Papers, wiki, Option Blackboard, encyclopedia, and cards.
We conclude from an analysis of high resolution NYSE data that the distribution of the traded value $f_i$ (or volume) has a finite variance $σ_i$ for the very large majority of stocks $i$, and the distribution itself is non-universal across stocks. The Hurst exponent of the same time series displays a crossover from weakly to strongly correlated behavior around the time scale of 1 day. The persistence in the strongly…
ABSTRACT The repo market has been viewed as a potential source of financial instability since the 2007 to 2009 financial crisis, based in part on findings that margins increased sharply in a segment of this market. This paper provides evidence suggesting that there was no system‐wide run on repo. Using confidential data on tri‐party repo, a major segment of this market, we show that, the level of margins and the amou…
ABSTRACT Most structural models of default preclude the firm from altering its capital structure. In practice, firms adjust outstanding debt levels in response to changes in firm value, thus generating mean‐reverting leverage ratios. We propose a structural model of default with stochastic interest rates that captures this mean reversion. Our model generates credit spreads that are larger for low‐leverage firms, and …
Abstract In this paper, we identify jumps in U.S. Treasury-bond (T-bond) prices and investigate what causes such unexpected large price changes. In particular, we examine the relative importance of macroeconomic news announcements versus variation in market liquidity in explaining the observed jumps in the U.S. Treasury market. We show that while jumps occur mostly at prescheduled macroeconomic announcement times, an…
Leveraged ETFs (L-ETFs) are exchange-traded funds that achieve price movements several times greater than an index by holding index-linked futures such as Nikkei Stock Average Index futures. It is known that when the price of an L-ETF falls, the L-ETF uses the liquidity of futures to limit the decline through arbitrage trading. Conversely, when the price of a futures contract falls, the futures contract uses the liqu…
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…
The tokenization of real-world assets (RWAs) promises to transform financial markets by enabling fractional ownership, global accessibility, and programmable settlement of traditionally illiquid assets such as real estate, private credit, and government bonds. While technical progress has been rapid, with over \$25 billion in tokenized RWAs brought on-chain as of 2025, liquidity remains a critical bottleneck. This pa…
Meme tokens represent a distinctive asset class within the cryptocurrency ecosystem, characterized by high community engagement, significant market volatility, and heightened vulnerability to market manipulation. This paper introduces an innovative approach to assessing liquidity risk in meme token markets using entity-linked address identification techniques. We propose a multi-dimensional method integrating fund fl…
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 …
We study liquidity on decentralized exchanges (DEXs), identifying factors at the platform, blockchain, token pair, and liquidity pool levels with predictive power for market depth metrics. We introduce the v2 counterfactual spread metric, a novel criterion which assesses the degree of liquidity concentration in pools using the ``concentrated liquidity'' mechanism, allowing us to decompose the effect of a factor on ma…
Concentrated liquidity (CL) provisioning is a way how to improve the capital efficiency of Automated Market Makers (AMM). Allowing liquidity providers to use leverage is a step towards even higher capital efficiency. A number of Decentralized Finance (DeFi) protocols implement this technique in conjunction with overcollateralized lending. However, the properties of leveraged CL positions have not been formalized and …
We develop a liquidity-sensitive multivariate volatility framework to improve the estimation of time-varying covariance structures under market frictions. We introduce two novel portfolio-level liquidity measures, liquidity jump and liquidity diffusion, which capture magnitude and volatility of liquidity fluctuation, respectively, and construct liquidity-adjusted return and volatility that reflect real-time liquidity…
Lending Protocols (LPs), as blockchain-based lending systems, allow any agents to borrow and lend cryptocurrencies. However, liquidity risks could occur, especially when salient loans are initiated by a particular group of borrowers. This paper proposes measurements of liquidity risks, focusing on both available liquidity and market concentration in LPs. By using Aave as a case study, we find that liquidity risks are…
This paper examines the effect of the political network of Chinese municipal leaders on the pricing of municipal corporate bonds. Using municipal leaders' working experience to measure the political network, we find that this network reduces the bond issuance yield spreads by improving the credit ratings of the issuer, the local government financing vehicle. The relationship between political networks and issuance yi…
