Volatility Is Rough — TEI-tagged view

Jim Gatheral, Thibault Jaisson, Mathieu Rosenbaum · 2018 · quantitative-finance · doi:10.1080/14697688.2017.1393551 · full text at the source · TEI xml · tagged edition · index · concepts · authors
Where this record's text came from. faceted 2026-08 by reading the locally held text (pdftext); every evidence string verified as an exact substring of that text before merging. Claim type is 'existence' because the headline claim is that a property (roughness, H ≈ 0.1) is present in observed volatility; the paper additionally contains a critique strand (spurious long memory) and a mechanism strand (Hawkes-process order splitting as the microstructural origin), neither of which the single-valued facet can carry. method is 'observational-study' since the primary evidence is estimation on market high-frequency and realized-variance data (DAX, Bund, S&P, NASDAQ and the Oxford-Man indices); the paper also uses Monte Carlo simulation for bias checks and includes short appendix proofs, so it is genuinely mixed-method. Quotes were taken from lines free of the page numbers and footnote markers that the PDF extraction interleaves into sentences. 2026-08-14, 2026-08-16 and 2026-08-25 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node. Update, 16 September 2026: EXCEPTION TO THE DATED PASS ABOVE, on this record only: this record carries 1 composite quote field - a quote assembled from two or more passages joined by an ellipsis. A COMPOSITE IS NOT AN EXACT CONTIGUOUS SUBSTRING of the staged text and is deliberately not anchored: each of its parts is verified separately at the offsets in evidence composite spans, and a part occurring more than once carries span null rather than a guessed offset. So the 'every quote verified as an exact substring' clause of the dated pass above DOES NOT HOLD for those fields, and never did - the parts were verified, the whole was not. Nothing is deleted, re-grounded or withdrawn: this writes down an exception that was always true of composites and had not been recorded, so a reader was told more than the pass had checked. 2026-09-16 the project CORRECTION TO THE EXEMPTION ABOVE, SAME DAY: THE EXEMPTION DOES NOT APPLY TO THIS RECORD. Its evidence composite spans row(s) are FOSSILS of a 2026-08-25 composite marking that was superseded by a 2026-08-26 re-slice; the quote is an exact contiguous substring of the staged text, so the dated substring claim above holds for it WITHOUT exception. The clause is left in place with this correction beside it rather than removed, so that the error and its repair are both on the record.
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Other fields define these differently the same word, defined another way in another discipline
memory 23 fields · sample-size 5 fields · integration 3 fields · linear regression 2 fields · variance 2 fields
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stochastic volatility fractional Brownian motion Hurst exponent realized volatility power law8 long memory2 Hurst parameter spot volatility volatility surface continuous semimartingales volatility process Black-Scholes framework deterministic function of time local volatility model time-inhomogeneous future volatility surfaces exotic options stochastic volatility models continuous Brownian semi-martingale local volatility dynamics stochastic volatility dynamics local-stochastic-volatility (LSV) models reasonable dynamics slow decay of the autocorrelation function not integrable power-law with exponent less than 1 asymptotic behavior of the covariance function usual time scales of observation dependence structure implied volatility volatility parameter market price of that option strike price time to expiry power-law function of time to expiry term structure of at-the-money (ATM) skew sum of decaying exponentials explosion of the volatility smile presence of jumps ATM volatility skew smoothness parameter increments of the log-volatility approximately normally distributed monofractal scaling property fractional stochastic volatility (FSV) model Rough FSV (RSFV) Rough FSV RFSV FSV Rough FSV (RFSV) RFSV model improved forecasts of realized volatility volatility persistence classical statistical procedures RFSV volatility forecasts conventional AR and HAR volatility forecasts market microstructure explanation high degree of endogeneity of the market algorithmic trading integrated variance model with uncertainty zones very short time intervals daily realized variance estimates Oxford-Man Institute of Quantitative Finance Realized Library integration8 biased upwards smoothness parameter sq regressing log m(q, ∆) against log ∆ fractional Ornstein-Uhlenbeck process mathematical tractability reasonableness of the model at very large times very long reversion time scale choice H < 1/2 observed smoothness of the volatility process term structure of volatility skew mean reversion by practitioners fBM like-behavior of the log-volatility stationarity property one-hour window daily eight-hour windows regularizing effect of the integral operator volatility of volatility persistent periods of high volatility low volatility periods fractal-type behavior range of observation scales volatility process over one day volatility process over a decade straight lines with slope 1.86 power law behavior of the autocorrelation function wrongly convinced classical estimation procedures spurious long memory of volatility classical power law sense RFSV forecast AR and HAR forecasts longer horizons more parsimonious forecast time horizon relevant regression window forecasting horizon volatility over the last week volatility over the last month Hawkes processes extensions of Poisson processes intensity at a given time past jumps point process kernel2 L1 norm power law tail degree of endogeneity of the market one given order many other orders electronic high frequency trading splitting of large orders metaorder superposition effect irregular volatility macroscopic scaling limit of Hawkes processes integrated fractional process cumulated order flow Rough Fractional Stochastic Volatility (RFSV) model FSV model one major difference observational-study83

Our tag layer for this paper

Cross-disciplinary analysis
content-tags & facets: phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text
phenomenonsmoothness (roughness) of the volatility process— “Estimating volatility from recent high frequency data, we revisit the question of the smoothness of the volatility process.”
