Estimating moral and financial subjective values to explain preferences for philanthropy Philanthropy is characterized by a tension between promoting moral values aimed at increasing humanity’s quality of life and the material cost incurred to achieve said goal. Material values and preferences are well captured by computational models of choice; however, little is known about moral values: can computation models of choice explain moral preferences?
We develop a simple, non-parametric approach for estimating the entire distribution of skill. Our approach avoids the challenge of correctly specifying the distribution, and allows for a joint analysis of multiple measures–a key requirement for examining skill. Our results show that more than 85% of the funds are skilled at detecting profitable trades, but unskilled at overriding capacity constraints. Aggregating both skill dimensions using the value added, we find that around 70% of the funds are able to generate profits. The average value added after funds have reached their long-term size equals 7.1 mio. per year, which represents two thirds of the optimal value predicted by neoclassical theory. For all skill measures, the distribution is highly non-normal and reveals a strong heterogeneity across funds.
Dynamic stochastic general equilibrium models with ex-post heterogeneity due to idiosyncratic risk pose numerous challenges stemming from the cross-sectional distribution of endogenous variables which changes stochastically over time due to aggregate risk. In this thesis, I tackle various open questions. My first contribution is of a theoretical nature as I establish existence and uniqueness of the Aiyagari-Bewley growth model. The second challenge I address has a more practical concern. I propose a new numerical method to compute solutions to heterogeneous agent models. With the derived approximation error bounds, I ensure convergence to the rational expectations equilibrium. Equipped with this novel theoretically founded method, I show that even two standard economic models like the Aiyagari-Bewley growth model and the Huggett economy yield intriguing results. When agents progressively share idiosyncratic risk, heterogeneity increasingly amplifies aggregate risk. Furthermore, the volatility of the expected stationary cross-sectional distribution and of the stationary price distribution rises.
This paper examines whether the predictability of securitized real estate returns differs from that of stock returns. It also provides a cross-country comparison of securitized real estate return predictability. In contrast to most of the literature on this issue, the analysis is not based on a multifactor asset pricing framework as such analyses may bias the results.We use a time series approach and thus create a level playing field to compare the predictability of the two asset classes. Forecasts are performed with ARMAand ARMA–EGARCH models and evaluated by comparing the entire empirical distributions of prediction errors, as well as with a trading strategy. The results, based on daily data for the 1990–2007 period, show that securitized real estate returns are generally more predictable than stock returns in countries with mature and well established REIT regimes. ARMA–EGARCH models are found to have portfolio outperformance potential even in the presence of transaction costs, with generally better results for securitized real estate than for stocks.
Using properties of the cdf of a random variable defined as a saddle-type point of a real valued continuous stochastic process, we derive first-order asymptotic properties of tests for stochastic spanning w.r.t. a stochastic dominance relation. First, we define the concept of Markowitz stochastic dominance spanning, and develop an analytical representation of the spanning property. Second, we construct a non-parametric test for spanning via the use of an empirical analogy. The method determines whether introducing new securities or relaxing investment constraints improves the invest- ment opportunity set of investors driven by Markowitz stochastic dominance. In an application to standard data sets of historical stock market returns, we reject mar- ket portfolio Markowitz efficiency as well as two-fund separation. Hence there exists evidence that equity management through base assets can outperform the market, for investors with Markowitz type preferences.
We use the database leak of Mt. Gox exchange to analyze the dynamics of the price of bitcoin from June 2011 to November 2013. This gives us a rare opportunity to study an emerging retail-focused, highly speculative and unregulated market with trader identifiers at a tick transaction level. Jumps are frequent events and they cluster in time. The order flow imbalance and the preponderance of aggressive traders, as well as a widening of the bid-ask spread predict them. Jumps have short-term positive impact on market activity and illiquidity and see a persistent change in the price.
This paper introduces a new numerical option pricing method by fast recursive projections. The projection step consists in representing the payoff and the state price density with a fast discrete transform based on a simple grid sampling. The recursive step consists in transmitting coefficients of the representation from one date to the previous one by an explicit recursion formula. We characterize the convergence rate of the computed option price. Numerical illustrations with different American and Bermudan payoffs with discrete dividend paying stocks in the Black-Scholes and Heston models show that the method is fast, accurate, and general.