2019 Q3 Quarterly Research Archive¶
Records scoring at least 40 within the primary scope pass rule review and are published without additional manual review. This page does not validate author claims or provide investment advice.
- Coverage: 2019-07-01 to 2019-09-30
- Passed rule review: 8
- Sources: 6
Topic distribution¶
Domains¶
- volatility: 7
- microstructure: 3
- option returns: 1
- hedging exposure risk: 1
- execution costs: 1
Methods¶
- research methods: 4
- financial ml: 1
Facets¶
- instrument vix options: 2
- instrument index options: 1
- instrument single stock options: 1
Passed rule review¶
Using Machine Learning to Predict Realized Variance¶
- Published: 2019-09-22
- Source: arXiv Quantitative Finance History
- Publication status:
preprint - Original source: Open original source
Why it matters¶
The study explores the use of regularized regression and neural networks to predict realized volatility from option data, aiming to improve the predictability and liquidity of VIX-style indices. The authors find that predicting the difference between realized volatility and the VIX-style index prediction outperforms direct prediction, representing a blend of human and machine learning. This offers a potential pathway for more efficient volatility indexing. (abstract:S1, abstract:S3, abstract:S4)
Main author claims¶
- The authors report: Both Ridge regression and feedforward neural networks can improve volatility indexing with higher prediction performance and fewer options required. (
abstract:S3) - The authors report: Predicting the difference between realized volatility and the VIX-style index's prediction is superior to predicting realized volatility directly, representing a successful combination of human learning and machine learning. (
abstract:S4)
Data, method, or discussion scope¶
The evidence is based on S&P 500 Index and its option data. Algorithms tested include Ridge regression and Feedforward Neural Networks, with performance evaluated via time series validation. (abstract:S2, abstract:S3)
Main limitations¶
The analysis is restricted to S&P 500 index options; its transferability to other markets is unknown. The abstract does not address model stability, hyperparameter sensitivity, or the length of the out-of-sample test period. (abstract:S2)
The impact of net buying pressure on VIX option prices¶
- Published: 2019-09-22
- Source: Journal of Futures Markets
- Publication status:
peer_reviewed - Original source: Open original source
Why it matters¶
The study links VIX-option net buying pressure to implied-volatility changes, next-day hedged returns, and one reported futures-strategy result, making it relevant to whether order flow contains short-horizon information. (abstract:S2, abstract:S3, abstract:S4, abstract:S5)
Main author claims¶
- The authors report: There is a temporal relationship between net buying pressure and changes in VIX option implied volatility. (
abstract:S2) - The authors report: An increase in net buying pressure lowers next-day delta-hedged option returns. (
abstract:S3) - The authors report: A VIX futures trading strategy constructed using net buying pressure generates an annualized return of 10.09%. (
abstract:S5)
Data, method, or discussion scope¶
The study uses intraday trading activity data from the VIX options market to construct net buying pressure measures. It examines their relationship with implied volatility changes, delta-hedged returns, and limits-to-arbitrage proxies. A trading strategy is simulated using VIX futures. (abstract:S1, abstract:S2, abstract:S4, abstract:S5)
Main limitations¶
The study focuses exclusively on VIX options, so its conclusions may not extend to other volatility derivatives or equity options. (abstract:S1)
Implied volatility surface predictability: the case of commodity markets¶
- Published: 2019-09-21
- Source: arXiv Quantitative Finance History
- Publication status:
preprint - Original source: Open original source
Why it matters¶
The paper compares commodity IV-surface models under rolling out-of-sample evaluation and multiple-comparison control, directly addressing whether a model-search winner retains predictive advantage. (abstract:S1, abstract:S2, abstract:S3)
Main author claims¶
- The authors study the most actively traded commodity options from 2006–2016 and compare existing latent-factor and parametric IV-surface frameworks. (
abstract:S1,abstract:S2) - The authors report that, under their rolling out-of-sample and multiple-comparison procedure, Nelson–Siegel term-structure methods are most accurate for energy and precious-metals options. (
abstract:S3)
Data, method, or discussion scope¶
The abstract gives the period, active-commodity scope, evaluation design, and ranking, but not contract definitions, the candidate set, loss function, correction method, or numerical gaps. (abstract:S1, abstract:S2, abstract:S3)
Main limitations¶
“Most accurate” lacks a quantified gap; it is unclear whether multiple-comparison control spans the full search space or whether the result transfers to other commodities and periods. (abstract:S2, abstract:S3)
Multivariate realized volatility forecasts of agricultural commodity futures¶
- Published: 2019-09-06
- Source: Journal of Futures Markets
- Publication status:
peer_reviewed - Original source: Open original source
Why it matters¶
The study extends multivariate HAR for agricultural futures with flexible heteroscedastic structures and heavy-tailed innovations, comparing statistical and economic forecast criteria. (abstract:S1, abstract:S2, abstract:S3, abstract:S4)
Main author claims¶
- The authors propose flexible-heteroscedastic MHAR variants and report better in-sample and out-of-sample performance than benchmark MHAR models. (
abstract:S1,abstract:S3) - The authors report further gains from Bayesian MHAR models with t innovations relative to Gaussian-innovation variants. (