claim-typeexistence— “Our main result is that log-volatility behaves essentially as a fractional Brownian motion with Hurst exponent H of order 0.1, at any reasonable time scale.”
method — evidence— “We now proceed to estimate the smoothness parameter sq for each q by computing the m(q, ∆) for different values of ∆ and regressing log m(q, ∆) against log ∆.”
transferfrom stochastic processes / statistical physics (fractional Brownian motion, the Hurst exponent): fractional Brownian motion with a Hurst exponent below 1/2 used as the model of log-volatility, in place of the standard semimartingale volatility models of finance— “Our main result is that log-volatility behaves essentially as a fractional Brownian motion with Hurst exponent H of order 0.1, at any reasonable time scale.”transfers[0]
mechanismTHE TERM'S TWO SENSES ARE SEPARATED BY THEIR HISTORY: 'long memory' initially meant the slow decay of the autocorrelation function - anything slower than exponential - and over time acquired the more precise meaning that the function is not integrable, and more precisely still that it decays as a power law with exponent less than one— “Initially, it appears that the term long memory referred to the slow decay of the autocorrelation function (of absolute returns for example), anything slower than exponential. Over time however, it seems that this term has acquired the more precise meaning that the autocorrelation function is not integrable, see [8], and even more precisely…mechanisms[0]
mechanismTHE TECHNICAL SENSE IS DEFENDED AS THE ONLY ESTIMABLE ONE: the recent literature assumes long memory in the technical sense, and the authors add that meaningful results can probably ONLY be obtained under such a specification, since the asymptotic behaviour of the covariance function cannot be estimated without assuming a specific form for it— “Much of the more recent literature, for example [7, 11, 13], assumes long memory in volatility in this more technical sense. Indeed, meaningful results can probably only be obtained under such a specification, since it is not possible to estimate the asymptotic behavior of the covariance function without assuming a specific form.”mechanisms[1]
mechanismTHE QUESTION IS DECLARED UNANSWERABLE AS POSED, and the authors then supply a different instrument rather than a different answer: stated in terms of the asymptotic behaviour of the autocorrelation function the long memory question simply cannot be answered, but explicit expressions still allow the dependence structure to be analysed— “that when stated in term of the asymptotic behavior of the autocorrelation function, the long memory question can simply not be answered. Nevertheless, we are able to provide explicit expressions enabling us to analyze thoroughly the dependence structure of the volatility process.”mechanisms[2]
mechanismA WIDELY ACCEPTED STYLIZED FACT IS EXPLAINED AS AN ARTEFACT OF ITS OWN DETECTOR: the model has no long memory property, and yet the classical statistical procedures designed to detect volatility persistence TEND TO CONCLUDE THAT LONG MEMORY IS PRESENT in data the model generated— “Our main finding is that although the RFSV model does not have any long memory property, classical statistical procedures aiming at detecting volatility persistence tend to conclude the presence of long memory in data generated from it. This sheds new light on the supposed long memory in the volatility of financial data.”mechanisms[3]
mechanismthe evidence for the stylized fact is dated rather than dismissed: the analyses supporting it use data that PREDATE high-frequency electronic trading, and the authors note the evidence has never satisfied the remaining doubters— “Nevertheless, analyses such as that of Andersen et al. [3] use data that predate the advent of high-frequency electronic trading, and the evidence for long memory has never been sufficient to satisfy remaining doubters”mechanisms[4]
mechanismTHE NEW NAME IS CHOSEN TO CARRY THE DEPARTURE: an existing model is adopted and renamed, with the new word picked to underline the single parameter inequality that separates it from its parent— “Specifically we adopt the fractional stochastic volatility (FSV) model of Comte and Renault [16]. We call our model Rough FSV (RSFV) to underline that, in contrast to FSV, we take H < 1/2.”mechanisms[5]
mechanismin the process used, ONE PARAMETER CONTROLS BOTH PROPERTIES AT ONCE - regularity and long memory stand in one-to-one correspondence through the Hurst parameter - so a claim about smoothness is inseparably a claim about memory— “Note that in the case of the fBM, there is a one to one correspondence between regularity and long memory through the Hurst parameter H.”mechanisms[6]
mechanismA WIDESPREAD BELIEF IS MET WITH A COUNTEREXAMPLE RATHER THAN AN ARGUMENT: the model is offered as a counterexample to the widespread belief that the explosion of the volatility smile at short expiries implies the presence of jumps— “This is interesting in and of itself in that it provides a counterexample to the widespread belief that the explosion of the volatility smile as τ → 0 (as clearly seen in Figures 1.1 and 1.2) implies the presence of jumps”mechanisms[7]
mechanismTHE MODEL IS OFFERED AGAINST TWO SEPARATE REQUIREMENTS rather than one - consistency with the empirically observed properties of the volatility time series AND consistency with the shape of the volatility surface - and the claim is stated as POTENTIAL, not as achievement— “The volatility model that we will specify in Section 3.1, driven by fBM with H < 1/2, therefore has the potential to be not only consistent with the empirically observed properties of the volatility time series but also consistent with the shape of the volatility surface.”mechanisms[8]
mechanismthe smoothness estimate is reported as a RANGE ACROSS ASSETS rather than a single fitted number, and it is accompanied by two further empirical properties of the increments - approximate normality and a monofractal scaling of the moments - which together are what licenses the modelling choice— “In Section 2, we report our estimates of the smoothness of the log-volatility for selected assets. This smoothness parameter lies systematically between 0.08 and 0.2 (in the sense of H¨older regularity for example). Furthermore, we find that increments of the log-volatility are approximately normally distributed and that their moments enjoy a…mechanisms[9]
builds-on fractional Brownian motion (from mathematics/physics)— “log-volatility behaves essentially as a fractional Brownian motion with Hurst exponent H of order 0.1”claim_relations[0]
builds-on the fractional stochastic volatility (FSV) model of Comte and Renault [16]— “This leads us to adopt the fractional stochastic volatility (FSV) model of Comte and Renault [16].”claim_relations[1]
