Research
Every research paper we have written up, newest paper first. New arrivals are screened automatically for topical fit with systematic trading; the ones that clear it are read, written up, and — where the idea is testable on our data — backtested. 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.
16,915 papers screened · 588 on the radar · 68 shown
Indonesia's Islamic capital market has grown rapidly, with PT Bank Syariah Indonesia Tbk (BSI) emerging as one of its actively traded Sharia-compliant issuers.
Worst-case risk evaluation is central to finance and operations research, yet existing analytical results remain fragmented and largely confined to ambiguity sets with specific structures.
OUR BACKTEST · Sharpe 0.67 · Return +44.6% · Max DD -28.9%
The rapid advancement of generative artificial intelligence has catalyzed a paradigm shift in quantitative finance, transitioning from traditional point forecasting toward comprehensive financial time-series generation (FTSG) across extrapolation, imputation,…
Estimating extreme risk in reinforcement learning becomes increasingly difficult as the confidence level increases, since only a small number of observations contribute to empirical estimates of Conditional Value-at-Risk (CVaR).
PAPER REPORTS · Simulated Merton jump-diffusion, K=140 call, 63 daily steps; transaction costs not mentioned. · Same setting, EVT-trained policy hedging error on 3,000 paths conditional on unhedged payoff > 0.999 quantile: mean…
This paper studies continuous-time portfolio selection under Range Value-at-Risk (RVaR), with both portfolio performance and risk evaluated in terms of logarithmic returns.
OUR BACKTEST · Sharpe 0.18 · Return +15.4% · Max DD -38.2%
Value-at-Risk (VaR) is a widely adopted quantile-based risk measure in practice, yet incorporating VaR objectives into Markov decision processes (VaR MDPs) remains a longstanding challenge.
PAPER REPORTS · Simulated alpha-VaR of total execution revenue, dynamic vs. optimal static strategy (Regime 2, Almgren-Chriss…
Abstract Evaluating systemic risk transmission across financial and commodity markets is vital for maintaining macroeconomic stability in hydrocarbon-dependent economies.
Pergerakan harga emas yang dipengaruhi oleh volatilitas pasar dan ketidakpastian ekonomi memerlukan metode peramalan serta analisis risiko yang akurat.
PAPER REPORTS · ARIMA(1,1,1) price forecast: MAPE 9.15%, MAE USD 396.14, RMSE USD 454.31, directional accuracy 100%. · MC GBM Geopolitik scenario: MAPE 10.37%, MAE USD 426.43, RMSE USD 465.29, directional accuracy 100%.
OUR BACKTEST · Sharpe 0.01 · Return +0.6% · Max DD -35.1%
This paper studies decision functionals under ambiguity when pure-risk evaluation is determined by a general, possibly non-affine statistic.
OUR BACKTEST · Sharpe 0.54 · Return +30.9% · Max DD -25.2%
We develop a data-driven approach to dynamic portfolio selection that combines vine-copula simulation with reinforcement learning.
PAPER REPORTS · NN-vine-RL ensemble, 22 complete monthly holding periods within 31 Jul 2024–30 Jun 2026 decisions: CAGR 34.13%, net of… · Sharpe ratio 2.309 (zero annual risk-free rate), same sample and costs.
Expected Shortfall (ES) measures the average size of tail losses. Its sequential backtests typically also require a Value-at-Risk (VaR) forecast.
A conditional normalizing flow can produce extreme scenarios even when its point forecasts appear reasonable. We compare two fitted models for daily Treasury yield changes at eleven maturities reconstructed from Federal Reserve curve parameters.
