Research
A continuously updated feed of research papers that pass our automated relevance screening for systematic trading — plus every paper we have published a review of, whatever it scored. Particular focus on alpha hypotheses that can be formalised and tested. The Radar also covers portfolio construction, market risk and execution where the research is directly relevant to systematic investment processes. Follow new entries by RSS.
15,697 papers screened · 250 on the radar · 41 shown
We compare the Heston model with $ρ=-1$ to the one-dimensional local-volatility model calibrated to the same European option prices.
We study deep hedging in the context of dynamics risk measures, where sequential decisions are time-consistent.
PAPER REPORTS · Terminal hedging loss CVaR95% at 1-year maturity, 10,000-path test set with initial state perturbation, 0.1%… · Mean terminal P&L, same setup (alpha=95%): log -1.1143 (std 1.7095), static +0.2161 (std 2.6423)
This paper proposed a new model to price a stock option based on the Skewed Laplace distribution approach (SLOP).
PAPER REPORTS · MSE of SLOP 65.6667 vs MSE of BSOP 87.1059 across all 44 contracts (May 14, 2018 - May 14, 2019 sample for volatility;… · APE of SLOP 0.0425 (underpricing) vs APE of BSOP -0.1929 (overpricing), same sample
We study whether nuclear and energy-adjacent equity options exhibit a harvestable variance risk premium. Using CRSP and OptionMetrics data for 2000-2024, we construct a systematic cash-secured short-put strategy on a curated universe of nuclear-related firms.
PAPER REPORTS · EW put unconditional, 2000-2024 (300 months): 18.7% annualized return, 2.4% annualized volatility, Sharpe 7.81, MaxDD… · CAP-10 unconditional: 18.6% return, 2.4% vol, Sharpe 7.79, MaxDD 0.0%
We propose a deterministic numerical method for pricing and hedging surrenderable equity-linked life-insurance contracts with periodic premiums and fund contributions, maturity and death guarantees, and Bermudan surrender under correlated stochastic…
We investigate arbitrage in a discrete-time financial market model where, in addition to finitely many dynamically traded assets, there are also static options to choose from.
OUR BACKTEST · Sharpe 0.35 · Return +21.4% · Max DD -34.1%
We propose a neural calibration method to construct a recombining binomial tree directly from a set of given option prices.
Classical option-hedging methods like Black-Scholes delta assume constant, free rebalancing, which real markets don't allow. Deep hedging trains a neural network to handle these frictions directly, and prior work reports strong results.
PAPER REPORTS · Whalley-Wilmott (paper's best strategy), test period Sep 2023-Dec 2024, 11,546 episodes, 5bp round-trip cost: mean… · Whalley-Wilmott cost saving vs BS delta: -$1.79 per episode, 95% CI [-2.21, -1.39], p < 0.0001 (test period, 5bp cost)
The factor HJM stochastic volatility model introduced by Sepp and Rakhmonov (2025) obtains tractable swaption pricing by freezing the nonlinear swap-rate loading along a deterministic expected-state path.
Haug and Haug extend the Margrabe exchange option by adding knock-in and knock-out provisions written on the ratio of two asset prices. This paper applies and develops their framework for stock-for-stock takeover bids with collars.
This paper studies European option pricing in a regime-switching Heston-Hull-White framework.
PAPER REPORTS · In-sample (train, 2 Jan-6 Aug 2024, 8,286 obs) DL-RS-HHW pricing error: RMSE 0.0072, MAE 0.0050 (option prices in yuan;… · Out-of-sample (test, 7 Aug-30 Sep 2024, 1,234 obs) DL-RS-HHW pricing error: RMSE 0.0179, MAE 0.0097
Bitcoin inverse options, traded on the Deribit exchange and settled in the underlying cryptocurrency rather than in fiat currency, combine extreme and genuinely rough volatility dynamics with a non-linear, currency-dependent payoff structure.
