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Kamil Yilmaz

Researcher at Koç University

Publications -  98
Citations -  11580

Kamil Yilmaz is an academic researcher from Koç University. The author has contributed to research in topics: Social connectedness & Volatility (finance). The author has an hindex of 29, co-authored 92 publications receiving 7571 citations.

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Better to give than to receive: Predictive directional measurement of volatility spillovers

TL;DR: This paper used a generalized vector autoregressive framework to characterize daily volatility spillovers across US stock, bond, foreign exchange and commodities markets, from January 1999 to January 2010, and showed that despite significant volatility fluctuations in all four markets during the sample, cross-market volatility spillover were quite limited until the global financial crisis, which began in 2007.
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On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms

TL;DR: In this paper, the authors propose several connectedness measures built from pieces of variance decomposition positions, and argue that they provide natural and insightful measures of connectedness among nancial asset returns and volatilities.
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Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets

TL;DR: In this article, the authors provide a simple and intuitive measure of interdependence of asset returns and/or volatilities, and formulate and examine precise and separate measures of return spillovers and volatility spillovers.
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Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets*

TL;DR: In this paper, the authors provide a simple and intuitive measure of interdependence of asset returns and/or volatilities, and formulate and examine precise and separate measures of return spillovers and volatility spillovers.
Journal ArticleDOI

On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms

TL;DR: In this article, the authors propose several connectedness measures built from pieces of variance decompositions, and argue that they provide natural and insightful measures of connectedness among financial asset returns and volatilities.