concentrated in firms that are vulnerable to legislative interventions and has important long-term implications. Our robustness analysis also shows that our baseline results are not sensitive to the inclusion/exclusion of influential states and years with high IPO activity.     

Environmental uncertainty and the market pricing of earnings smoothness

Environmental uncertainty induces variability in an organization's reported earnings, and accentuates the information asymmetry between its managers and outside stakeholders. Managers operating in an environment of high uncertainty, therefore, have an incentive to reduce such variability by smoothing income numbers. We investigate the stock market response to earnings smoothness for firms operating in an environment of high uncertainty. We measure income smoothing by the negative correlation of a firm's change in discretionary accruals with its change in pre-managed earnings as per Tucker and Zarowin (2006). Using future earnings response coefficient (FERC) methodology to measure the informativeness of smoothed earnings, and two measures of environmental uncertainty, this paper documents that current stock price incorporates more information about future earnings for firms operating in high uncertain environments, thus supporting the informational value view of income smoothing.     

Strategy under uncertainty

At the heart of the traditional approach to strategy lies the assumption that by applying a set of powerful analytic tools, executives can predict the future of any business accurately enough to allow them to choose a clear strategic direction. But what happens when the environment is so uncertain that no amount of analysis will allow us to predict the future? What makes for a good strategy in highly uncertain business environments? The authors, consultants at McKinsey & Company, argue that uncertainty requires a new way of thinking about strategy. All too often, they say, executives take a binary view: either they underestimate uncertainty to come up with the forecasts required by their companies' planning or capital-budging processes, or they overestimate it, abandon all analysis, and go with their gut instinct. The authors outline a new approach that begins by making a crucial distinction among four discrete levels of uncertainty that any company might face. They then explain how a set of generic strategies--shaping the market, adapting to it, or reserving the right to play at a later time--can be used in each of the four levels. And they illustrate how these strategies can be implemented through a combination of three basic types of actions: big bets, options, and no-regrets moves. The framework can help managers determine which analytic tools can inform decision making under uncertainty--and which cannot. At a broader level, it offers executives a discipline for thinking rigorously and systematically about uncertainty and its implications for strategy.     

Growth uncertainty,generalized disappointment aversion and production-based asset pricing

We study a production economy with regime switching in the conditional mean and volatility of productivity growth. The representative agent has generalized disappointment aversion (GDA) preferences. We show that volatility risk in productivity growth carries a positive and sizable risk premium in levered equity. Our model can endogenously generate long-run risks in the volatility of consumption growth observed in the data. We show that introducing leverage with a procyclical dividend process consistent with the data is critical for the GDA preferences to have a large impact on equity returns.     

Option pricing under regime switching

Abstract

This paper develops a family of option pricing models when the underlying stock price dynamic is modelled by a regime switching process in which prices remain in one volatility regime for a random amount of time before switching over into a new regime. Our family includes the regime switching models of Hamilton (Hamilton J 1989 Econometrica 57 357–84), in which volatility influences returns. In addition, our models allow for feedback effects from returns to volatilities. Our family also includes GARCH option models as a special limiting case. Our models are more general than GARCH models in that our variance updating schemes do not only depend on levels of volatility and asset innovations, but also allow for a second factor that is orthogonal to asset innovations. The underlying processes in our family capture the asymmetric response of volatility to good and bad news and thus permit negative (or positive) correlation between returns and volatility. We provide the theory for pricing options under such processes, present an analytical solution for the special case where returns provide no feedback to volatility levels, and develop an efficient algorithm for the computation of American option prices for the general case.     

Option pricing under a stressed-beta model

Empirical studies have concluded that stochastic volatility is an important component of option prices. We introduce a regime-switching mechanism into a continuous-time Capital Asset Pricing Model which naturally induces stochastic volatility in the asset price. Under this Stressed-Beta model, the mechanism is relatively simple: the slope coefficient—which measures asset returns relative to market returns—switches between two values, depending on the market being above or below a given level. After specifying the model, we use it to price European options on the asset. Interestingly, these option prices are given explicitly as integrals with respect to known densities. We find that the model is able to produce a volatility skew, which is a prominent feature in option markets. This opens the possibility of forward-looking calibration of the slope coefficients, using option data, as illustrated in the paper.     

Cryptocurrency returns under empirical asset pricing

This study examines the predictability of cryptocurrency returns based on investors' risk premia. Prior studies that have examined the predictability of cryptocurrencies using various economic risk factors have reported mixed results. Our out-of-sample evidence identifies the existence of a significant return predictability of cryptocurrencies based on the cryptocurrency market risk premium. Consistent with capital asset pricing theory (CAPM), our results show that investors often require higher positive returns before taking on any additional risks, particularly in terms of riskier assets like cryptocurrencies. Tests involving the CAPM model demonstrates that the three largest cryptocurrencies have significant exposures to the proposed market factor with insignificant intercepts, demonstrating that the market factor explains average cryptocurrency returns very well.     