Convertible instruments are contracts, used in venture financing, which give investors the right to receive shares in the venture in certain circumstances. In liquidity events, investors may have the option to either receive back their principal investment, or to receive a proportional payment after conversion of the contract to a shareholding. In each case, the value of the payment may depend on the choices made by …
The principal portfolios of the standard Capital Asset Pricing Model (CAPM) are analyzed and found to have remarkable hedging and leveraging properties. Principal portfolios implement a recasting of any correlated asset set of N risky securities into an equivalent but uncorrelated set when short sales are allowed. While a determination of principal portfolios in general requires a detailed knowledge of the covariance…
Market liquidity plays a vital role in the field of market micro-structure, because it is the vigor of the financial market. This paper uses a variable called convexity to measure the potential liquidity provided by order-book. Based on the high-frequency data of each stock included in the SSE (Shanghai Stock Exchange) 50 Index for the year 2011, we report several statistical properties of convexity and analyze the a…
Bilateral CVA as currently implement has the counterintuitive effect of profiting from one's own widening CDS spreads, i.e. increased risk of default, in practice. The unified picture of CVA and liquidity introduced by Morini & Prampolini 2010 has contributed to understanding this. However, there are two significant omissions for practical implementation that come from the same source, i.e. positions not booked in us…
Attention builds a weighted average of values, with weights from a compatibility function of queries and keys. It lets a model focus on relevant parts of a context instead of a single fixed vector.
An autoencoder learns to reconstruct its input through a bottleneck, producing a compressed latent that can be used for denoising, retrieval, or as a generative seed.
Backpropagation computes gradients of a scalar loss with respect to every weight by applying the chain rule backwards through the computational graph.
BERT is a bidirectional Transformer encoder trained with masked language modeling and next-sentence prediction, then fine-tuned on downstream NLP tasks.
DQN approximates Q(s, a) with a deep net, using experience replay and a frozen target network so the TD target does not chase itself every step.
A diffusion model learns to reverse a gradual noising process. Sampling starts from noise and iteratively denoises toward the data distribution.
An embedding is a learned dense vector for an object (token, sentence, image, user) such that geometry supports retrieval, clustering, or as input to a downstream model.
Knowledge distillation trains a smaller student to match a teacher’s output distribution (soft labels), transferring behavior without copying every weight.
MoE routes each token (or example) to a sparse subset of specialist feed-forward experts, raising parameter count without paying dense FLOPs on every token.
Overfitting is when a model fits training idiosyncrasies instead of the transferable regularity, so held-out or live error rises even as train loss falls.
Prompt injection is an attack that inserts instructions into retrieved or user-supplied text so the model obeys the attacker instead of the developer’s system policy.
PPO is a policy-gradient algorithm that clips the probability ratio so each update stays close to the previous policy, giving much of TRPO’s stability with first-order SGD.
RAG retrieves relevant documents first, then conditions a language model on that evidence so answers can be grounded, cited, and updated without retraining.
Self-attention is attention where queries, keys, and values all come from the same sequence, so each position can mix information from every other position in one layer.
Teacher forcing trains a sequential decoder on the ground-truth previous token instead of its own prediction — fast and biased, which is why exposure bias exists.
The Transformer is a sequence model built only from self-attention and feed-forward blocks, with no recurrence. It is the architecture behind BERT, GPT, T5, and almost every modern foundation model.
A word embedding is a dense vector for a token such that geometry (distance, direction) reflects distributional meaning. It is the input layer of almost every neural NLP model.
Leverage Ratio Constraint — Non-risk-weighted capital floor binding balance-sheet capacity.
Natural Gas Storage — Inventory levels driving seasonal price spikes and energy inflation.
A CDS is a bilateral contract that pays the loss on a reference credit after a credit event — default insurance quoted as a spread.
A debt covenant is a contractual limit on the borrower — maintain a ratio, not do a thing, or report a thing — that turns a miss into a default or a fee.
Default risk is the chance the issuer misses a contractual payment — the event credit spread is trying, noisily, to price.
Interest coverage is EBIT (or EBITDA) divided by interest expense — how many times operating profit can pay the coupon bill.
A leveraged buyout is a purchase financed mostly with debt on the target’s cash flows — private equity’s standard carry trade on coverage.
Crypto Liquidation Cascade — Forced closes amplifying moves when leverage clusters breach.