We develop a PDE-based methodology for pricing and hedging European contingent claims in general one-dimensional diffusion markets characterized solely by their scale function and speed measure, possibly without a classical SDE representation, and with…
PAPER REPORTS · Bachelier (premium 2.0), N_MC=2000, N^space_FD=4000, T=10, no transaction costs: MTE* −0.002 ± 0.008 and StDTE* 0.192 ±… · Skew-Sticky 1 (premium 0.271, κ₋₁=0.3, κ₁=0.7, ρ=1, r=0.2, ELMM exists): MTE* 0.058 ± 0.022 and StDTE* 0.506 ± 0.024 at…
This paper develops the first end-to-end application of cross-sectional learning-to-rank to the S&P 500 weekly options (SPXW) zero-day-to-expiration surface, integrated with margin-aware position sizing, an abstention rule driven by model uncertainty, and a…
PAPER REPORTS · Out-of-time 2025 annualized Sharpe 4.308 to 5.761 across seven sizing methods, net of Reg-T margin, tiered IBKR fees,… · Headline Edge Allocation OOT 2025: Sharpe 5.7612, Sortino 7.0291, annualized return 10.48% (excess of risk-free),…
Option prices are prices of insurance, so the risk-neutral probabilities they imply overstate physical crash risk. A power utility pricing kernel undoes the premium.
Implied volatility surfaces summarise the option market and are central to many financial applications.
PAPER REPORTS · Surface point-forecast RMSE aggregated over 30 horizons: 0.01262 vs persistence 0.01343, +6.09% gain; MAE gain +3.45%;… · h=1: RMSE 0.00619 vs persistence 0.00535 (-15.72%); MAE -34.49%
OUR BACKTEST · Sharpe -0.00 · Return -1.0% · Max DD -329.1%
W-shaped smiles appear in near-expiry options around binary events such as earnings, and have been associated with bimodal risk-neutral densities. The three-parameter eSSVI slice cannot produce them.
Basket options are difficult to value under correlated lognormal dynamics because weighted sums and differences of lognormal variables have no tractable distribution.
Deep hedging is a data-driven approach to learn hedging strategies. It relies on synthetic price paths generator, as real market data is often limited for training.
OUR BACKTEST · Sharpe 1.78 · Return +5.9% · Max DD -0.6%
The Marketron model of \cite{HalperinItkin2025Mark} and its option pricing extension in \cite{HalperinItkinMarketron2} suffer from structural non-identifiability: an eighteen-parameter space traps solvers in suboptimal local minima and renders economic…
Hedge ratios, factor models and diversified portfolios all rest on an estimate of which firms move together.
PAPER REPORTS · Variance-harvest attribution (selling variance at VIX-squared against the paper's 12m equal-weighted realized leg, July… · Risk by REC quartile over the same 329 months: probability of loss 0.29, 0.21, 0.26, 0.15; mean loss given loss…
OUR BACKTEST · Sharpe 0.73 · Return +68.2% · Max DD -36.5%
Abstract Market timing models aim to anticipate short-term market movements according to a given source of information. Such information could be extracted from an analysis of history or a forecast of the future.
PAPER REPORTS · S&P500 timing, 2018: index -6.7% annualized; Strat1-L 3.8%, Strat1-LS 14.3%, Strat2-L 0.8%, Strat2-LS 8.2% (no… · S&P500 timing, 2023: index 21.6%; Strat1-L 21.9%, Strat1-LS 22.1%, Strat2-L 25.5%, Strat2-LS 29.4% (no transaction…
OUR BACKTEST · Sharpe 0.61 · Return +49.0% · Max DD -31.2%
Against the background of increasing volatility and complex risk factors in global markets, options and futures have become important instruments for risk hedging and uncertainty management.
PAPER REPORTS · Futures hedged portfolio, 2019-2024: annualized return 7.95%, annualized volatility 10.28%, hedging efficiency 46.87%,… · Option hedged portfolio, 2019-2024: annualized return 8.31%, annualized volatility 8.76%, hedging efficiency 54.73%,…
OUR BACKTEST · Sharpe 0.74 · Return +83.0% · Max DD -47.8%
This study examines the dynamic effects of monetary policy changes on derivatives pricing behavior, emphasizing applications in financial risk management for industrial commodities.
Narrow Uniswap v3 liquidity ranges resemble short dated options, and Panoptic's streaming premium echoes the short maturity concentration of Black-Scholes theta near the strike.
We develop a geometric theory of arbitrage-free implied variance surface dynamics.