PAPER REPORTS · Stabilized flow, development windows (5×60 dates, 2002–2026; forecast metrics only, no trading strategy or costs):… · Stabilized flow, subsequent window April 13–July 8, 2026 (frozen checkpoints): scenario-mean MSE 0.3351, 90% coverage…
OUR BACKTEST · Sharpe 0.03 · Return +0.2% · Max DD -5.6%
PAPER REPORTS · AIM_PU_CVaR (real data, daily, 2020-01-01 to present, T=1471 days): Sharpe ratio 0.7718; annualization not stated; no… · AIM_PU_CVaR: total reward 1.5048, mean daily return 0.0010, total CVaR-like cost 0.0194, 23 crisis days (>5% daily…
OUR BACKTEST · Sharpe 0.98 · Return +61.8% · Max DD -22.0%
We examine climate variables in a pipeline for dynamic Value-at-Risk (VaR) of the SRI-KEHATI ESG index and the IHSG (Jakarta Composite Index).
PAPER REPORTS · Main QRNN backtest, test N=300 (Nov 2023-Dec 2025), VaR violation rates and Kupiec p: SRI-KEHATI tau=0.01 0.33% (1… · Main QRNN DM statistics on pinball loss (positive = QRNN better), all p<0.001. Vs HS-VaR: SRK 4.31 / 6.43 / 7.23 and…
A realized tail-risk conditional autoregressive expectile (RTCARE) model is proposed for joint forecasting of value-at-risk (VaR) and expected shortfall (ES).
PAPER REPORTS · RTCARE out-of-sample mean FZ0 loss (IBM, 3513 rolling one-day forecasts within 2005-2024): 1.100 (5%), 1.379 (2.5%),… · RTCARE NO INT out-of-sample mean FZ0 loss: 1.097 (5%), 1.372 (2.5%), 1.769 (1%), 2.057 (0.5%)
OUR BACKTEST · Sharpe 1.82 · Return +9.2% · Max DD -6.2%
Renewed attention to the identity of Bitcoin’s pseudonymous creator has revived an old worry: that the roughly 1.148 million BTC mined by Satoshi and never moved represent a major tail risk for Bitcoin. This paper argues that the worry is overstated.
Expectiles are the only law-invariant risk measures that are both coherent and elicitable. Unlike Conditional Value-at-Risk (CVaR), however, they do not admit a Rockafellar--Uryasev representation that admits tractable Wasserstein reformulations.
PAPER REPORTS · Proposed model, (rho, lambda)=(0.005, 0.10), FTSE 90 stocks, 3,341 OOS days 2012-2026, gross of transaction costs: ann. · Proposed model, (0.001, 0.10), same sample, gross: ann. return 20.62%, ann. vol 20.52%, Sharpe 1.005, MaxDD 44.41%,…
Risk evaluation under distributional ambiguity is central to decision making in finance, economics, and operations research. Wasserstein balls provide a natural way to describe uncertainty around a reference distribution.
OUR BACKTEST · Sharpe 0.58 · Return +43.5% · Max DD -29.9%
In this paper, we present robust portfolio selection models by incorporating a reward and penalty mechanism into portfolio management.
OUR BACKTEST · Sharpe 0.97 · Return +77.6% · Max DD -27.8%
Deep hedging learns trading policies from historical or simulated market trajectories, yet under nonstationarity these training paths may not represent future market conditions.
PAPER REPORTS · Nonstationary Heston (simulated), European call, CVaR_0.95 of hedging loss, no transaction costs: WRAP-W far-future… · Nonstationary Heston (simulated) with proportional costs c=0.005: WRAP-U far-future CVaR 8.349±0.189 vs ERM-U…
Entropic risk optimisation is a general framework for pricing and hedging financial derivatives in incomplete markets that can be used to decompose P&L into market and model risk contributions.
Taiwan’s central role in global semiconductor manufacturing exposes Taiwan-related ETFs to technology concentration, geopolitical uncertainty, and supply-chain disruptions, resulting in return distributions characterized by heavy tails, volatility clustering,…
OUR BACKTEST · Sharpe 0.37 · Return +28.9% · Max DD -34.3%
Deep reinforcement-learning policies for order execution can vary substantially across training seeds, so apparent architectural gains may reflect favourable training realisations rather than reproducible properties of the architecture.
PAPER REPORTS · Mean implementation shortfall, out-of-sample Jan 16–20 2023, frictionless replay with no commissions or market impact,… · Per-run CVaR-95 under the same setup: DDQL 110.8, MoE K=8 122.2, DDQL-cap8 104.1 bps.
