Measuring and Modeling Risk Using High-Frequency Data

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be obtained by summing over squared high-frequency returns. In turn, this so-called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure of volatility. Moreover, non-parametric measures of systematic risk are attainable, that can straightforwardly be used to model the commonly observed time-variation in the betas. The discussion of these new measures and methods is accompanied by an empirical illustration using high-frequency data of the IBM incorporation and of the DJIA index

Multinational Enterprise and Subsidiaries' Absorptive Capacity and Global Knowledge Sourcing

We build on extant theory of the Multinational Enterprise (MNE), MNE subsidiaries and absorptive capacity (AC) to develop a framework that allows us to explore the role of MNE subsidiaries in the global sourcing of knowledge and MNE performance. We develop and test hypotheses using primary questionnairecollected data. Our results support the idea that subsidiaries realized AC can be improved by the realized and potential AC of the MNE group and the subsidiary and in turn may improve the performance of the subsidiaries and the MNE group as a whole

Realized volatility and jumps in the Athens Stock Exchange

We test for and model the volatility jumps for three major indices of the Athens Stock Exchange (ASE). Using intra-day data we first construct several, state-of-the-art realized volatility estimators. We use these estimators to construct the jump components of volatility and perform various tests on their properties. Then we use the class of Heterogeneous Autoregressive (HAR) models for assessing the relevant effects of jumps on volatility. Our results expand and complement the previous literature on the ASE market and, in particular, this is the first time, to the best of our knowledge, that volatility jumps are examined and modelled for the Greek market, using a variety of realized volatility estimators. Finally, we compare the economic value of these volatility estimators and examine their differences in the context of a two-asset portfolio and volatility timing