PAPER REPORTS · Out-of-sample RMSE(delta a2) improvement of full (beta,eta,psi) model over SSR-only: 17-21% at 3M-6M (215.7 vs 272.8 at… · Out-of-sample RMSE(delta a1) improvement of adding eta: 1-4% versus SSR-only at 1M-6M, essentially flat at 12M
OUR BACKTEST · Sharpe -0.84 · Return -0.1% · Max DD -0.1%
This article presents with DYSANOS the first generative market model for smooth SANOS option surfaces for all strikes and expiries which are free of static arbitrage.
OUR BACKTEST · Sharpe -0.19 · Return -0.0% · Max DD -0.1%
Our primary goal is to forecast and empirically examine the evolution of the implied volatility (IV) surface, with particular focus on the dates of scheduled meetings of the Federal Open Market Committee (FOMC).
PAPER REPORTS · h=1 out-of-sample RMSE, calls, ConvLSTM on SVI surface without dummy: 0.085 (sd 0.003) vs random walk 0.092; test year… · h=1 out-of-sample RMSE, puts, ConvLSTM on SVI surface without dummy: 0.077 (sd 0.003) vs random walk 0.084; test year…
Thousands of SOFR derivatives are available in exchanges and OTC, but the market remains illiquid and incomplete.
Hawkes-based microstructural foundations for rough volatility, leverage, and rough Heston-type limits were developed by El Euch et al.
Control policies optimized in simulation can perform poorly in the real system when the parameters $x$ of the simulator are estimated from limited data but the resulting parameter uncertainty is not represented inside the simulation.
PAPER REPORTS · In-simulator spectral risk (x100, warm-up H=T), BS-VOL: RLM-trained policy 10.15 on RLM paths and 10.33 on SLM paths;… · In-simulator spectral risk (x100, warm-up H=T), HESTON-CORR: RLM policy 19.87 (SLM paths) / 19.93 (RLM paths) vs SLM…
OUR BACKTEST · Sharpe -0.18 · Return -66.9% · Max DD -81.1%
This paper develops a unified mathematical theory of implied, local, and learned volatility surfaces.
Fractional Brownian motion (fBm) exhibits attractive features for financial modeling, including long-range dependence, path roughness, and anomalous diffusion.
OUR BACKTEST · Sharpe 0.23 · Return +8.6% · Max DD -6.9%
We present a novel application of Neural Networks with Local Converging Inputs (NNLCI) to improve the efficiency of existing numerical methods for pricing multi-asset options.
Local-stochastic volatility (LSV) combines vanilla marginals with richer smile dynamics, but calibration requires a slow, noisy and sequential McKean--Vlasov fixed point. We learn a projection-consistent operator for the calibration triple.
PAPER REPORTS · Calibration latency 0.60 ms/surface vs 98.5 ms particle baseline (paired, same hardware, synthetic held-out states) · Vanilla repricing RMSE 58.2 +/- 3.3 bps on 8 held-out surfaces, 2 seeds (spread across seeds, not a confidence…
OUR BACKTEST · Sharpe 0.53 · Return +5.6% · Max DD -2.0%
We propose the VIX-derived volatility (VDV) model, a VIX-first framework for joint SPXVIX modeling.
We propose an arbitrage-aware latent flow-matching framework for unconditional implied volatility surface generation.
OUR BACKTEST · Sharpe -0.06 · Return -1.2% · Max DD -5.1%
Implied volatility surface forecasting is essential for option valuation, hedging,and risk management, but remains difficult because future surfaces are stochastic while pricing inputs must satisfy static no-arbitrage shape restrictions.
This note studies the conditional-density equation and its pathwise transformation in local stochastic rough volatility models, with rough Heston (rHeston) as the main explicit example.
OUR BACKTEST · Sharpe -0.45 · Return -4.2% · Max DD -7.5%
Gerhold and Gülüm derived necessary calendar-vertical-basket conditions for finite call bid-ask quotes when the cash-settlement reference price lies inside a dynamically traded stock spread of bounded absolute width.
The Gasoil options market is illiquid, making it difficult to construct its implied volatility surface directly. However, it is closely linked to the highly liquid Brent options market.