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Practical Volatility and Correlation Modeling for Financial Market Risk Management
"What do academics have to offer market risk management practitioners in financial institutions? Current industry practice largely follows one of two extremely restrictive approaches: historical simulation or RiskMetrics. In contrast, we favor flexible methods based on recent developments in financial econometrics, which are likely to produce more accurate assessments of market risk. Clearly, the demands of real-world risk management in financial institutions--in particular, real-time risk tracking in very high-dimensional situations--impose strict limits on model complexity. Hence we stress parsimonious models that are easily estimated, and we discuss a variety of practical approaches for high-dimensional covariance matrix modeling, along with what we see as some of the pitfalls and problems in current practice. In so doing we hope to encourage further dialog between the academic and practitioner communities, hopefully stimulating the development of improved market risk management technologies that draw on the best of both worlds"--National Bureau of Economic Research web site.
Handbook of Financial Time Series
The Handbook of Financial Time Series gives an up-to-date overview of the field and covers all relevant topics both from a statistical and an econometrical point of view. There are many fine contributions, and a preamble by Nobel Prize winner Robert F. Engle.
Volatility
Volatility ranks among the most active and successful areas of research in econometrics and empirical asset pricing finance over the past three decades. This two-volume collection of papers comprises some of the most influential published works from this burgeoning literature, both classic and contemporary. Topics covered include GARCH, stochastic and multivariate volatility models as well as forecasting, evaluation and high-frequency data. Together with an original introduction by the editors, this definitive compilation presents the most important milestones and contributions that helped pave the way to today's understanding of volatility.
Responding to Uncertain Conditions
The global business environment is highly uncertain, fractured by unforeseen events and making decisions that deal with a largely unknown future - organizations must improve their ability to respond. This volume of articles presents a new set of studies that attempt to better understand and address this very need.
The Routledge Companion to Strategic Risk Management
Managing risk in and across organizations has always been of vital importance, both for individual firms and for the globalized economy more generally. With the global financial crisis, a dramatic lesson was learnt about what happens when risk is underestimated, misinterpreted, or even overlooked. Many possible solutions have been competing for international recognition, yet, there is little empirical evidence to support the purported effectiveness of these regulations and structured control approaches, which leaves the field wide open for further interpretation and conceptual development. This comprehensive book pulls together a team of experts from around the world in a range of key discip...
Volatility Forecasting
Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.
Short Introduction to Strategic Management
Provides a concise yet rigorous introduction to strategic management and its contemporary challenges, with multiple examples, case studies and references.
Memo
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Modeling and Forecasting Realized Volatility
This paper discusses the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions.
