Essays on Risk Aversion, Diversification and Non-Participation

My dissertation consists of three essays. The central theme of these essays is the psychological factors and biases that affect the portfolio allocation decision. The first essay entitled, “Are women more risk-averse than men?” examines the gender difference in risk aversion as revealed by actual investment choices. Using a sample that controls for biases in the level of education and finance knowledge, there is evidence that when individuals have the same level of education, irrespective of their knowledge of finance, women are no more risk-averse than their male counterparts. However, the gender-risk aversion relation is also a function of age, income, wealth, marital status, race/ethnicity and the number of children in the household. The second essay entitled, “Can diversification be learned?” investigates if investors who have superior investment knowledge are more likely to actively seek diversification benefits and are less prone to allocation biases. Results of cross-sectional analyses suggest that knowledge of finance increases the likelihood that an investor will efficiently allocate his direct investments across the major asset classes; invest in foreign assets; and hold a diversified equity portfolio. However, there is no evidence that investors who are more financially sophisticated make superior allocation decisions in their retirement savings. The final essay entitled, “The demographics of non-participation”, examines the factors that affect the decision not to hold stocks. The results of probit regression models indicate that when individuals are highly educated, the decision to not participate in the stock market is less related to demographic factors. In particular, when individuals have attained at least a college degree and have advanced knowledge of finance, they are significantly more likely to invest in equities either directly or indirectly through mutual funds or their retirement savings. There is also evidence that the decision not to hold stocks is motivated by short-term market expectations and the most recent investment experience. The findings of these essays should increase the body of research that seeks to reconcile what investors actually do (positive theory) with what traditional theories of finance predict that investors should do (normative theory).

REITs: Structure and Performance

[Excerpt] REITs are attractive to investors, particularly institutional investors, due to their high dividend payouts and ability to provide more liquidity to the underlying market for direct real estate investment. This chapter analyzes the performance of real estate investment trusts (REITs). It compares the returns on REITs with those on more traditional asset classes, specifically bonds and mid-cap equities, and surveys the academic literature dealing with the diverse issues related to valuation. The chapter also examines the linkages between REIT performance and the behavior of the underlying real estate market. Because the chapter takes the perspective of a U.S.-based investor, it does not directly address the broader issues of global REITs.

Measuring, Modeling, and Forecasting Volatility and Correlations from High-Frequency Data

This dissertation contains four essays that all share a common purpose: developing new methodologies to exploit the potential of high-frequency data for the measurement, modeling and forecasting of financial assets volatility and correlations. The first two chapters provide useful tools for univariate applications while the last two chapters develop multivariate methodologies. In chapter 1, we introduce a new class of univariate volatility models named FloGARCH models. FloGARCH models provide a parsimonious joint model for low frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of the models in a realistic numerical study and on the basis of a data set composed of 65 equities. Using more than 10 years of high-frequency transactions, we document significant statistical gains related to the FloGARCH models in terms of in-sample fit, out-of-sample fit and forecasting accuracy compared to classical and Realized GARCH models. In chapter 2, using 12 years of high-frequency transactions for 55 U.S. stocks, we argue that combining low-frequency exogenous economic indicators with high-frequency financial data improves the ability of conditionally heteroskedastic models to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multi-period Value-at-Risk. Using a refined version of the Realized LGARCH model allowing for time-varying intercept and implemented with realized kernels, we document that nominal corporate profits and term spreads have strong long-run predictive ability and generate accurate risk measures forecasts over long-horizon. The results are based on several loss functions and tests, including the Model Confidence Set. Chapter 3 is a joint work with David Veredas. We study the class of disentangled realized estimators for the integrated covariance matrix of Brownian semimartingales with finite activity jumps. These estimators separate correlations and volatilities. We analyze different combinations of quantile- and median-based realized volatilities, and four estimators of realized correlations with three synchronization schemes. Their finite sample properties are studied under four data generating processes, in presence, or not, of microstructure noise, and under synchronous and asynchronous trading. The main finding is that the pre-averaged version of disentangled estimators based on Gaussian ranks (for the correlations) and median deviations (for the volatilities) provide a precise, computationally efficient, and easy alternative to measure integrated covariances on the basis of noisy and asynchronous prices. Along these lines, a minimum variance portfolio application shows the superiority of this disentangled realized estimator in terms of numerous performance metrics. Chapter 4 is co-authored with Niels S. Hansen, Asger Lunde and Kasper V. Olesen, all affiliated with CREATES at Aarhus University. We propose to use the Realized Beta GARCH model to exploit the potential of high-frequency data in commodity markets. The model produces high quality forecasts of pairwise correlations between commodities which can be used to construct a composite covariance matrix. We evaluate the quality of this matrix in a portfolio context and compare it to models used in the industry. We demonstrate significant economic gains in a realistic setting including short selling constraints and transaction costs.

Pricing derivatives with stochastic volatility

This Ph.D. thesis contains 4 essays in mathematical finance with a focus on pricing Asian option (Chapter 4), pricing futures and futures option (Chapter 5 and Chapter 6) and time dependent volatility in futures option (Chapter 7). In Chapter 4, the applicability of the Albrecher et al.(2005)'s comonotonicity approach was investigated in the context of various benchmark models for equities and com- modities. Instead of classical Levy models as in Albrecher et al.(2005), the focus is the Heston stochastic volatility model, the constant elasticity of variance (CEV) model and the Schwartz (1997) two-factor model. It is shown that the method delivers rather tight upper bounds for the prices of Asian Options in these models and as a by-product delivers super-hedging strategies which can be easily implemented. In Chapter 5, two types of three-factor models were studied to give the value of com- modities futures contracts, which allow volatility to be stochastic. Both these two models have closed-form solutions for futures contracts price. However, it is shown that Model 2 is better than Model 1 theoretically and also performs very well empiri- cally. Moreover, Model 2 can easily be implemented in practice. In comparison to the Schwartz (1997) two-factor model, it is shown that Model 2 has its unique advantages; hence, it is also a good choice to price the value of commodity futures contracts. Fur- thermore, if these two models are used at the same time, a more accurate price for commodity futures contracts can be obtained in most situations. In Chapter 6, the applicability of the asymptotic approach developed in Fouque et al.(2000b) was investigated for pricing commodity futures options in a Schwartz (1997) multi-factor model, featuring both stochastic convenience yield and stochastic volatility. It is shown that the zero-order term in the expansion coincides with the Schwartz (1997) two-factor term, with averaged volatility, and an explicit expression for the first-order correction term is provided. With empirical data from the natural gas futures market, it is also demonstrated that a significantly better calibration can be achieved by using the correction term as compared to the standard Schwartz (1997) two-factor expression, at virtually no extra effort. In Chapter 7, a new pricing formula is derived for futures options in the Schwartz (1997) two-factor model with time dependent spot volatility. The pricing formula can also be used to find the result of the time dependent spot volatility with futures options prices in the market. Furthermore, the limitations of the method that is used to find the time dependent spot volatility will be explained, and it is also shown how to make sure of its accuracy.