Abstract Estimating value-at-risk on time series data with potentially heteroskedastic dynamics is a highly challenging task.
PAPER REPORTS · LSTM VaR estimator, Fama-French 25 BM/OP portfolios, VaR 1%, n=250, test period ending 08.03.2019 (~500 backtest days):… · LSTM VaR estimator, Fama-French 25 portfolios, VaR 1%, n=250, test period ending 28.02.2022 incl.
The increasing volatility of electricity prices driven by renewable energy integration, market shocks, and regulatory changes has reinforced the need for forecasting methods that go beyond point predictions and accurately describe the full conditional price…
PAPER REPORTS · JSU(4), the paper's preferred specification: average MAE 19.39 (SD 0.12) over the 24 hourly series. · JSU(4) average pinball loss: tau=0.99 1.29, tau=0.95 3.41, tau=0.05 3.53, tau=0.01 1.13. Same evaluation setup.
We develop a spouse-protected tontine in which a first death changes the household state but generates no pool transfer.
PAPER REPORTS · Out-of-sample learned policy, payment-seeking (λ=0.3): expected cumulative real payments 1860.4 (thousand AUD over 35… · Out-of-sample learned policy, intermediate (λ=1): expected cumulative real payments 1704.8, CVaR_0.95 terminal…
Bank treasury portfolios must balance yield, liquidity, and interest-rate risk across bonds of different maturities.
PAPER REPORTS · DP mean terminal wealth over 6 months, starting from 1 (simulated DNS-VAR chain, unconditional-mean initial curve):… · DP mean terminal wealth over 12 months: 1.1801 at c=0; 1.1166 at c=0.01.
We introduce a deep kernel hedging framework that combines the flexibility of deep learning with the structural inductive bias of kernel methods.
PAPER REPORTS · S&P500 out-of-sample (Jan 1 2023 to May 31 2026), ATM European call, quadratic hedging with order-3 signature features,… · S&P500 out-of-sample (Jan 1 2023 to May 31 2026), ATM European call, CVaR hedging, frictionless.
OUR BACKTEST · Sharpe 2.30 · Return +22.0% · Max DD -2.8%
The internal-models approach of the Fundamental Review of the Trading Book (FRTB-IMA) determines market-risk capital at the aggregate level of a bank's approved trading desks, but it does not say how that charge should be attributed to individual trades.
This article is concerned with the asymptotic shape of quantile surfaces, defined as the set of quantiles at a given level $α$ generated by a controlled one-dimensional distribution.
OUR BACKTEST · Sharpe 0.53 · Return +55.1% · Max DD -40.5%
Volatility forecasts play a central role in financial risk management because their overall level and day-to-day movements affect downstream decisions. Most studies compare forecasting models while keeping the training loss fixed.
Catastrophe (cat) bonds overcome some lack of reinsurance by sourcing capacity from the wider capital markets. We present a new type of cat bond addressing the known trade-off between moral hazard and basis risk.
PAPER REPORTS · Cat bond tranche A (attachment 7.861e9 = modeled 100-yr, exhaustion 11.008e9 = modeled 200-yr), extremal-t Cauchy… · Tranche B (attachment 9.593e9 = modeled 150-yr, same exhaustion), same parameters: price 100.69 EUR; par-implied spread…
This paper develops inference for a Gaussian-nested hypergeometric family of distribution functions.
We develop a diffusion-model framework for dynamic implied-volatility surface generation and evaluate its economic usefulness through data-driven hedging. The framework consists of two models.
PAPER REPORTS · AD-Seq-Vol hedging, held-out 1 Jul 2018 - 28 Feb 2023, transaction costs included as half bid-ask spread in the… · AD-Seq-Vol, same period, COVID window (13 Feb - 21 Jul 2020) excluded: mean 0.78, median 0.02, std 8.33, 5% VaR 10.72,…
Abstract Whether richer information systematically improves portfolio allocation remains an open question in extreme-risk digital asset markets.
