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This paper advances the specification and estimation of econometric models of retirement and saving in two earner families. The complications introduced by the interaction of retirement decisions by husbands and wives have led researchers to adopt a number of simplifications. Our analysis relaxes these restrictions. The model includes three labor market states, full-time work, partial retirement, and full retirement; reverse flows from states of lesser to greater work; an extended choice set created when spouses make independent retirement decisions; heterogeneity in time preference; varying taste parameters for full-time and part-time work; and the possibility of changes in preferences after retirement.

A dynamic model of the evolution of health for those over the age of 50 is embedded in a structural, econometric model of retirement and saving. Effects of smoking, obesity, alcohol consumption, depression, and other proclivities on medical conditions are analyzed, including hypertension, diabetes, cancer, lung disease, heart problems, stroke, psychiatric problems, and arthritis. Compared to a population in good health, the current health of the population reduces retirement age by about one year. Including detailed health dynamics in a retirement model does not influence estimates of the marginal effects of economic incentives on retirement.

This chapter focuses on examining how changes in the liquidity differential between nominal and TIPS yields influence optimal portfolio allocations in U.S. Treasury securities. Based on a nonparametric estimation technique and comparing the optimal allocation decisions of mean-variance and CRRA investor, when investment opportunities are time varying, I present evidence that liquidity risk premium is a significant risk-factor in a portfolio allocation context. In fact, I find that a conditional allocation strategy translates into improved in-sample and out-of-sample asset allocation and performance. The analysis of the portfolio allocation to U.S. government bonds is particularly important for central banks, specially in developing countries, given the fact that, collectively they have accumulate a large holdings of U.S. securities over the last 15 years.

It has long been challenged that the distributions of empirical returns do not follow the log-normal distribution upon which many celebrated results of finance are based including the Black–Scholes Option-Pricing model. Borland (2002) succeeds in obtaining alternate closed form solutions for European options based on Tsallis distribution, which allow for statistical feedback as a model of the underlying stock returns. Motivated by this, we simulate two distinct time series based on initial data from NIFTY daily close values, one based on the Gaussian return distribution and the other on non-Gaussian distribution. Using techniques of non-linear dynamics, we examine the underlying dynamic characteristics of both the simulated time series and compare them with the characteristics of actual data. Our findings give a definite edge to the non-Gaussian model over the Gaussian one.

The pricing kernel puzzle of Jackwerth (2000) concerns the fact that the empirical pricing kernel implied in S&P 500 index options and index returns is not monotonically decreasing in wealth as standard economic theory would suggest. Thus, those options are currently priced in a way such that any risk-averse investor would increase his/her utility by trading in them. We provide a representative agent model where volatility is a function of a second momentum state variable. This model is capable of generating the empirical patterns in the pricing kernel, albeit only for parameter constellations that are not typically observed in the real world.

In a Bayesian approach, we compare the forecasting performance of five classes of models: ARCH, GARCH, SV, SV-STAR, and MSSV using daily Tehran Stock Exchange (TSE) market data. To estimate the parameters of the models, Markov chain Monte Carlo (MCMC) methods is applied. The results show that the models in the fourth and the fifth class perform better than the models in the other classes.

Increasing availability of the financial data has opened new opportunities for quantitative modeling. It has also exposed limitations of the existing frameworks, such as low accuracy of the simplified analytical models and insufficient interpretability and stability of the adaptive data-driven algorithms. I make the case that boosting (a novel, ensemble learning technique) can serve as a simple and robust framework for combining the best features of the analytical and data-driven models. Boosting-based frameworks for typical financial and econometric applications are outlined. The implementation of a standard boosting procedure is illustrated in the context of the problem of symbolic volatility forecasting for IBM stock time series. It is shown that the boosted collection of the generalized autoregressive conditional heteroskedastic (GARCH)-type models is systematically more accurate than both the best single model in the collection and the widely used GARCH(1,1) model.