2019 Q2 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: 2019-04-01 to 2019-06-30
- Passed rule review: 4
- Sources: 4
Topic distribution¶
Domains¶
- volatility: 2
- microstructure: 2
- execution costs: 2
- option returns: 1
- hedging exposure risk: 1
- portfolio construction risk transfer: 1
Methods¶
- financial ml: 1
Facets¶
- instrument single stock options: 1
Passed rule review¶
Artur Sepp - Conditional Beta (S2E5)¶
- Published: 2019-06-08
- Source: Flirting with Models
- Publication status:
unknown - Original source: Open original source
Why it matters¶
The podcast description identifies liquidity, option hedging on illiquid underlyings, discrete hedging, and transaction costs as the interview's main themes, helping readers locate topics for deeper listening or follow-up. (description:S4, description:S5, description:S6)
Main author claims¶
- The description attributes to Artur Sepp the view that liquidity is a key factor behind many market risk premia. (
description:S4) - The description says the interview discusses his paper on delta hedging with discrete rebalancing and transaction costs, as well as experience communicating quantitative ideas to clients. (
description:S6,description:S7,description:S8,description:S9)
Data, method, or discussion scope¶
The material is a guest biography and topic preview, not a full transcript, paper, or data analysis; it establishes the discussion themes but not technical details or empirical support. (description:S1, description:S2, description:S4, description:S6, description:S10, description:S11)
Main limitations¶
The statement that liquidity is a key factor is the guest's view and is unsupported within the description; promotional wording cannot be converted into a measurable research finding. (description:S4, description:S12, description:S13, description:S14)
A neural network-based framework for financial model calibration¶
- Published: 2019-04-23
- Source: arXiv Quantitative Finance History
- Publication status:
preprint - Original source: Open original source
Why it matters¶
CaNN reframes high-dimensional financial-model calibration as offline pricing learning followed by online parameter inference, targeting computational bottlenecks and local optima. (abstract:S1, abstract:S3, abstract:S4, abstract:S5)
Main author claims¶
- The authors propose a two-stage ANN calibration process: learn the parameter-to-option-value map offline, then infer input parameters through online backward optimization. (
abstract:S1,abstract:S2,abstract:S3) - The authors state that a parallel global optimizer reduces local-minimum trapping and report fast, accurate calibration for high-dimensional stochastic-volatility models. (
abstract:S4,abstract:S5)
Data, method, or discussion scope¶
The abstract describes the workflow and a numerical-experiment conclusion, but not architecture, training data, benchmarks, error metrics, convergence, or repeated runs. (abstract:S1, abstract:S2, abstract:S3, abstract:S4, abstract:S5)
Main limitations¶
“Avoid local minima” and “efficient and accurate” are unquantified author claims rather than guarantees; transfer across models and noise conditions remains unknown. (abstract:S4, abstract:S5)
Volatility and Expected Option Returns¶
- Published: 2019-04-17
- Source: Journal of Financial and Quantitative Analysis
- Publication status:
peer_reviewed - Original source: Open original source
Why it matters¶
The study gives opposite directional predictions for expected call and put returns as underlying volatility changes, a clear and falsifiable cross-sectional option-return relation. (abstract:S1, abstract:S2, abstract:S4)
Main author claims¶
- The authors report that expected call returns decline with underlying volatility while expected put returns rise. (
abstract:S2,abstract:S3) - The authors state that the relation is not driven by cross-sectional expected stock returns and is robust across maturity, moneyness, volatility measures, and weighting choices. (
abstract:S4,abstract:S5,abstract:S6)
Data, method, or discussion scope¶
The abstract gives directional predictions and a robustness summary, but not the sample period, estimating equations, risk adjustment, controls, or effect sizes. (abstract:S1, abstract:S2, abstract:S3, abstract:S4, abstract:S5, abstract:S6)
Main limitations¶
“Supported” and “robust” are not quantified; the abstract cannot rule out omitted variables or establish behavior in stress periods or after trading costs. (abstract:S3, abstract:S5, abstract:S6)
Volatility Uncertainty, Time Decay, and Option Bid-Ask Spreads in an Incomplete Market¶
- Published: 2019-04
- Source: Management Science
- Publication status:
peer_reviewed - Original source: Open original source
Why it matters¶
The paper explains why option IV bid–ask spreads accelerate near expiry and links a detrended spread measure to future jump intensity, connecting liquidity and jump-risk modeling. (abstract:S1, abstract:S2, abstract:S3, abstract:S7)
Main author claims¶
- The authors document an accelerating IV percentage spread near maturity and explain it with static and multiperiod market-microstructure models. (
abstract:S1,abstract:S2,abstract:S3,abstract:S4) - The authors report empirical support for the model and better explanatory power for future jump intensity from a detrended percentage volatility spread. (
abstract:S6,abstract:S7)
Data, method, or discussion scope¶
The abstract provides the stylized fact, model structure, and empirical claim, but not the sample, estimation, identification, test statistics, or appendix evidence. (abstract:S1, abstract:S2, abstract:S3, abstract:S4, abstract:S6, abstract:S7, abstract:S8)
Main limitations¶
“Confirm validity” and “better explains” have no quantitative criterion here, and the spread–jump relation may vary across venues, assets, and regimes. (abstract:S6, abstract:S7)