extends the FSV model of Comte and Renault [16], modified to H < 1/2— “We call our model Rough FSV (RFSV) to underline that, in contrast to FSV, H < 1/2.”claim_relations[2]
contradicts the accepted stylized fact of power-law long memory in volatility— “Once again, our conclusion is that although the (log-)volatility may exhibit some form of persistence, it does not present any long memory in the classical power law sense.”claim_relations[3]
replicates the stylized fact of near-Gaussian log-volatility increments reported by Andersen et al. ([2], [3])— “That the distribution of increments of logvolatility is close to Gaussian is a well-established stylized fact reported for example in the papers [2] and [3] of Andersen et al.”claim_relations[4]
builds-on Fukasawa [25] on the ATM volatility skew generated by fBM-driven volatility— “As already shown by Fukasawa [25], such a model with a small H reproduces the observed decay of at-the-money volatility skew with respect to time to expiry, asymptotically for short times.”claim_relations[5]
senselong memory - SENSE 1, THE ORIGINAL AND LOOSE ONE: slow decay of the autocorrelation function, meaning anything slower than exponential— “Initially, it appears that the term long memory referred to the slow decay of the autocorrelation function (of absolute returns for example), anything slower than exponential”senses[0]
senselong memory - SENSE 2, THE LATER TECHNICAL ONE the field drifted to: a non-integrable autocorrelation function, more precisely one decaying as a power law with exponent below 1, and the recent literature assumes this sense— “Over time however, it seems that this term has acquired the more precise meaning that the autocorrelation function is not integrable, see [8], and even more precisely that it decays as a power-law with exponent less than 1. Much of the more recent literature, for example [7, 11, 13], assumes long memory in volatility in this more technical…senses[1]
sensevolatility - AS A PROXY, ALWAYS: the process is not directly observable, so every quantity here is computed from an estimated daily stand-in taken at a fixed time of day— “Of course, the volatility process is not directly observable, and an exact computation of m(q, ∆) is not possible in practice. We must therefore proxy spot volatility values by appropriate estimated values”senses[2]
senseLocal volatility - exact European-option fit with time-inhomogeneous and unrealistic future dynamics— “In the derivatives world, log-prices are often modeled as continuous semimartingales. For a given asset with log-price Yt, such a process takes the form dYt = µtdt + σtdWt, where µt is a drift term and Wt is a one-dimensional Brownian motion. The term σt denotes the volatility process and is the most important ingredient of the model. In the…senses[3]
senseConventional stochastic volatility - more realistic dynamics without exact consistency with observed European option prices— “In the derivatives world, log-prices are often modeled as continuous semimartingales. For a given asset with log-price Yt, such a process takes the form dYt = µtdt + σtdWt, where µt is a drift term and Wt is a one-dimensional Brownian…senses[4]
senseLocal-stochastic volatility - market hybrid targeting both exact fit and reasonable dynamicssenses[5]
senseImplied volatility - the Black-Scholes volatility input required to reproduce an option's market price
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senses[6]
senseVolatility surface - implied volatility plotted across strike price and time to expiry
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senses[7]
senseShort-maturity volatility-smile explosion - reproducible without jumps when volatility is fractionally rough
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senses[8]
senseRough FSV - fractional stochastic volatility with H below one half rather than the long-memory H above one half
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senses[9]
senseSpot-volatility observation - intrinsically latent and therefore replaced by daily fixed-time proxies
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senses[10]
senseWhole-day realized variance - a regularized spot proxy expected to bias smoothness upward
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senses[11]
senseStationary rough-volatility specification - fractional Ornstein-Uhlenbeck process with a very long reversion scale
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senses[12]
sensePractitioner mean reversion - unusually high volatility tends down and unusually low volatility tends up
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senses[13]
senseWindow-length bias - longer integration raises estimated H and lowers estimated volatility-of-volatility
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senses[14]
senseRFSV scale resemblance - a daily volatility path qualitatively resembles a decade-long path
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senses[15]
senseSpurious memory diagnosis - standard estimators report power-law memory on simulations whose model has none
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senses[16]
senseRFSV forecast parsimony - one H parameter rather than horizon-dependent AR/HAR coefficients
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senses[17]
senseForecast-history rule - past window approximately matches the future horizon
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senses[18]
senseHawkes endogeneity - one order triggers many more as algorithms respond to other participants' orders
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senses[19]
senseOrder splitting - many observed orders belong to a slowly executed metaorder
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senses[20]
senseMicrostructural roughness mechanism - near-critical endogenous feedback plus long-tailed order influence produces a fractional macroscopic limit
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senses[21]
controla detailed test of the model against observed option prices - NOT performed, and deferred to a future article — absent-declared-unreported — rules out the model fitting volatility time series while failing on the option prices it would be used to compute, which is the comparison the paper itself says its predecessors fail
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controls[0]
edgelong memory —operationalised-as→ Hurst parameter
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concept_edges[0]
auditspot volatility —operationalised-as→ realized volatility — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span writes 'daily spot variances' and 'daily realized variance estimates', not 'spot volatility' / 'realized volatility'.
