Essays on Asset Return and Housing Market

The last two decades have witnessed substantial amount of research on time variation in asset returns. It has been found that macroeconomic variables contain useful information about asset returns. This dissertation consists of three essays that study the link between the macroeconomy and financial markets. A central idea behind the link is that households adjust their consumption spending in anticipation of variations in the return on household assets. The first essay proposes a latent-variables approach to estimate expected returns on total household assets and expected growth rate of excess consumption (consumption in excess of labor income) within a present-value model of consumption. The present-value model of consumption implies that the ratio of consumption- aggregate wealth reflects information about future asset returns and consumption growth. Since expected returns and expected excess consumption growth are unobserved variables, the current literature uses lagged excess consumption-assets ratio or other proxies for estimation. This essay goes beyond the existing literature by using an unobserved component approach to filter these unobserved variables from the observed history of realized returns and realized excess consumption growth. Results suggest that both filtered returns and filtered excess consumption growth rate are significant and better predictors of realized returns and realized excess consumption growth rate than the one obtained by lagged excess consumption-assets ratio. The second essay focuses on estimating expected return on housing by exploiting the information from the movements in consumption, income, and observable assets. To do so, a present-value model of consumption is combined with an unobserved component model. Kalman filter is then applied to extract expected housing returns from the observed history of realized returns and realized excess consumption growth. Results suggest that the filtered housing returns does a significantly better job in predicting realized housing returns than other popular predictors like mortgage rate and price-rent ratio. The third essay uses an unobserved components model with heteroskedastic disturbances to measure the time-varying importance of permanent and transitory components in the U.S. and U.K. house prices. Estimation results suggest that the movement in house prices in the two economies is mainly transitory in nature from its trend path

A FLEXIBLE PARAMETRIC GARCH MODEL WITH AN APPLICATION TO EXCHANGE RATES

International Relations/Trade, Research Methods/ Statistical Methods,

Evaluating exponential GARCH models

In this paper, a unified framework for testing the adequancy of an estimated EGARCH model is presented. The tests are Lagrange multiplier or Lagrange multiplier type tests and include testing an EGARCH model against a higher-order one and testing parameter constancy. Furthermore, various existing ways of testing the EGARCH model against GARCH one are investigated as another check of model adequacy. This is done by size and power simulations. Small-sample properties of the other tests are also investigated by simulations.evalation of volatility models; modelling volatility; parameter constancy; GARCH

Bootstrap Methods in Econometrics

There are many bootstrap methods that can be used for econometric analysis. In certain circumstances, such as regression models with independent and identically distributed error terms, appropriately chosen bootstrap methods generally work very well. However, there are many other cases, such as regression models with dependent errors, in which bootstrap methods do not always work well. This paper discusses a large number of bootstrap methods that can be useful in econometrics. Applications to hypothesis testing are emphasized, and simulation results are presented for a few illustrative cases.bootstrap, Monte Carlo test, wild bootstrap, sieve bootstrap, moving block bootstrap

A flexible parametric GARCH model with an application to exchange rates

[[notice]]補正完畢[[conferencetype]]國內[[conferencedate]]19990901~1999090

The transformed Gram Charlier distribution: Parametric properties and financial risk applications

In this paper we study an extension of the Gram-Charlier (GC) density in Jondeau and Rockinger (2001) which consists of a Gallant and Nychka (1987) transformation to ensure positivity without parameter restrictions. We derive its parametric properties such as unimodality, cumulative distribution, higher-order moments, truncated moments, and the closed-form expressions for the expected shortfall (ES) and lower partial moments. We obtain the analytic k-th order stationarity conditions for the unconditional moments of the TGARCH model under the transformed GC (TGC) density. In an empirical application to asset return series, we estimate the tail index; backtest the density, VaR and ES; and implement a comparative analysis based on Hansen's skewed-t distribution. Finally, we present extensions to time-varying conditional skewness and kurtosis, and a new class of mixture densities based on this TGC distribution

Forecasting risk via realized GARCH, incorporating the realized range

The realized GARCH framework is extended to incorporate the realized range, and the intra-day range, as potentially more efficient series of information than re- alized variance or daily returns, for the purpose of volatility and tail risk forecasting in a financial time series. A Bayesian adaptive Markov chain Monte Carlo method is employed for estimation and forecasting. Compared to a range of well known parametric GARCH models, predictive log-likelihood results across six market in- dex return series favor the realized GARCH models incorporating the realized range. Further, these same models also compare favourably for tail risk forecasting, both during and after the global financial crisis

Bootstrap Hypothesis Testing

This paper surveys bootstrap and Monte Carlo methods for testing hypotheses in econometrics. Several different ways of computing bootstrap P values are discussed, including the double bootstrap and the fast double bootstrap. It is emphasized that there are many different procedures for generating bootstrap samples for regression models and other types of model. As an illustration, a simulation experiment examines the performance of several methods of bootstrapping the supF test for structural change with an unknown break point.bootstrap test, supF test, wild bootstrap, pairs bootstrap, moving block bootstrap, residual bootstrap, bootstrap P value

Modeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-type Volatility Models

This paper applies Markov-switching multifractal (MSM) processes to model and forecast carbon dioxide (CO2) emission price volatility, and compares their forecasting performance to the standard GARCH, fractionally integrated GARCH (FIGARCH) and the two-state Markov-switching GARCH (MS-GARCH) models via three loss functions (the mean squared error, the mean absolute error and the value-at-risk). We evaluate the performance of these models via the superior predictive ability test. We find that the forecasts based on the MSM model cannot be outperformed by its competitors under the vast majority of criteria and forecast horizons, while MS-GARCH mostly comes out as the least successful model. Applying various VaR backtesting procedures, we do, however, not find significant differences in the performance of the candidate models under this particular criterion. We also find that we cannot reject the null hypothesis of MSM forecasts encompassing those of GARCH-type models. In line with this result, optimally combined forecasts do indeed hardly improve upon the best single models in our sample