Papers, wiki, Option Blackboard, encyclopedia, and cards.
Complex model suites composed of multiple interacting component models are widely used in financial forecasting and risk management. In model performance testing, including in-sample backtesting (BT) and out-of-sample ongoing performance monitoring (OPM), a material gap between a model-suite forecast and the realized outcome must often be attributed to individual component models for development, validation, and regu…
We identify a robust structural signature of stock markets during exogenous shock events by analyzing collective return dynamics across G5 countries. Using Random Matrix Theory, we introduce the complexity gap, defined as the difference between the normalized largest eigenvalue and the average pairwise correlation, to quantify changes in market structure. This measure reveals a consistent three-phase pattern across m…
Although not a formal pricing consideration, gap risk or hedging errors are the norm of derivatives businesses. Starting with the gap risk during a margin period of risk of a repurchase agreement (repo), this article extends the Black-Scholes-Merton option pricing framework by introducing a reserve capital approach to the hedging error's irreducible variability. An extended partial differential equation is derived wi…
We present a dialogue on Funding Costs and Counterparty Credit Risk modeling, inclusive of collateral, wrong way risk, gap risk and possible Central Clearing implementation through CCPs. This framework is important following the fact that derivatives valuation and risk analysis has moved from exotic derivatives managed on simple single asset classes to simple derivatives embedding the new or previously neglected type…
We develop a continuous-time structural dynamic model to determine the exact insolvency regions of banks arising from the non-linear interaction between liquidity and credit risk. While existing literature predominantly treats these risks in isolation or via reduced-form specifications, we explicitly model the feedback loop where funding shocks and regulatory constraints force balance-sheet adjustments that can lead …
Concentrated Liquidity Market Makers (CLMMs) represent a fundamental innovation in market microstructure, transforming liquidity provision from passive portfolio allocation to active risk management. This evolution creates significant challenges for performance evaluation and strategy optimization, particularly due to the absence of comprehensive historical liquidity data. We address these challenges through a novel …
This study examines how institutional differences and external crises shape volatility dynamics in emerging Asian stock markets. Using daily stock index returns for Indonesia, Malaysia, and the Philippines from 2010 to 2024, we estimate EGARCH(1,1) and TGARCH(1,1) models in a by-window design. The sample is split into the 2013 Taper Tantrum, the 2020-2021 COVID-19 period, the 2022-2023 rate-hike cycle, and tranquil p…
In volatile financial markets, balancing risk and return remains a significant challenge. Traditional approaches often focus solely on equity allocation, overlooking the strategic advantages of options trading for dynamic risk hedging. This work presents DeltaHedge, a multi-agent framework that integrates options trading with AI-driven portfolio management. By combining advanced reinforcement learning techniques with…
This research aims to demonstrate a dynamic cointegration-based pairs trading strategy, including an optimal look-back window framework in the cryptocurrency market, and evaluate its return and risk by applying three different scenarios. We employ the Engle-Granger methodology, the Kapetanios-Snell-Shin (KSS) test, and the Johansen test as cointegration tests in different scenarios. We calibrate the mean-reversion sp…
Intrinsic time is an example of an event-based conception of time, used to analyze financial time series. Here, for the first time, we reveal the connection between intrinsic time and physical time. In detail, we present an analytic relationship which links the two different time paradigms. Central to this discovery are the emergence of scaling laws. Indeed, a novel empirical scaling law is presented, relating to the…
We investigate portfolio optimization in financial markets from a trading and risk management perspective. We term this task Risk-Aware Trading Portfolio Optimization (RATPO), formulate the corresponding optimization problem, and propose an efficient Risk-Aware Trading Swarm (RATS) algorithm to solve it. The key elements of RATPO are a generic initial portfolio P, a specific set of Unique Eligible Instruments (UEIs),…
This paper proposes a Deep Reinforcement Learning algorithm for financial portfolio trading based on Deep Q-learning. The algorithm is capable of trading high-dimensional portfolios from cross-sectional datasets of any size which may include data gaps and non-unique history lengths in the assets. We sequentially set up environments by sampling one asset for each environment while rewarding investments with the result…
Small Medium-sized Enterprises (SMEs) face many obstacles when they try to access credit market. These obstacles are increased if the SMEs are innovative. In this case, financial data are insufficient or even not reliable. Thus, when building a judgemental rating model, mainly based on qualitative criteria (soft information), it is very important to finance SMEs' activities. Until now, there isn't a multicriteria cre…
This paper introduces a novel stochastic model for credit spreads. The stochastic approach leverages the diffusion of default intensities via a CIR++ model and is formulated within a risk-neutral probability space. Our research primarily addresses two gaps in the literature. The first is the lack of credit spread models founded on a stochastic basis that enables continuous modeling, as many existing models rely on fa…
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers the modeling of the liability liquidity risk (or funding liquidity), the second dimension is dedicated to the modeling of the asset liquidity risk (or market liquidity), whereas the third dimension considers the management of the asset-liability liquidi…
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers the asset-liability management of the liquidity gap risk (or asset-liability…
This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers liability liquidity risk (or funding liquidity) modeling, the second dimension focuses on asset liquidity risk (or market liquidity) modeling, and the third dimension considers asset-liability liquidity risk management (or asset-liability matching). The…
Portfolio optimisation is essential in quantitative investing, but its implementation faces several practical difficulties. One particular challenge is converting optimal portfolio weights into real-life trades in the presence of realistic features, such as transaction costs and integral lots. This is especially important in automated trading, where the entire process happens without human intervention. Several works…
Trend and carry on BTC/ETH (and maybe a few alts) using listed or crypto-native perps — a young sleeve with 24/7 gaps and funding.