Refinery margin: long gasoline and distillate, short crude, in a stated ratio — energy RV rather than a WTI call.
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.
Harvest roll-down and yield carry in bond and STIR futures — a rates-specific premia book that can fight the trend sleeve in a hiking cycle.
Trade nearby versus deferred on the same curve — a pure term-structure book, the smallest-beta cousin of commodity RV.
Inside a managed-futures book, overweight backwardated contracts and underweight contango — roll yield as a second family next to price trend.
The live tax on a systematic futures book — impact, roll, and the fact that the signal is correlated with everyone else’s signal.
The standard G10/EM carry trade run as a vol-targeted futures/forward sleeve beside FX trend — coupon versus crash.
Fade stretched moves in futures over short horizons — the anti-trend sleeve that makes money in ranges and loses when a crisis trend persists.
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.
Scale the whole book (or each market) so forecast σ hits a target — the reason a 15% vol CTA is not ‘more leveraged crude’ in a quiet month.
Trend and overlays on ES, NQ, RTY, SX5E, NKY, EM indexes — the financial-CTA equity sleeve, not a stock-picker.
Code-linked scanner pattern that maps front-vol stress, ATM IV, and dealer gamma context into Blackboard + Options Lab actions.
Educational IV × Greeks × surface sandbox already mounted on the Blackboard — synthetic regimes for pedagogy beside the live scanner.
Adverse selection is the expected loss a liquidity provider takes when the other side is informed — the Glosten–Milgrom reason spreads exist even with no inventory.
Long names with upward earnings-estimate revisions and short downward revisions — the revision-momentum book.
Archegos was a family-office total-return-swap blow-up in March 2021: concentrated longs, huge hidden leverage across prime brokers, and a week of block sales that hit ViacomCBS and others.
Argentina’s 2001–02 collapse ended the convertibility 1:1 peg with default, corralito, and a violent real devaluation — a political-economy crisis of an overvalued peg.
The 1997–98 Asian crisis was a sequence of peg breaks, bank runs, and sudden stops starting in Thailand — short-dollar corporate debt plus weak bank regulation meeting a reversal of carry.
Attention builds a weighted average of values, with weights from a compatibility function of queries and keys. It lets a model focus on relevant parts of a context instead of a single fixed vector.
An autoencoder learns to reconstruct its input through a bottleneck, producing a compressed latent that can be used for denoising, retrieval, or as a generative seed.
Backpropagation computes gradients of a scalar loss with respect to every weight by applying the chain rule backwards through the computational graph.
Long leveraged low-beta stocks and short high-beta stocks so the book is roughly market-neutral — BAB, not raw low-vol.
Beveridge Curve — Vacancy-unemployment relationship signaling matching efficiency and structural labor shifts.
CDS Basis Trade — Arbitrage between cash bonds and CDS contracts revealing funding and counterparty frictions.
China Property Cycle — Developer stress and land sales impacting global commodities and EM growth.
CLO Issuance — Structured credit supply that absorbs leveraged loans and shapes spread regimes.
Cointegration Pairs Trading — Mean-reversion on stationary spreads between related instruments.
Comparative advantage says a country (or desk) should specialize in the activity with the lowest opportunity cost, even if it is worse at everything in absolute terms.
Convexity Risk — Non-linear price response to yield changes, especially relevant in MBS and long bonds.
The February–March 2020 COVID crash was a dash-for-cash that hit even Treasuries, ended by an unprecedented joint monetary-fiscal backstop — a health shock that became a market-function crisis.
Refinery margin: long gasoline and distillate, short crude, in a stated ratio — energy RV rather than a WTI call.
A CDS is a bilateral contract that pays the loss on a reference credit after a credit event — default insurance quoted as a spread.
Time equity beta with oil’s recent move or level — a macro overlay that treats crude as a growth/inflation signal.
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.
Crypto Liquidation Cascade — Forced closes amplifying moves when leverage clusters breach.
Harvest roll-down and yield carry in bond and STIR futures — a rates-specific premia book that can fight the trend sleeve in a hiking cycle.
Trade nearby versus deferred on the same curve — a pure term-structure book, the smallest-beta cousin of commodity RV.
A funding-stress monitor can warn before equity volatility reprices.
A liquidity premium monitor can separate default risk from liquidity-driven spread widening.
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.