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concept_edges[1]
notestochastic volatility dynamics —contrasted-with→ local volatility dynamics — The stochastic dynamics are said to be more realistic than local-volatility dynamics, though still price-inconsistent.
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concept_edges[2]
notepower-law function of time to expiry —contrasted-with→ sum of decaying exponentials — Observed skew is a power law, in contrast to the conventional model's constant/exponential shape.
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concept_edges[3]
noteRough FSV —same-as→ RFSV — The model name is immediately followed by its acronym in parentheses.
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concept_edges[4]
noteRough FSV (RFSV) —distinct-from→ FSV — The paper explicitly contrasts the rough H below one-half regime with FSV.
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concept_edges[5]
notechoice H < 1/2 —causes→ observed smoothness of the volatility process — The choice enables the model to reproduce the observed smoothness.
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concept_edges[6]
notechoice H < 1/2 —causes→ term structure of volatility skew — The same choice enables agreement with the observed skew term structure.
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concept_edges[7]
noteone given order —causes→ many other orders — The paper states that one order endogenously generates many others.
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concept_edges[8]
auditsuperposition effect —causes→ irregular volatility — Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but 'We believe' the two phenomena lead to the superposition effect; hedged.
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concept_edges[9]
notesuperposition effect —causes→ irregular volatility — The authors say they believe the combined phenomena induce irregular volatility.
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concept_edges[9]
weightsuperposition effect —causes→ irregular volatility — 0.6
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concept_edges[9]
noteRough Fractional Stochastic Volatility (RFSV) model —distinct-from→ FSV model — The models are separated by the explicitly named major difference H below versus above one half.
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concept_edges[10]
evidence status noteconcept — PAGE FURNITURE IS SPLICED INTO THE 'evidence' QUOTE - the text of footnote 1 ('1Actually H corresponds…') and a page number, between 'This implies' and 'that when stated'. The words on either side of it were never continuous on the page; read the quote with it deleted.
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concepts[28]
outcome (stated by the tagger, no quotation)control — absent-declared-unreportedcontrols[0]
for which uselimitation — the scaling result, over the range examined: AT LEAST AT USUAL TIME SCALES OF OBSERVATION
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limitations[0]
for which uselimitation — the smoothness estimates specifically: WE EXPECT ESTIMATES OF THE SMOOTHNESS OF THE VOLATILITY PROCESS TO BE BIASED UPWARDS, INTEGRATION BEING A REG[ularising operation] - the direction of the bias is stated
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limitations[1]
for which uselimitation — every quantity computed in the paper: THE VOLATILITY PROCESS IS NOT DIRECTLY OBSERVABLE, AND AN EXACT COMPUTATION OF m(q, delta) IS NOT POSSIBLE IN PRACTICE
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limitations[2]
for which uselimitation — the option-pricing implications: A MORE DETAILED ANALYSIS OF THE CONSISTENCY OF OUR MODEL WITH OPTION PRICES IS LEFT FOR A FUTURE ARTICLE
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limitations[3]
for which uselimitation — any estimate of long memory in volatility: the asymptotic behaviour of the covariance function cannot be estimated without assuming a specific form for it, so the specification is an input to the result rather than an output
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limitations[4]
for which uselimitation — the debate over whether volatility has long memory: stated in terms of the asymptotic behaviour of the autocorrelation function, the paper says the question simply cannot be answered
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limitations[5]
for which uselimitation — the widely accepted stylized fact that volatility has long memory: classical procedures for detecting volatility persistence conclude it is present in data simulated from a model that has no long memory property at all
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limitations[6]
evidence status notemechanism — Span re-sliced at drafting: the staged text splices footnote 1 AND a page number between 'This implies' and its own subordinate clause ('…see Section 2.1. 3 that when stated in term…'), so the quote begins after the splice. Extraction damage, not authorial. Damage class C.
