Journal ArticleDOI
Time series models : in econometrics, finance and other fields
Abstract:
Statistical Aspects of ARCH and Scholastic Volatility Likelihood-Based Inference for Cointegration of Some Non-Stationary Time Series Forecasting in Macroeconomics Longitudinal Panel Data: An Overview of Current Methodologyread more
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Book ChapterDOI
Stochastic processes in insurance and finance
TL;DR: In this paper, the authors dealt mainly with the application of financial pricing techniques to insurance problems, and presented that realistic models for asset price processes are typically incomplete, and that actuarial concepts for risk-management might prove helpful in dealing with these “unhedgeable” risks.
Book ChapterDOI
Parametric and Nonparametric Volatility Measurement
TL;DR: In this paper, a unified continuous-time, frictionless, no-arbitrage framework for systematically categorizing the various volatility concepts, measurement procedures, and modeling procedures is presented.
Posted Content
Volatility and correlation forecasting
TL;DR: A recent survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications is provided in this paper, where 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.
Journal ArticleDOI
Multivariate Stochastic Volatility: A Review
TL;DR: A wide range of MSV models is presented, namely, asymmetric models, factor models, time-varying correlation models, and alternative MSV specifications, including models based on the matrix exponential transformation, the Cholesky decomposition, and the Wishart autoregressive process.
Journal ArticleDOI
Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility Measurements
Siem Jan Koopman,Siem Jan Koopman,Siem Jan Koopman,Borus Jungbacker,Eugenie Hol Uspensky,Eugenie Hol Uspensky +5 more
TL;DR: In this paper, the authors explore the forecasting value of historical volatility (extracted from daily return series), of implied volatility (derived from option pricing data) and of realised volatility (computed as the sum of squared high frequency returns within a day).