Forecasting hedge fund volatility: a Markov regime-switching approach

Blazsek, Szabolcs; Downarowicz, Anna

doi:10.1080/1351847x.2011.653576

Cited by 5 publications

(1 citation statement)

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“…Our data identify producer, manufacturer, dealer, swap dealer, hedge fund, floor trader, arbitrageur, and non-reportable subcategories) and apply Markov switching models (conditioning on unexplained-by fundamentals-trader positions) as a systematic approach to modeling futures price data. We recursively generate daily probabilities in the model to allow for regime shifts in the data-generating process (Markov regime-switching models can also capture fat tails, asymmetries, autocorrelation, volatility clustering, and mean reversion in financial asset series (see [25])). Many authors argue that nonlinear processes model the behavior of financial variables better than linear processes-e.g., [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Crude Oil Price Movements and Institutional Traders

Brunetti,

Harris,

Büyükşahin

2024

Commodities

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We analyze the role of hedge fund, swap dealer, and arbitrageur activity in the crude oil market. The contribution of our work is to examine the role of institutional traders in switching between high-volatility and low-volatility regimes. Using confidential position data on institutional investors, we first analyze the linkages between trader positions and fundamentals. We find that these institutional position changes reflect fundamental economic factors. Subsequently, we adopt a Markov regime-switching model with time-varying probabilities and find that institutional position changes contribute incrementally to the probability of regime changes.

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