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mechanisms[2]
evidence status notemechanism — The span contains 'H¨older' for 'Hölder' - the diaeresis is emitted BEFORE its base letter by the PDF extraction. The quote is still an exact substring and anchors correctly; the damage is in what a reader of the tag sees, not in the provenance. None of the four shapes an internal tool looks for covers this, so it is annotated by hand.
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mechanisms[9]
evidence status notesense — PAGE FURNITURE IS SPLICED INTO THE 'evidence' QUOTE - the text of footnotes 13 and 14 and a page number, between 'requires the parameter' and 'H to forecast'. The words on either side of it were never continuous on the page; read the quote with it deleted.
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senses[17]
weightclassical statistical procedures → long memory — 0.8
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unhoused_relations[1]
weightmacroscopic scaling limit of Hawkes processes → integrated fractional process — 0.8
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unhoused_relations[4]
replicationuntested
no type for thisRFSV model → improved forecasts of realized volatility — The RFSV model enables improved forecasts of realized volatility. Enables is absent from the eight governed types and is not direct physical causation.
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unhoused_relations[0]
no type for thisclassical statistical procedures → long memory — Classical persistence diagnostics tend to conclude long memory in data generated by a model that has none. Misidentifies-as is absent from the edge vocabulary.
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unhoused_relations[1]
no type for thisRFSV forecast → AR and HAR forecasts — The RFSV forecast consistently outperforms AR and HAR, especially at longer horizons. Outperforms is absent from the eight types and contrast alone would discard the direction of performance.
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unhoused_relations[2]
no type for thisrelevant regression window → forecasting horizon — The relevant history window is linear in the forecasting horizon. Proportional-to is absent from the governed edge types.
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unhoused_relations[3]
no type for thismacroscopic scaling limit of Hawkes processes → integrated fractional process — The near-critical long-tail Hawkes scaling limit can be seen as an integrated fractional process. Scaling-limit-is-represented-by is absent and a plain is-a would overstate an asymptotic result.
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unhoused_relations[4]
no type for thiscumulated order flow → integrated variance — The paper reports a linear relation between cumulated order flow and integrated variance. Correlates-with/linearly-related-to is absent from the eight types.
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unhoused_relations[5]
limitationthe scaling result is established at usual observation time scales
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limitations[0]
limitationSmoothness estimates based on whole-trading-day realized variance are expected to be biased upwards because integration smooths the process.
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limitations[1]
limitationVolatility is never observed directly, so the key quantity m(q, Δ) can only be computed from proxies.
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limitations[2]
limitationThe paper only treats the volatility time series; whether the model is consistent with observed option prices is deferred.
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limitations[3]
limitationThe property cannot be estimated without first assuming the functional form whose presence is at issue.
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limitations[4]
limitationAs the term is now defined, the field's central question about volatility cannot be answered at all.
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limitations[5]
limitationStandard procedures report the property in data generated by a model that does not have it, which the authors offer as the reason the field accepted it as a stylized fact.
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limitations[6]
Content tags
content-observations: finding · quantity · variable · definition · key-term — grounded in the full text
findingLog-volatility acts essentially like fractional Brownian motion with a Hurst exponent near 0.1 at any reasonable time scale, which leads to the Rough FSV (RFSV) model with H < 1/2.
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content_tags.findings[0]
findingAlthough volatility is NOT long-memory under RFSV, classical persistence tests spuriously detect long memory in RFSV-generated data — explaining why long memory became an accepted stylized fact.
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content_tags.findings[1]
quantityH ≈ 0.1 — Hurst exponent of log-volatility (H<1/2 means rough)
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content_tags.quantities[0]
quantityH = 1/2 — the classical Brownian-motion boundary case
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content_tags.quantities[1]
variableHurst parameter H (independent)
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content_tags.variables[0]
definitionRFSV model
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content_tags.definitions[0]
definitionEVIDENCE CORRECTED (RE-GROUNDED) · fractional Brownian motion
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content_tags.definitions[1]
retracteddefinition — RE-GROUNDED. The old span welded 'model the log of volatility using' - Gatheral's OWN model, a fBM with H < 1/2 - to 'motion (fBM for…' 5,695 characters EARLIER, which belongs to Comte and Renault's H > 1/2 model. It conflated the two Hurst regimes, which is the single distinction this paper exists to draw.content_tags.definitions[1]
key-termsvolatility, fractional, Brownian, Hurst, log-volatility, exponent, memory, realized, rough
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
conceptfractional Brownian motion
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concepts[1]
conceptrealized volatility
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concepts[3]
conceptlong memory
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concepts[5]
conceptHurst parameter
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concepts[6]
conceptspot volatility
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concepts[7]
conceptvolatility surface
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concepts[8]
conceptcontinuous semimartingales — not defined in this paper's own words; standard process class used for derivative log-prices
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concepts[9]
conceptvolatility process — not defined in this paper's own words; time-varying scale term treated as the key model ingredient
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concepts[10]
conceptBlack-Scholes framework — not defined in this paper's own words; framework using constant or deterministic-time volatility
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concepts[11]
conceptdeterministic function of time — not defined in this paper's own words; non-random volatility specification available in Black-Scholes
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concepts[12]
conceptlocal volatility model — not defined in this paper's own words; price-and-time deterministic model calibrated to European options
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concepts[13]
concepttime-inhomogeneous — not defined in this paper's own words; status assigned to local-volatility dynamics
