Detecting change points in VIX and S&amp;P 500: A new approach to dynamic asset allocation

Nystrup, Peter; Hansen, Bo William; Madsen, Henrik; Lindström, Erik

doi:10.1057/jam.2016.12

Cited by 16 publications

(7 citation statements)

References 32 publications

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“…Other literature shows how another quantitative indicator, the VIX (CBOE Volatility Index), is used (Copeland & Copeland, 1999;Nystrup et al, 2016;Vorlow, 2017). The VIX was created to measure expected volatility in the equity market, specifically the volatility in the US equity market and is often referred to as the 'fear index'.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There exists extensive literature on the use of various tools and methodologies which have been incorporated into portfolio strategies to deliver the best possible outcome for investors (Ammann & Zimmermann, 2001;Blitz & van Vliet, 2008;Clewell et al, 2017;Dahlquist & Harvey, 2005;Nystrup et al, 2016). These have been tried and tested, but the jury is still out regarding their effectiveness to produce meaningful returns for tactically managed portfolios.…”

Section: Introductionmentioning

confidence: 99%

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Tactical asset allocation using the Kalman filter

Rooyen

Vuuren

2022

Investment Analysts Journal

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Tactical asset allocation (TAA) is a dynamic investment strategy which seeks actively to adjust fund allocation to a variety of asset classes by systematically exploiting inefficiencies and temporary imbalances in equilibrium values. This approach contrasts with strategic asset allocation (SAA) in which a long-term investment view target allocation is established using a combination of target return and risk tolerance. Asset returns are forecasted using the Capital Asset Pricing Model (CAPM), complemented with results obtained from the Kalman filter. Performance of TAA and SAA approaches are compared using several diagnostic metrics. The TAA approach outperforms its SAA counterpart for most of these metrics for the period under consideration, showing some potential benefits of using this approach.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tactical asset allocation using the Kalman filter

Rooyen

Vuuren

2022

Investment Analysts Journal

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“…Fitting a jump model is related to change-point detection (Nystrup et al, 2016;Oh & Han, 2000;Ross et al, 2011), segmentation (Hallac et al, 2019;Katz & Crammer, 2015), and trend filtering (Kim et al, 2009) with the fundamental difference that the states are assumed to be recurring. The HMM is a special case of a jump model where the probability distribution that generates an observation depends on the state of an unobserved Markov chain.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection in Jump Models

2021

Self Cite

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“…Other alternatives are very popular volatility indexes, like DAX Volatility Index and CBOE VIX, (see CBOE (2003) or Kuepper (2022) for details) that estimate the implied volatility of options with an average expiration of 30 days, but they aggregate multiple put/call options (over both the maturities and the strikes), they are computationally more complex, and they mainly serve as exploratory tools to assess the overall market sentiment. Nevertheless, there can be also used some nonparametric detection approaches as suggested in Nystrup et al (2016) or Füss et al (2011). However, our primary focus-embedded within an option and strike specific interpolation instead-is to provide market agents with a valid stochastic tool to be able to correctly make inferences about some rather specific market based on the significance of changes occurring due to some particular (well recognized) external stimuli.…”

Section: Introductionmentioning

confidence: 99%

Using interpolated implied volatility for analysing exogenous market changes

Maciak,

Vitali

2024

Comput Manag Sci

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This paper focuses on market changes due to exogenous effects. The standard implied volatility is shown to be insufficient for a proper detection and analysis of this type of risk. This is mainly because such changes are usually dominated by endogenous effects coming from a specific trading mechanism or natural market dynamics. A methodologically unique approach based on artificial options that always have a constant time to maturity is proposed and explicitly defined. The key principle is to use interpolated volatilities, which can effectively eliminate instabilities due to the natural market dynamics while the changes caused by the exogenous causes are preserved. Formal statistical tests for distinguishing significant effects are proposed under different theoretical and practical scenarios. Statistical theory, computational and algorithmic details, and comprehensive empirical comparisons together with a real data illustration are all presented.

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Detecting change points in VIX and S&P 500: A new approach to dynamic asset allocation

Cited by 16 publications

References 32 publications

Tactical asset allocation using the Kalman filter

Tactical asset allocation using the Kalman filter

Feature Selection in Jump Models

Using interpolated implied volatility for analysing exogenous market changes

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