This paper addresses the following issue: given a set of daily observations on an asset (historical opening, closing, high and low prices), how should one go about estimating the asset’s volatility? We use high-frequency data on very liquid assets to construct daily realized volatility series, which enables us to treat volatility as observed rather than latent. We then compare the empirical per...

This study examines the statistical properties of volatility. Fractal dimension, probability distribution and two-point volatility correlation are used to measure and compare volatility among six di¤erent countries for the 12-year period from Jan. 1 1990 to Dec.31 2001. New York market is found to be the strongest among the six in terms of market e¢ ciency. Moreover, the Tokyo and Singapore mar...

We investigate whether bonds can hedge volatility risk in the U.S. Treasury market, as predicted by most ‘affine’ term structure models. To this end, we construct powerful and model-free empirical measures of the quadratic yield variation for a cross-section of fixed-maturity zero-coupon bonds (‘realized yield volatility’) through the use of high-frequency data. We find that the yield curve fai...

In the first chapter ``Gold, Platinum, and Expected Stock Returns'', I show that the ratio of gold to platinum prices (GP) reveals variation in risk and proxies for an important economic state variable. GP predicts future stock returns in the time-series and explains variation in average stock returns in the cross-section. GP outperforms existing predictors and similar patterns are found in int...

The necessity of an accurate volatility estimate in order to price derivatives, and the time varying nature of volatility, make it imperative to obtain reliable volatility estimates using only the most recent data. More speciically, it is crucial to make use of all the available information. Many volatility estimates are based on the close prices of the instrument alone, despite the fact that h...

We investigate whether bonds can hedge volatility risk in the U.S. Treasury market, as predicted by most ‘affine’ term structure models. To this end, we construct powerful and model-free empirical measures of the quadratic yield variation for a cross-section of fixed-maturity zero-coupon bonds (‘realized yield volatility’) through the use of high-frequency data. We find that the yield curve fai...

Volatility is a significant parameter both in financial and real options valuation. However, in the case of several real option projects there is no historical data available. In such cases, one alternative is to use Monte Carlo simulation on projects’ cash flows for volatility estimation. An important issue that has not been taken into account with most of these volatility simulation procedure...

In this paper, we develop and apply Bayesian inference for an extended NelsonSiegel (1987) term structure model capturing interest rate risk. The so-called Stochastic Volatility Nelson-Siegel (SVNS) model allows for stochastic volatility in the underlying yield factors. We propose a Markov chain Monte Carlo (MCMC) algorithm to efficiently estimate the SVNS model using simulation-based inference...

The main consequence of the launch, in 2005, of the European Union Emission Trading Scheme (EU ETS) has been the establishment of a price for carbon emissions. Thus, major energy producers in Europe are now aware of the impact of their polluting activities. The interest in analysing the carbon markets from a financial point of view has exponentially increased since the launch of the EU ETS. How...

An understanding of volatility in stock markets is important for determining the cost of capital and for assessing investment and leverage decisions as volatility is synonymous with risk. Substantial changes in volatility of financial markets are capable of having significant negative effects on risk averse investors. Using daily returns from 1992 to 2002, we investigate volatility co-movement ...