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concepts[14]
conceptfuture volatility surfaces — not defined in this paper's own words; model-generated surfaces criticized as unrealistic
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concepts[15]
conceptexotic options — not defined in this paper's own words; contracts whose local-volatility prices can be off-market
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concepts[16]
conceptstochastic volatility models — not defined in this paper's own words; Brownian-semimartingale volatility models including Heston and SABR
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concepts[17]
conceptcontinuous Brownian semi-martingale — not defined in this paper's own words; process form assigned to conventional stochastic volatility
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concepts[18]
conceptlocal volatility dynamics — not defined in this paper's own words; deterministic price-and-time dynamics contrasted with stochastic dynamics
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concepts[19]
conceptstochastic volatility dynamics — not defined in this paper's own words; more realistic dynamics that still miss observed European prices
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concepts[20]
conceptlocal-stochastic-volatility (LSV) models — not defined in this paper's own words; market-practice hybrids fitting prices while retaining reasonable dynamics
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concepts[21]
conceptreasonable dynamics — not defined in this paper's own words; dynamical behavior jointly targeted with exact market fit
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concepts[22]
conceptslow decay of the autocorrelation function — not defined in this paper's own words; original loose meaning of long memory
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concepts[23]
conceptnot integrable — not defined in this paper's own words; later technical requirement imposed on the autocorrelation function
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concepts[24]
conceptpower-law with exponent less than 1 — not defined in this paper's own words; most precise long-memory definition described
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concepts[25]
conceptasymptotic behavior of the covariance function — not defined in this paper's own words; quantity that requires a specified form to estimate
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concepts[26]
conceptusual time scales of observation — not defined in this paper's own words; finite scales at which volatility autocorrelation is tested
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concepts[27]
conceptdependence structure — not defined in this paper's own words; object analyzed explicitly despite unresolved asymptotic memory
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concepts[28]
conceptimplied volatility — not defined in this paper's own words; Black-Scholes volatility value that matches an option's market price
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concepts[29]
conceptvolatility parameter — not defined in this paper's own words; Black-Scholes input adjusted to reproduce an observed price
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concepts[30]
conceptmarket price of that option — not defined in this paper's own words; calibration target defining implied volatility
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concepts[31]
conceptstrike price — not defined in this paper's own words; one coordinate of the volatility surface
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concepts[32]
concepttime to expiry — not defined in this paper's own words; second coordinate of the volatility surface
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concepts[33]
conceptpower-law function of time to expiry — not defined in this paper's own words; empirical form of the short-maturity skew term structure
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concepts[34]
conceptterm structure of at-the-money (ATM) skew — not defined in this paper's own words; conventional-model prediction contrasted with the observed power law
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concepts[35]
conceptsum of decaying exponentials — not defined in this paper's own words; large-maturity skew shape generated by conventional stochastic-volatility models
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concepts[36]
conceptexplosion of the volatility smile — not defined in this paper's own words; short-maturity option feature not requiring price jumps
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concepts[37]
conceptpresence of jumps — not defined in this paper's own words; widespread explanation countered by fractional-volatility skew
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concepts[38]
conceptATM volatility skew — not defined in this paper's own words; option-surface feature generated by fractional Brownian volatility
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concepts[39]
conceptsmoothness parameter — not defined in this paper's own words; estimated log-volatility regularity lying around 0.08 to 0.2
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concepts[40]
conceptincrements of the log-volatility — not defined in this paper's own words; empirical changes found close to normally distributed
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concepts[41]
conceptapproximately normally distributed — not defined in this paper's own words; distributional regularity of log-volatility increments
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concepts[42]
conceptmonofractal scaling property — not defined in this paper's own words; moment scaling whose smoothness is constant across orders
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concepts[43]
conceptfractional stochastic volatility (FSV) model — not defined in this paper's own words; Comte–Renault model adopted with a rough-H modification
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concepts[44]
conceptRough FSV (RSFV) — not defined in this paper's own words; rough fractional stochastic-volatility model as spelled in the paper's overview
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concepts[45]
conceptRough FSV — not defined in this paper's own words; short model name introduced before the RFSV acronym
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concepts[46]
conceptRFSV — not defined in this paper's own words; abbreviation for the authors' rough fractional stochastic-volatility model
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concepts[47]
conceptFSV — not defined in this paper's own words; fractional stochastic-volatility predecessor with H above one half
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concepts[48]
conceptRough FSV (RFSV) — not defined in this paper's own words; rough model name and abbreviation as printed in the abstract
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concepts[49]
conceptRFSV model — not defined in this paper's own words; rough fractional stochastic-volatility model used for forecasting and diagnostics
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concepts[50]
conceptimproved forecasts of realized volatility — not defined in this paper's own words; forecasting capability attributed to the RFSV model
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concepts[51]
conceptvolatility persistence — not defined in this paper's own words; dependence behavior reexamined through RFSV simulations
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concepts[52]
conceptclassical statistical procedures — not defined in this paper's own words; estimators shown capable of reporting spurious long memory
