On the Relationship Between the Conditional Mean and Volatility of Stock Returns: A Latent VAR Approach

We model the conditional mean and volatility of stock returns as a latent vector autoregressive (VAR) process to study the contemporaneous and intertemporal relationship between expected returns and risk in a flexible statistical framework and without relying on exogenous predictors. We find a strong and robust negative correlation between the innovations to the conditional moments that leads to pronounced counter-cyclical variation in the Sharpe ratio. We document significant lead-lag correlations between the conditional moments that also appear related to business cycles. Finally, we show that although the conditional correlation between the mean and volatility is negative, the unconditional correlation is positive due to the lead-lag correlations.

Pricing Weather Derivatives

This paper presents a general method for pricing weather derivatives. Specification tests find that a temperature series for Fresno, California follows a mean-reverting Brownian motion process with discrete jumps and ARCH errors. Based on this process, we define an equilibrium pricing model for cooling degree day weather options. Comparing option prices estimated with three methods: a traditional burn-rate approach, a Black-Scholes-Merton approximation, and an equilibrium Monte Carlo simulation reveals significant differences. Equilibrium prices are preferred on theoretical grounds, so are used to demonstrate the usefulness of weather derivatives as risk management tools for California specialty crop growers.derivative, jump-diffusion process, mean-reversion, volatility, weather, Demand and Price Analysis,

Machine learning methods for public policy : simulation, optimization, and visualization

Society faces many complex management problems, particularly in the area of shared public resources such as ecosystems. Existing decision making processes are often guided by personal experience and political ideology rather than state-of-the-art scientific understanding. This dissertation envisions a future in which multiple stakeholders are provided with computational tools for formalizing their management preferences and computing optimal solutions based on state-of-the-art computational simulations. To make this vision a reality, advances are required in optimization and visualization, and this dissertation presents research on both topics within the formalism of the Markov decision process (MDP). First, it describes an interactive visualization system for understanding the MDP under user-defined management policies, reward functions, and transition dynamics. Second, it presents a method for optimizing management policies for the user-parameterized MDPs. The research is illustrated and validated using a combination of benchmark MDPs and an application to the management of wildfire in ponderosa pine forests. For the wildfire problem, an excellent high-fidelity model of forest growth and wildfire behavior is employed. However, this model is extremely slow, which prevents interactive visualization and optimization. To address simulation computational expense, the dissertation also presents a method for creating a fast surrogate model and shows that this model is sufficiently accurate to support policy optimization and visualization.Keywords: direct policy search, reinforcement learning, testing, artificial intelligence, markov decision processes, public policy, visualization, wildfire, optimization, model-free Monte Carl

Does Britain or the United States Have the Right Gasoline Tax?

This paper develops an analytical framework for assessing the second-best optimal level of gasoline taxation, taking into account unpriced pollution, congestion, and accident externalities and interactions with the broader fiscal system. We provide calculations of the optimal taxes for the United States and the United Kingdom under a wide variety of parameter scenarios, with the gasoline tax substituting for a distorting tax on labor income. Under our central parameter values, the second-best optimal gasoline tax is $1.01 per gallon for the United States and$1.34 per gallon for the United Kingdom. These values are moderately sensitive to alternative parameter assumptions. The congestion externality is the largest component in both nations, and the higher optimal tax for the United Kingdom is due mainly to a higher assumed value for marginal congestion cost. Revenue-raising needs, incorporated in a “Ramsey” component, also play a significant role, as do accident externalities and local air pollution. The current gasoline tax in the United Kingdom ($2.80 per gallon) is more than twice this estimated optimal level. Potential welfare gains from reducing it are estimated at nearly one-fourth the production cost of gasoline used in the United Kingdom. Even larger gains in the United Kingdom can be achieved by switching to a tax on vehicle miles with equal revenue yield. For the United States, the welfare gains from optimizing the gasoline tax are smaller, but those from switching to an optimal tax on vehicle miles are very large.gasoline tax, pollution, congestion, accidents, fiscal interactions

Common Factors and Spatial Dependence: An Application to US House Prices

This paper considers panel data models with cross-sectional dependence arising from both spatial autocorrelation and unobserved common factors. It derives conditions for model identification and proposes estimation methods that employ cross-sectional averages as factor proxies, including the 2SLS, Best 2SLS, and GMM estimations. The proposed estimators are robust to unknown heteroskedasticity and serial correlation in the disturbances, unrequired to estimate the number of unknown factors, and computationally tractable. The paper establishes the asymptotic distributions of these estimators and compares their consistency and efficiency properties. Extensive Monte Carlo experiments lend support to the theoretical findings and demonstrate the satisfactory finite sample performance of the proposed estimators. The empirical section of the paper finds strong evidence of spatial dependence of real house price changes across 377 Metropolitan Statistical Areas in the US from 1975Q1 to 2014Q4. The results also reveal that population and income growth have significantly positive direct and spillover effects on house price changes. These findings are robust to different specifications of the spatial weights matrix constructed based on distance, migration flows, and pairwise correlations