PAPER REPORTS · Gross of costs (2021-01-01 to 2024-03-31, out-of-sample rolling windows): GBRT+MV cumulative return 4.978, annual SD… · Gross: RF+MV return 4.806, SD 1.195, Sharpe 4.020; RF+MMV return 4.944, SD 1.386, Sharpe 3.567
Abstract This paper investigates the pricing of ESG momentum in corporate bond markets, identifying a credibility barrier that conditions whether sustainability improvements are rewarded by investors.
PAPER REPORTS · Long-short ESG momentum quintile spread: mean 0.926 bps per month, t = 1.302, p = 0.196, Dec 2014 - Apr 2025 (114… · Conditional ESG momentum marginal effect of 0.877 bps per month (95% CI [0.292, 1.462]) for issuers excluded by 5+…
We develop Entropic Value-at-Risk (EVaR) parity for tempered stable returns. EVaR-based inverse risk parity (IRP) and equal risk contribution (ERC) portfolios are constructed using multivariate normal tempered stable models and independent component analysis…
PAPER REPORTS · SECTOR EVaR-ERC ICA+NTS: cumulative return 853.79%, Sharpe 0.588, max drawdown 48.56%, turnover 6.93% (out-of-sample… · SECTOR EVaR-ERC ICA+CTS: cumulative return 821.40%, Sharpe 0.579, MDD 48.39%, turnover 9.49% (gross)
OUR BACKTEST · Sharpe 0.37 · Return +14.3% · Max DD -22.0%
Modeling the joint distribution of extreme values in high-dimensional financial time series is challenging because extremes are sparse and locally extreme observations are not necessarily extreme relative to their full marginal distribution.
PAPER REPORTS · Out-of-sample average log score (lower better), healthcare 2021–2024: HR JEAM p75 w5 = 2.878 lower tail, 3.189 upper… · Out-of-sample average log score, finance: HR JEAM p75 w5 = 3.023 lower tail, 3.367 upper tail vs baseline 3.478 / 3.812
We develop a theoretical model that endogenizes the regulator's decision to impose short-selling bans to prevent large stock price declines.
Decentralized finance (DeFi) lending has grown from nonexistent in 2017 to nearly 40 billion US Dollars in deposited funds in May 2022.
Modern portfolio theory identifies diversification as the primary tool for risk reduction. However, under model uncertainty, this cornerstone may no longer remain optimal.
OUR BACKTEST · Sharpe -0.52 · Return -29.5% · Max DD -24.9%
This article investigates the risk exposure of eight Central and Eastern European markets using monthly data.
Abstract This study develops a robust framework for modeling dynamic volatility, asymmetry, and tail dependence in financial returns, focusing on the daily returns of Natural Resource Index () and the Oil and Gas Index ().
OUR BACKTEST · Sharpe 0.47 · Return +42.5% · Max DD -27.9%
Abstract Value-at-Risk (VaR), the most widely used measure of market risk, is typically evaluated through backtesting of point forecasts. Such procedures, however, say little about the uncertainty of the estimated quantile.
We establish the consistency and asymptotic normality of a two-step estimator of conditional expectiles in the context of conditional scale models.
Similar to banks, DeFi protocols expose depositors to operational risk (USD 9.45 billion across 1,075 events since 2020). Unlike banks, they are not required to hold capital against it. A protocol may maintain a buffer voluntarily.
We introduce ISCOS, a cross-entropy importance-sampling calibration method for rare credit-portfolio losses. We derive Gaussian and Gaussian--inverse-Gamma proposals and analyse the propagation of finite-COS approximation errors to the fitted parameters.
Abstract The emergence of cryptocurrencies has presented investors with novel portfolio diversification opportunities.
PAPER REPORTS · Unconstrained mean-CVaR with crypto: mean monthly return 2.63%, mean monthly CVaR 1.15%, mean monthly risk-return ratio… · Unconstrained without crypto: mean monthly return 0.46%, CVaR 0.42%, risk-return ratio 1.09%
Abstract Precious metals historically have been adopted as an effective hedging instrument by investors due to their price dynamics shaped in line with economic and financial risks.