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concepts[53]
conceptRFSV volatility forecasts — not defined in this paper's own words; model-based forecasts compared with AR and HAR
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concepts[54]
conceptconventional AR and HAR volatility forecasts — not defined in this paper's own words; benchmark forecasting approaches
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concepts[55]
conceptmarket microstructure explanation — not defined in this paper's own words; order-flow account of macroscopic roughness
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concepts[56]
concepthigh degree of endogeneity of the market — not defined in this paper's own words; algorithmic-order feedback component of the proposed explanation
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concepts[57]
conceptalgorithmic trading — not defined in this paper's own words; market automation linked to endogenous order generation
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concepts[58]
conceptintegrated variance — not defined in this paper's own words; short-window quantity used to proxy unobservable spot variance
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concepts[59]
conceptmodel with uncertainty zones — not defined in this paper's own words; high-frequency estimator used for futures data
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concepts[60]
conceptvery short time intervals — not defined in this paper's own words; windows over which integrated variance approximates spot variance
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concepts[61]
conceptdaily realized variance estimates — not defined in this paper's own words; whole-day proxy used for S&P and NASDAQ spot variance
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concepts[62]
conceptOxford-Man Institute of Quantitative Finance Realized Library — not defined in this paper's own words; source of precomputed realized-variance series
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concepts[63]
conceptintegration — not defined in this paper's own words; regularizing operation expected to bias estimated smoothness upward
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concepts[64]
conceptbiased upwards — not defined in this paper's own words; direction of smoothness-estimation bias from whole-day integration
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concepts[65]
conceptsmoothness parameter sq — not defined in this paper's own words; quantity estimated for each moment order by log-log regression
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concepts[66]
conceptregressing log m(q, ∆) against log ∆ — not defined in this paper's own words; empirical procedure for estimating smoothness
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concepts[67]
conceptfractional Ornstein-Uhlenbeck process — not defined in this paper's own words; stationary log-volatility model with locally fractional behavior
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concepts[68]
conceptmathematical tractability — not defined in this paper's own words; one reason stationarity is imposed
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concepts[69]
conceptreasonableness of the model at very large times — not defined in this paper's own words; second motivation for a stationary specification
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concepts[70]
conceptvery long reversion time scale — not defined in this paper's own words; mean-reversion scale chosen for the stationary process
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concepts[71]
conceptchoice H < 1/2 — not defined in this paper's own words; roughness choice enabling joint time-series and skew fit
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concepts[72]
conceptobserved smoothness of the volatility process — not defined in this paper's own words; first empirical feature reproduced by small H
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concepts[73]
conceptterm structure of volatility skew — not defined in this paper's own words; second empirical feature reproduced by small H
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concepts[74]
conceptmean reversion by practitioners — not defined in this paper's own words; tendency of unusually high or low volatility to reverse
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concepts[75]
conceptfBM like-behavior of the log-volatility — not defined in this paper's own words; local behavior retained by taking a small reversion coefficient
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concepts[76]
conceptstationarity property — not defined in this paper's own words; global property combined with local fractional behavior
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concepts[77]
conceptone-hour window — not defined in this paper's own words; uncertainty-zone window yielding a near-true H estimate
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concepts[78]
conceptdaily eight-hour windows — not defined in this paper's own words; realized-variance window yielding a higher H estimate
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concepts[79]
conceptregularizing effect of the integral operator — not defined in this paper's own words; source of upward H bias under longer windows
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concepts[80]
conceptvolatility of volatility — not defined in this paper's own words; interpretation of nu whose estimate falls with longer windows
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[81]
conceptpersistent periods of high volatility — not defined in this paper's own words; visual regime shared by observed and simulated paths
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[82]
conceptlow volatility periods — not defined in this paper's own words; alternating visual regime shared by both paths
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[83]
conceptfractal-type behavior — not defined in this paper's own words; qualitative scale resemblance reproduced by RFSV
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[84]
conceptrange of observation scales — not defined in this paper's own words; interval over which small-H rescaled paths retain similar laws
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[85]
conceptvolatility process over one day — not defined in this paper's own words; short-scale path resembling the long-scale path
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[86]
conceptvolatility process over a decade — not defined in this paper's own words; long-scale path resembling the daily path
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[87]
conceptstraight lines with slope 1.86 — not defined in this paper's own words; simulation-and-data pattern that would imply a spurious memory exponent
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[88]
conceptpower law behavior of the autocorrelation function — not defined in this paper's own words; incorrect inference induced by the first diagnostic
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[89]
conceptwrongly convinced — not defined in this paper's own words; epistemic failure produced when the diagnostic is applied to RFSV-like data
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[90]
conceptclassical estimation procedures — not defined in this paper's own words; procedures collectively shown to identify spurious long memory
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[91]
conceptspurious long memory of volatility — not defined in this paper's own words; false property reported for RFSV simulations
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[92]
conceptclassical power law sense — not defined in this paper's own words; long-memory definition rejected for log-volatility
