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Battery Energy Storage Systems (BESS) are a cornerstone of the energy transition, as their ability to shift electricity across time enables both grid stability and the integration of renewable generation. This paper investigates the profitability of different market bidding strategies for BESS in the Central European wholesale power market, focusing on the day-ahead auction and intraday trading at EPEX Spot. We emplo…
In this paper, we consider a generic interest rate market in the presence of roll-over risk, which generates spreads in spot/forward term rates. We do not require classical absence of arbitrage and rely instead on a minimal market viability assumption, which enables us to work in the context of the benchmark approach. In a Markovian setting, we extend the control theoretic approach of Gombani & Runggaldier (2013) and…
We show that net demand for liquidity by algo strategies is identifiable from its trade and price history alone, with no knowledge of its signal or optimization problem. An exact multi-period regret decomposition implies that the sign of this statistic classifies a linear strategy as a net liquidity consumer or provider, recovering the Kyle (1985) informed-trader/market-maker dichotomy from observables alone. Under a…
We develop a rigorous walk-forward validation framework for algorithmic trading designed to mitigate overfitting and lookahead bias. Our methodology combines interpretable hypothesis-driven signal generation with reinforcement learning and strict out-of-sample testing. The framework enforces strict information set discipline, employs rolling window validation across 34 independent test periods, maintains complete int…
Everlasting options, a relatively new class of perpetual financial derivatives, have emerged to tackle the challenges of rolling contracts and liquidity fragmentation in decentralized finance markets. This paper offers an in-depth analysis of markets for everlasting options, modeled using a dynamic proactive market maker. We examine the behavior of funding fees and transaction costs across varying liquidity condition…
This paper applies deep reinforcement learning (DRL) to optimize liquidity provisioning in Uniswap v3, a decentralized finance (DeFi) protocol implementing an automated market maker (AMM) model with concentrated liquidity. We model the liquidity provision task as a Markov Decision Process (MDP) and train an active liquidity provider (LP) agent using the Proximal Policy Optimization (PPO) algorithm. The agent dynamica…
Recent literature seek to forecast implied volatility derived from equity, index, foreign exchange, and interest rate options using latent factor and parametric frameworks. Motivated by increased public attention borne out of the financialization of futures markets in the early 2000s, we investigate if these extant models can uncover predictable patterns in the implied volatility surfaces of the most actively traded …
MARK J. P. ANSON is affiliated with OppenheimerFunds, Inc., in New York. R ecent academic and practitioner Ž research Schneeweis 1996 ; . Schneeweis and Spurgin 1998 has emphasized the diversification benefits of a wide range of alternative investments including managed futures products as well as hedge funds. Many of these alternative investment products are based on active management strategies that often concentra…
This paper tests whether graph neural networks improve realized volatility forecasts and whether those forecasts improve portfolio performance. Using weekly realized volatility for 465 S&P 500 equities from 2015-2025, Heterogeneous Autoregressive and Long Short-Term Memory baselines are compared against GraphSAGE models built on rolling correlation, sector, and Granger-causal graphs, with and without macro regime fea…
Improvements in return forecast accuracy do not always lead to proportional improvements in portfolio decision quality, especially under realistic trading frictions and constraints. This paper adopts the Smart Predict--then--Optimize (SPO) paradigm for portfolio optimization in real markets, which explicitly aligns the learning objective with downstream portfolio decision quality rather than pointwise prediction accu…
Rolling-window factor pipelines for Chinese A-share markets contain a subtle but costly flaw: daily price-move limits (+/-10% main-board, +/-20% STAR/ChiNext) render a fraction of closing prices non-executable, yet standard implementations ingest these values before any row-filtering runs. The contaminated aggregates propagate silently through moving averages, correlations, and ranks--a failure mode we term "upstream…
In this article, the long-term behavior of the stock market index of the New York Stock Exchange is studied, for the period 1950 to 2013. Specifically, the CRSP Value-Weighted and CRSP Equal-Weighted index are analyzed in terms of market efficiency, using the standard ratio variance test, considering over 1600 one week rolling windows. For the equally weighted index, the null hypothesis of random walk is rejected in …
We propose a unified structural credit risk model incorporating both insolvency and illiquidity risks, in order to investigate how a firm's default probability depends on the liquidity risk associated with its financing structure. We assume the firm finances its risky assets by mainly issuing short- and long-term debt. Short-term debt can have either a discrete or a more realistic staggered tenor structure. At rollov…
Prediction markets are attracting growing attention as trading volumes rise and their practical relevance increases. To ensure efficient price discovery, liquidity provision becomes ever more important. Due to the binary settlement structure in prediction markets, optimal market making leads to an optimization problem that is fundamentally different from the ones studied in classical settings. In this paper, we devel…
Automated market makers (AMMs) for prediction markets descend from market scoring rules, where a mechanism operator subsidizes a market to aggregate beliefs about uncertain events. The existing literature has focused on bounding the total worst-case loss to the subsidizer, but has not addressed how that loss is distributed across price states or over time. We use the framework of loss-versus-rebalancing (LVR) to stud…
Applying historical data from the USD LIBOR transition period, we estimate a joint model for SOFR, Fed Funds, and Eurodollar futures rates as well as spot USD LIBOR and term repo rates. The framework endogenously models basis spreads between each of the benchmark rates and allows for the decomposition of spreads. Modelling the LIBOR-OIS spread as credit and funding-liquidity roll-over risk, we find that the spike in …
We test whether five widely promoted retail signal families - trend, oscillator, candlestick, volume, and calendar rules - deliver a positive, economically meaningful, net-of-cost, and survivable edge. Practical viability is the conjunction of three predeclared gates: statistical edge after multiplicity correction, economic viability after trading costs, and finite-bankroll survival under leverage. Exposure-matched b…
Scaling generative inverse and forward rendering to real-world scenarios is bottlenecked by the limited realism and temporal coherence of existing synthetic datasets. To bridge this persistent domain gap, we introduce a large-scale, dynamic dataset curated from visually complex AAA games. Using a novel dual-screen stitched capture method, we extracted 4M continuous frames (720p/30 FPS) of synchronized RGB and five G-…
QT pace refers to the speed at which the Federal Reserve allows assets to roll off its balance sheet, affecting reserves, duration supply, and market liquidity.