Distributional dynamics under smoothly state-dependent pricing

Starting from the assumption that firms are more likely to adjust their prices when doing so is more valuable, this paper analyzes monetary policy shocks in a DSGE model with firm-level heterogeneity. The model is calibrated to retail price microdata, and inflation responses are decomposed into “intensive”, “extensive”, and “selection” margins. Money growth and Taylor rule shocks both have nontrivial real effects, because the low state dependence implied by the data rules out the strong selection effect associated with fixed menu costs. The response to sector-specific shocks is gradual, but inappropriate econometrics might make it appear immediate.     

Loan pricing under Basel capital requirements

We analyze the loan pricing implications of the reform of bank capital regulation known as Basel II. We consider a perfectly competitive market for business loans where, as in the model underlying the internal ratings based (IRB) approach of Basel II, a single risk factor explains the correlation in defaults across firms. Our loan pricing equation implies that low risk firms will achieve reductions in their loan rates by borrowing from banks adopting the IRB approach, while high risk firms will avoid increases in their loan rates by borrowing from banks that adopt the less risk-sensitive standardized approach of Basel II. We also show that only a very high social cost of bank failure might justify the proposed IRB capital charges, partly because the net interest income from performing loans is not counted as a buffer against credit losses. A net interest income correction for IRB capital requirements is proposed.     

The Phillips curve under state-dependent pricing

We derive a Phillips curve equation from the dynamic stochastic general equilibrium (DSGE) model with state-dependent pricing developed by Dotsey et al. [1999. State-dependent pricing and the general equilibrium dynamics of money and output. Quarterly Journal of Economics 114, 655-690]. This state-dependent Phillips curve encompasses the new Keynesian Phillips curve (NKPC) based on Calvo-type price setting as a special case. We analyze the effect of the state-dependent terms (that is, the variations in the distributions of price vintages) on inflation persistence, and we examine whether the hybrid NKPC (that is, the NKPC extended by a lagged inflation term) can adequately describe inflation dynamics generated in a calibrated state-dependent pricing economy.     

Asset pricing under quantile utility maximization

“Focus on the downside, and the upside will take care of itself” is a famous quote among professional investors. By considering an agent who follows this advice, we reproduce the first and second moments of stock returns, risk-free rate and consumption growth. The agent's behavior toward risk is analogous to a relative risk aversion of about 3 under expected utility, the elasticity of intertemporal substitution is about 0.5 and the time discount factor is below 1. In particular, the proposed model separates time and risk preferences in an innovative way.     

Quantifying the uncertainty about the fit of a new Keynesian pricing model

Recent studies by Gali and Gertler [1999. Inflation dynamics: a structural econometric analysis, Journal of Monetary Economics 44, 195-222] and Sbordone [2002. Prices and unit labor costs: testing models of pricing, Journal of Monetary Economics 49, 265-292] conclude that a theoretical inflation series implied by a forward-looking New Keynesian pricing equation fits post-1960 U.S. inflation closely. Their theoretical inflation series is conditional on (i) a reduced-form forecasting process for real marginal cost; and (ii) the calibration of the pricing equation. The present paper shows that both of these determinants are surrounded by considerable uncertainty. When quantifying the impact of this uncertainty on theoretical inflation, we can no longer say whether the forward-looking pricing equation explains observed inflation dynamics very well or very poorly.     

Monetary policy under financial uncertainty

Monetary policy may play a substantial role in mitigating the effects of financial crises. In this paper, I suppose that the economy occasionally but infrequently experiences crises, where financial variables affect the broader economy. I analyze optimal monetary policy under such financial uncertainty, where policymakers recognize the possibility of crises. Optimal monetary policy is affected during the crisis and in normal times, as policymakers guard against the possibility of crises. In the estimated model this effect is quite small. Optimal policy does change substantially during a crisis, but uncertainty about crises has relatively little effect.     

Risk aversion under preference uncertainty

We show that if an agent is uncertain about the precise form of his utility function, his actual relative risk aversion may depend on wealth even if he knows his utility function lies in the class of constant relative risk aversion (CRRA) utility functions. We illustrate the consequences of this result for optimal asset allocation: poor agents that are uncertain about their risk aversion parameter invest less in risky assets than wealthy investors with identical risk aversion uncertainty.     

Strategic capacity investment under uncertainty

This article considers investment decisions within an uncertain dynamic and duopolistic framework. Each investment decision involves to determine the timing and the capacity level. The simultaneous analysis of timing and capacity decisions extends work on entry deterrence/accommodation to consider a timing/delay element. We find that, when applying an entry deterrence policy, the first investor, or incumbent, overinvests in capacity for two reasons. First, it delays the investment of the second investor, or entrant. Second, the entrant will invest in less capacity. We also find that greater uncertainty makes entry deterrence more likely.     

Option pricing under a Gamma-modulated diffusion process

We study a Gamma-modulated diffusion process as a long-memory generalization of the standard Black-Scholes model. This model introduces a time dependent volatility. The option pricing problem associated with this type of processes is computed.