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[93]
conceptRFSV forecast — not defined in this paper's own words; one-parameter forecast evaluated against AR and HAR
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[94]
conceptAR and HAR forecasts — not defined in this paper's own words; multi-coefficient benchmarks for the RFSV predictor
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[95]
conceptlonger horizons — not defined in this paper's own words; forecast ranges where the RFSV advantage is greatest
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[96]
conceptmore parsimonious — not defined in this paper's own words; one-parameter advantage claimed for the RFSV method
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[97]
conceptforecast time horizon — not defined in this paper's own words; quantity on which benchmark coefficients depend
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[98]
conceptrelevant regression window — not defined in this paper's own words; amount of past volatility required for forecasting
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[99]
conceptforecasting horizon — not defined in this paper's own words; future interval proportional to the relevant history window
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[100]
conceptvolatility over the last week — not defined in this paper's own words; history suggested for a one-week forecast
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[101]
conceptvolatility over the last month — not defined in this paper's own words; history suggested for a one-month forecast
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[102]
conceptHawkes processes — not defined in this paper's own words; self-exciting point processes used to analyze order flow
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[103]
conceptextensions of Poisson processes — not defined in this paper's own words; class description given for Hawkes processes
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[104]
conceptintensity at a given time — not defined in this paper's own words; conditional event rate depending on prior jumps
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[105]
conceptpast jumps — not defined in this paper's own words; history on which Hawkes intensity depends
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[106]
conceptpoint process — not defined in this paper's own words; transaction-count process assumed to follow Hawkes dynamics
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[107]
conceptkernel — not defined in this paper's own words; nonnegative function weighting effects of past jump times
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[108]
conceptL1 norm — not defined in this paper's own words; kernel norm calibrated close to one
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[109]
conceptpower law tail — not defined in this paper's own words; second recurring feature of calibrated Hawkes kernels
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[110]
conceptdegree of endogeneity of the market — not defined in this paper's own words; near-critical feedback implied by the kernel norm
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[111]
conceptone given order — not defined in this paper's own words; order capable of endogenously generating further orders
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[112]
conceptmany other orders — not defined in this paper's own words; endogenous descendants of a given order
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[113]
conceptelectronic high frequency trading — not defined in this paper's own words; algorithmic setting associated with market endogeneity
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[114]
conceptsplitting of large orders — not defined in this paper's own words; explanation offered for long-lived order influence
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[115]
conceptmetaorder — not defined in this paper's own words; large parent order whose execution spans many child orders
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[116]
conceptsuperposition effect — not defined in this paper's own words; combined near-critical and long-tail mechanism proposed to induce roughness
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[117]
conceptirregular volatility — not defined in this paper's own words; macroscopic property induced by the proposed superposition
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[118]
conceptmacroscopic scaling limit of Hawkes processes — not defined in this paper's own words; large-scale limit described as integrated fractional
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[119]
conceptintegrated fractional process — not defined in this paper's own words; limit process with H below one half
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[120]
conceptcumulated order flow — not defined in this paper's own words; large-scale order-flow quantity linked to integrated variance
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[121]
conceptRough Fractional Stochastic Volatility (RFSV) model — not defined in this paper's own words; expanded model name used in the conclusion
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[122]
conceptFSV model — not defined in this paper's own words; long-memory predecessor distinguished by H above one half
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[123]
conceptone major difference — not defined in this paper's own words; opposite Hurst regimes separating FSV and RFSV
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
concepts[124]
Statistics — cross-field join keys
field-invariant statistical terms, used in their statistical sense (gated on a number / symbol / test). A p-value or a standard deviation means the SAME thing in physics, biology, psychology and sociology — so these are the strongest cross-field join keys. Grounded verbatim.
mean μ as written: mean of
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[1]
evidence status notemean — THE QUOTE STARTS INSIDE A FORMULA AND ENDS IN A TABLE: it opens on the tail of the flattened expectation symbol that its sentence defines; this copy places that sentence's last words (the period the mean is taken over) before the quote; and after the quote's last words the text runs into the labels of the forecasting results table.content_tags.statistics[1]
regression β as written: linear regression
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[2]
sample-size n/N
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[3]
evidence status notesample-size — QUOTE REPAIRED: the extractor split a hyphen-space and the quote was recorded faithfully with the damage in it, which made it unanchorable. The repaired string is an EXACT SUBSTRING of the staged text - that is why it was accepted, not because a rule produced it. The text as originally recorded is kept in evidence as extracted.content_tags.statistics[3]
standard-error SE
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[4]
variance σ²
Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher.
content_tags.statistics[6]
evidence status notevariance — A FOOTNOTE CALL SITS INSIDE THIS QUOTE: the '6' between 'the S&P and' and 'NASDAQ indices' is the paper's footnote marker, not part of the sentence; read the quote with it deleted.content_tags.statistics[6]

When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.

Tag layer © the Meta-Tagging Project, CC BY 4.0. The paper text is © its authors/publisher and is linked, not reproduced. Excerpts shown are short quotations for identification and verification. · back to index · concepts · authors · עברית
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