Term premium is the extra compensation investors demand for holding longer-term bonds instead of rolling short-term debt, reflecting duration risk, uncertainty, and market structure.
Nonfarm Payrolls — The headline US jobs report that routinely moves rates, FX, and equity index volatility.
Carry and Roll Down — Expected return from holding higher-yielding tenor as it rolls down a positively sloped curve.
TBA Roll Specialness — Delivery-option value in TBA markets signaling collateral scarcity or abundance.
VIX Futures Term Structure — Curve shape driving roll yield for vol ETNs and systematic short-vol carry.
Volatility Carry Trade — Selling implied vol or rolling VIX futures in contango — crowded but regime-sensitive.
Commodity Carry — Return from rolling futures along the curve — core systematic commodity strategy.
Roll Yield Strategy (Commodities).
VIX Contango Roll (Derivatives).
Short Term External Debt (Emerging Markets).
Layer Two Rollup Risk — Sequencer, bridge, and finality risks in L2 architectures.
Commodity Roll Yield — P&L from rolling futures along a contango or backwardation curve.
CDS Index Roll (Credit).
Roll Down 1M (Fixed Income).
Roll Down 3M (Fixed Income).
Roll Down 6M (Fixed Income).
Roll Down 1Y (Fixed Income).
Roll Down 2Y (Fixed Income).
Roll Down 5Y (Fixed Income).
Roll Down 7Y (Fixed Income).
Roll Down 10Y (Fixed Income).
Roll Down 20Y (Fixed Income).
Roll Down 30Y (Fixed Income).
Roll Down front (Fixed Income).
Roll Down belly (Fixed Income).
Roll Down long-end (Fixed Income).
Roll Down ultra-long (Fixed Income).
Roll Down US (Fixed Income).
Roll Down Euro Area (Fixed Income).
Roll Down UK (Fixed Income).
Roll Down Japan (Fixed Income).
Roll Down China (Fixed Income).
Roll Down EM Asia (Fixed Income).
Roll Down LatAm (Fixed Income).
Roll Down CEEMEA (Fixed Income).
Carry and Roll Down — Expected return from holding higher-yielding tenor as it rolls down a positively sloped curve.
CDS Index Roll (Credit).
Commodity Carry — Return from rolling futures along the curve — core systematic commodity strategy.
Commodity Roll Yield — P&L from rolling futures along a contango or backwardation curve.
Layer Two Rollup Risk — Sequencer, bridge, and finality risks in L2 architectures.
Nonfarm Payrolls — The headline US jobs report that routinely moves rates, FX, and equity index volatility.
QT pace refers to the speed at which the Federal Reserve allows assets to roll off its balance sheet, affecting reserves, duration supply, and market liquidity.
Roll Down 10Y (Fixed Income).
Roll Down 1M (Fixed Income).
Roll Down 1Y (Fixed Income).
Roll Down 20Y (Fixed Income).
Roll Down 2Y (Fixed Income).
Roll Down 30Y (Fixed Income).
Roll Down 3M (Fixed Income).
Roll Down 5Y (Fixed Income).
Roll Down 6M (Fixed Income).
Roll Down 7Y (Fixed Income).
Roll Down agency (Fixed Income).
Roll Down Australia (Fixed Income).
Roll Down belly (Fixed Income).
Roll Down Canada (Fixed Income).
Roll Down CEEMEA (Fixed Income).
Roll Down China (Fixed Income).
Roll Down EM Asia (Fixed Income).