When too many trend books own the same contract the same way, entries get worse, exits gap, and ‘the CTA unwind’ becomes a flow event.
Crude, products, natgas, and sometimes power or emissions — a complex with storage, geopolitics, and some of the nastiest gaps in the futures world.
Live cattle, feeder cattle, lean hogs — a US-centric complex with biological lags, crush-like feeding margins, and event gaps on USDA.
Implied Realized Spread — Gap between implied and realized vol that defines carry for short-vol books.
Variance Risk Premium — Gap between implied and realized volatility that systematic vol sellers harvest.
Bidless means there is no meaningful posted or workable bid — you can sell only by walking the stairs or waiting, which is how fire sales become prices.
The Taylor rule is a simple policy reaction: set the policy rate to a neutral real rate plus inflation, then add weights on the inflation gap and the output gap.
Current Account Balance — External imbalance measure linking domestic savings-investment gaps to currency pressure.
Output Gap — Estimated distance of GDP from potential output, informing policy reaction functions.
US 10-Year Breakeven Inflation reflects the inflation rate implied by the gap between nominal Treasuries and TIPS, serving as a market-based gauge of long-term inflation expectations.
Barings Bank was wiped out in 1995 by Nick Leeson’s hidden Nikkei futures losses in Singapore — a rogue-trader plus failed control story, not a macro crisis.
Black Monday (19 October 1987) was a one-day ~22% crash in the DJIA, amplified by portfolio insurance — a mechanical selling program that turned a decline into a gap.
LIBOR-OIS spread tracks the gap between unsecured bank funding rates and overnight indexed swap rates, historically serving as a benchmark for banking-system stress.
The bid-ask spread is the gap between the best bid and the best offer — the round-trip tax of crossing the book.
Implementation shortfall is the gap between a decision price (or arrival price) and the actual average execution price, including missed-trade opportunity cost.
A stop-loss becomes a market (or stop-limit) order once a trigger trades — a planned exit that can become a gap-out.
Long undervalued currencies and short overvalued ones versus purchasing-power parity or real-rate gaps — FX value, slow and mean-reverting.
Sell implied volatility and buy realized — harvest the gap that insurance buyers pay, with a jump left tail.
Barings Bank was wiped out in 1995 by Nick Leeson’s hidden Nikkei futures losses in Singapore — a rogue-trader plus failed control story, not a macro crisis.
The bid-ask spread is the gap between the best bid and the best offer — the round-trip tax of crossing the book.
Black Monday (19 October 1987) was a one-day ~22% crash in the DJIA, amplified by portfolio insurance — a mechanical selling program that turned a decline into a gap.
Trend and carry on BTC/ETH (and maybe a few alts) using listed or crypto-native perps — a young sleeve with 24/7 gaps and funding.
When too many trend books own the same contract the same way, entries get worse, exits gap, and ‘the CTA unwind’ becomes a flow event.
Long undervalued currencies and short overvalued ones versus purchasing-power parity or real-rate gaps — FX value, slow and mean-reverting.
Current Account Balance — External imbalance measure linking domestic savings-investment gaps to currency pressure.
Crude, products, natgas, and sometimes power or emissions — a complex with storage, geopolitics, and some of the nastiest gaps in the futures world.
Implementation shortfall is the gap between a decision price (or arrival price) and the actual average execution price, including missed-trade opportunity cost.
Implied Realized Spread — Gap between implied and realized vol that defines carry for short-vol books.
LIBOR-OIS spread tracks the gap between unsecured bank funding rates and overnight indexed swap rates, historically serving as a benchmark for banking-system stress.
Live cattle, feeder cattle, lean hogs — a US-centric complex with biological lags, crush-like feeding margins, and event gaps on USDA.
Output Gap — Estimated distance of GDP from potential output, informing policy reaction functions.
A stop-loss becomes a market (or stop-limit) order once a trigger trades — a planned exit that can become a gap-out.
The Taylor rule is a simple policy reaction: set the policy rate to a neutral real rate plus inflation, then add weights on the inflation gap and the output gap.
US 10-Year Breakeven Inflation reflects the inflation rate implied by the gap between nominal Treasuries and TIPS, serving as a market-based gauge of long-term inflation expectations.
Variance Risk Premium — Gap between implied and realized volatility that systematic vol sellers harvest.
Sell implied volatility and buy realized — harvest the gap that insurance buyers pay, with a jump left tail.
A funding-stress monitor can warn before equity volatility reprices.
A card for handling fast markets, wide spreads, liquidity air pockets, and broken execution assumptions.
Tracks policy reversal probability, market repricing windows, and timing mismatch between rhetoric and positioning.