abstract:S4)
Data, method, or discussion scope¶
The abstract identifies agricultural futures, multivariate realized volatility, model families, and comparison direction, but not the sample, contracts, economic metric, priors, loss function, or significance. (abstract:S1, abstract:S2, abstract:S3, abstract:S4)
Main limitations¶
The “economic criteria” and “outperform” claims are not operationalized; gains from heavy tails and covariance choices may depend on the commodity set, priors, and regime. (abstract:S2, abstract:S3, abstract:S4)
HARK the SHARK: Realized Volatility Modeling with Measurement Errors and Nonlinear Dependencies¶
- Published: 2019-08-22
- Source: Journal of Financial Econometrics
- Publication status:
peer_reviewed - Original source: Open original source
Why it matters¶
The paper adds realized-variance measurement error and nonlinear dependence separately to HAR extensions, attempting to distinguish two sources of misspecification. (abstract:S1, abstract:S2, abstract:S3)
Main author claims¶
- The authors propose HARK, SHAR, and SHARK to address measurement error, nonlinearity, or both, combining realized-variance asymptotics with Kalman filtering. (
abstract:S1,abstract:S2,abstract:S3) - The authors report better out-of-sample forecasts than standard HAR and competing approaches in simulated and real data. (
abstract:S4)
Data, method, or discussion scope¶
The abstract lists the extensions, estimation method, and comparison direction, but not the simulation design, real data, frequency, loss function, statistics, or convergence diagnostics. (abstract:S1, abstract:S2, abstract:S3, abstract:S4)
Main limitations¶
“Better” has no magnitude or test threshold, and stability across noise structures, sampling frequencies, and regimes cannot be assessed from the abstract. (abstract:S3, abstract:S4)
Order Cancellations, Fees, and Execution Quality in U.S. Equity Options¶
- Published: 2019-08-07
- Source: The Review of Financial Studies
- Publication status:
peer_reviewed - Original source: Open original source
Why it matters¶
The study uses a PHLX cancellation-fee policy to examine joint changes in order submission, fill speed, and spreads, providing venue-specific evidence for option-market design. (abstract:S1, abstract:S2, abstract:S3, abstract:S4)
Main author claims¶
- The authors report that the fee lowers cancellation rates, discourages non-marketable orders, and increases marketable submissions. (
abstract:S2,abstract:S3) - The authors report higher non-marketable fill rates, slower marketable fills, wider bid–ask spreads, and slight increases in dollar volume and market share. (
abstract:S4,abstract:S5)
Data, method, or discussion scope¶
The abstract is limited to one PHLX policy event and directional results, without the window, identification design, effect sizes, significance, or heterogeneity. (abstract:S1, abstract:S2, abstract:S3, abstract:S4, abstract:S5)
Main limitations¶
“Slight increases” have no economic magnitude; the abstract cannot rule out concurrent market-structure changes or support direct transfer beyond PHLX. (abstract:S1, abstract:S5)
A dimension‐invariant cascade model for VIX futures¶
- Published: 2019-07-21
- Source: Journal of Futures Markets
- Publication status:
peer_reviewed - Original source: Open original source
Why it matters¶
The cascade stochastic-volatility model aims to add volatility components without adding parameters and supplies semi-closed-form VIX-futures pricing, directly targeting dimensionality in calibration. (abstract:S1, abstract:S2, abstract:S3)
Main author claims¶
- The authors state that, under an unspecified minor assumption, arbitrarily many cascade components can be added without extra parameters. (
abstract:S1,abstract:S2) - The authors report that a six-factor, six-parameter version fits spot VIX and VIX futures from 2004–2015, with out-of-sample pricing errors of similar magnitude to in-sample errors. (
abstract:S3)
Data, method, or discussion scope¶
The abstract provides the model structure and a fit summary, but not the key assumption, parameter identification, error metric, out-of-sample split, or benchmarks. (abstract:S1, abstract:S2, abstract:S3)
Main limitations¶
The no-parameter-growth result depends on an unstated assumption, while “closely fits” and error parity have no intervals or regime-stability evidence here. (abstract:S2, abstract:S3)
A Tractable Framework for Option Pricing with Dynamic Market Maker Inventory and Wealth¶
- Published: 2019-07-19
- Source: Journal of Financial and Quantitative Analysis
- Publication status:
peer_reviewed - Original source: Open original source
Why it matters¶
The paper puts limited capital, inventory, and wealth into a dynamic index-option market-maker model, directly linking intermediary constraints to the variance risk premium and option prices. (abstract:S1, abstract:S2, abstract:S3)
Main author claims¶
- The authors solve for the variance risk premium and option prices as functions of asset dynamics, market-maker option holdings, and wealth. (
abstract:S1,abstract:S2) - The authors estimate the model with returns, options, and inventory data and report stronger crisis-period performance and rejection of nested reduced-form restrictions. (
abstract:S4,abstract:S5)
Data, method, or discussion scope¶
The abstract covers the model mechanism, estimation inputs, and comparative conclusion, but not the sample, crisis definition, fit metrics, test statistics, or parameter stability. (abstract:S1, abstract:S2, abstract:S3, abstract:S4, abstract:S5)
Main limitations¶
“Performs well” and the crisis advantage are not operationalized; the abstract cannot assess misspecification, liquidity breaks, or cross-market transfer. (abstract:S4, abstract:S5)