Immigrants are always accused of stealing people's jobs. Yet, in a neoclassical model of the labor market, there are jobs for everybody and no jobs to steal. (There is no unemployment, so anybody who wants to work can work.) In standard matching models, there is some unemployment, but labor demand is perfectly elastic so new entrants into the labor force are absorbed without affecting jobseekers' prospects. Once again, no jobs are stolen when immigrants arrive. This paper shows that in a matching model with job rationing, in contrast, the entry of immigrants reduces the employment rate of native workers. Moreover, the reduction in employment rate is sharper when the labor market is depressed -- because jobs are more scarce then. Because immigration reduces labor-market tightness, it makes it easier for firms to recruit and improves firm profits. The overall effect of immigration on native welfare depends on the state of the labor market. It is always negative when the labor market is inefficiently slack, but some immigration improves welfare when the labor market is inefficiently tight.
In this research we employ a range of multivariate asset models based on L\'evy processes to price exotic derivatives. We compare their ability to fit market data and replicate price benchmarks, and evaluate their flexibility in terms of parametrization and dependence structure. We review recent risk-neutral calibration approaches and techniques in the multivariate setting, and provide tools to make well-informed decisions in a practical context. A special focus is given to the ability of the models to capture linear and nonlinear dependence, with implications on their pricing performance. Given the exotic features of the analyzed derivatives, valuation is carried out through Monte Carlo methods.
An actively managed portfolio almost never beats the market in the long term. Thus, many investors often resort to passively managed portfolios whose aim is to follow a certain financial index. The task of building such passive portfolios aiming also to minimize the transaction costs is called Index Tracking (IT), where the goal is to track the index by holding only a small subset of assets in the index. As such, it is an NP-hard problem and becomes unfeasible to solve exactly for indices with more than 100 assets. In this work, we present a novel hybrid simulated annealing method that can efficiently solve the IT problem for large indices and is flexible enough to adapt to financially relevant constraints. By tracking the S&P-500 index between the years 2011 and 2018 we show that our algorithm is capable of finding optimal solutions in the in-sample period of past returns and can be tuned to provide optimal returns in the out-of-sample period of future returns. Finally, we focus on the task of holding an IT portfolio during one year and rebalancing the portfolio every month. Here, our hybrid simulated annealing algorithm is capable of producing financially optimal portfolios already for small subsets of assets and using reasonable computational resources, making it an appropriate tool for financial managers.