H
Hans Ole Mikkelsen
Researcher at University of Southern California
Publications - Â 5
Citations - Â 3692
Hans Ole Mikkelsen is an academic researcher from University of Southern California. The author has contributed to research in topics: Capital asset pricing model & Conditional variance. The author has an hindex of 4, co-authored 5 publications receiving 3530 citations.
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Fractionally integrated generalized autoregressive conditional heteroskedasticity
TL;DR: In this article, the FIGARCH (Fractionally Integrated Generalized AutoRegressive Conditionally Heteroskedastic) process is introduced and the conditional variance of the process implies a slow hyperbolic rate of decay for the influence of lagged squared innovations.
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Modeling and pricing long- memory in stock market volatility
TL;DR: In this paper, a new class of fractionally integrated GARCH and EGARCH models for characterizing financial market volatility is discussed, and Monte Carlo simulations illustrate the reliability of quasi maximum likelihood estimation methods, standard model selection criteria, and residual-based portmanteau diagnostic tests in this context.
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Long-term equity anticipation securities and stock market volatility dynamics
TL;DR: In this paper, the authors compare the risk-neutralized option pricing distributions from various ARCH-type formulations and find that the degree of mean reversion in the volatility process implicit in these prices is best described by a Fractionally Integrated EGARCH (FIEGARCH) model.
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An Arbitrage Free Trilateral Target Zone Model
TL;DR: In this paper, an arbitrage free trilateral model of a credible target zone regime with bands on each bilateral exchange rate is proposed, where any two rates must obey their own boundaries but, in addition, free rein for movements is restricted by the band of the redundant rate.
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The Relation between Expected Return and Beta: A Random Resampling Approach
TL;DR: In this article, a method of randomly resampling portfolios from the population of individual stocks a large number of times to infer general population characteristics about the risk-return relationship, while being subject to few biases is proposed.