Institution
Danske Bank
About: Danske Bank is a based out in . It is known for research contribution in the topics: Volatility (finance) & Volatility smile. The organization has 69 authors who have published 145 publications receiving 3733 citations. The organization is also known as: Danske Bank A/S.
Topics: Volatility (finance), Volatility smile, Implied volatility, SABR volatility model, Stock exchange
Papers published on a yearly basis
Papers
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Vilnius University1, University of Ferrara2, Aarhus University3, University of Oslo4, Royal Institute of Technology5, Electromagnetic Geoservices6, University of Trieste7, Norwegian Computing Center8, University of Southern Denmark9, University of Santiago de Compostela10, Danske Bank11, Ruhr University Bochum12, Norwegian Meteorological Institute13, Norwegian Defence Research Establishment14, University of Auckland15, Norwegian University of Science and Technology16, Information Technology University17, Technical University of Ostrava18, Linköping University19, Karlsruhe Institute of Technology20, ETH Zurich21, Australian National University22, University of Modena and Reggio Emilia23, Cisco Systems, Inc.24, University of Buenos Aires25, University of Copenhagen26, University of Erlangen-Nuremberg27, Kazimierz Wielki University in Bydgoszcz28, National Scientific and Technical Research Council29, University of Valencia30, Paul Sabatier University31, University of Melbourne32, University of Nottingham33, University of Bristol34, CLC bio35, Princeton University36, La Trobe University37, Clemson University38
TL;DR: Dalton is a powerful general‐purpose program system for the study of molecular electronic structure at the Hartree–Fock, Kohn–Sham, multiconfigurational self‐consistent‐field, Møller–Plesset, configuration‐interaction, and coupled‐cluster levels of theory.
Abstract: Dalton is a powerful general-purpose program system for the study of molecular electronic structure at the Hartree-Fock, Kohn-Sham, multiconfigurational self-consistent-field, MOller-Plesset, confi ...
1,212 citations
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TL;DR: In this paper, the authors study the forecasting of future realized volatility in the stock, bond, and foreign exchange markets, as well as the continuous sample path and jump components of this, from variables in the information set, including implied volatility backed out from option prices.
Abstract: We study the forecasting of future realized volatility in the stock, bond, and foreign exchange markets, as well as the continuous sample path and jump components of this, from variables in the information set, including implied volatility backed out from option prices. Recent nonparametric statistical techniques of Barndor-Nielsen & Shephard (2004, 2006) are used to separate realized volatility into its continuous and jump components, which enhances forecasting performance, as shown by Andersen, Bollerslev & Diebold (2005). We generalize the heterogeneous autoregressive (HAR) model of Corsi (2004) to include implied volatility as an additional regressor, and to the separate forecasting of the realized components. We also introduce a new vector HAR (VecHAR) model for the resulting simultaneous system, controlling for possible endogeneity issues in the forecasting equations. We show that implied volatility contains incremental information about future volatility relative to both continuous and jump components of past realized volatility. Indeed, in the foreign exchange market, implied volatility completely subsumes the information content of daily, weekly, and monthly realized volatility measures, when forecasting future realized volatility or its continuous component. In addition, implied volatility is an unbiased forecast of future realized volatility in the foreign exchange and stock markets. Perhaps surprisingly, the jump component of realized return volatility is, to some extent, predictable, and options appear to be calibrated to incorporate information about future jumps in all three markets.
276 citations
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TL;DR: In this paper, a jump-process is used to fit the stock price model to the observed volatility smile/skew, which is shown to be unconditionally stable and, if combined with FFT (Fast Fourier Transform) methods, computationally efficient.
Abstract: The standard approach (e.g. Dupire (1994) and Rubinstein (1994)) to fitting stock processes to observed option prices models the underlying stock price as a one-factor diffusion process with state- and time-dependent volatility. While this approach is attractive in the sense that market completeness is maintained, the resulting model is often highly non-stationary, difficult to fit to steep volatility smiles, and generally is not well supported by empirical evidence. In this paper, we attempt to overcome some of these problems by overlaying the diffusion dynamics with a jump-process, effectively assuming that a large part of the observed volatility smiles can be explained by fear of sudden large market movements ("crash-o-phobia"). The first part of this paper derives a forward PIDE (Partial Integro-Differential Equation) satisfied by European call option prices and demonstrates how the resulting equation can be used to fit the model to the observed volatility smile/skew. In the second part of the paper, we discuss efficient methods of applying the calibrated model to the pricing of contingent claims. In particular, we develop an ADI (Alternating Directions Implicit) finite difference method that is shown to be unconditionally stable and, if combined with FFT (Fast Fourier Transform) methods, computationally efficient. The paper also discusses the usage of Monte Carlo methods, and contains several detailed examples from the S&P500 market. We compare pricing results obtained by the jump-diffusion approach with those of pure diffusion, and find significant differences for a range of popular contracts.
229 citations
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TL;DR: In this article, the authors consider extending the Libor market model to markets with volatility skews in observable option prices and discuss efficient techniques for calibration to quoted prices of caps and swaptions.
Abstract: This paper considers extensions of the Libor market model (Brace et al (1997), Jamshidian (1997), Miltersen et al (1997)) to markets with volatility skews in observable option prices. We expand the family of forward rate processes to include diffusions with non-linear forward rate dependence and discuss efficient techniques for calibration to quoted prices of caps and swaptions. Special emphasis is put on generalized CEV processes for which exact closed-form expressions for cap prices are derived. We also discuss modifications of the CEV process which exhibit appealing growth and boundary characteristics. The proposed models are investigated numerically through Crank-Nicholson finite difference schemes and Monte Carlo simulations.
223 citations
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TL;DR: In this article, the forecasting of future realized volatility in the foreign exchange, stock, and bond markets from variables in our information set, including implied volatility backed out from option prices, is studied.
217 citations
Authors
Showing all 69 results
Name | H-index | Papers | Citations |
---|---|---|---|
Schahram Dustdar | 70 | 804 | 28237 |
Jan Damsgaard | 29 | 113 | 3503 |
Tomas Toft | 24 | 43 | 2730 |
Jacob Gyntelberg | 19 | 63 | 1153 |
Caspar Rose | 16 | 46 | 1823 |
Kasper Hald | 14 | 21 | 2524 |
Troels Steenstrup | 14 | 17 | 1022 |
Tue Lehn-Schiøler | 11 | 19 | 652 |
Kristian Sneskov | 10 | 11 | 1620 |
Alexandre Antonov | 10 | 27 | 282 |
Jesper Andreasen | 10 | 17 | 925 |
Peter Lildholdt | 8 | 16 | 367 |
Kalina Stefanova Staykova | 7 | 18 | 174 |
Thomas Busch | 6 | 7 | 503 |
Steen Brahe | 5 | 7 | 133 |