arXiv · arXiv q-fin · 2025
Stablecoins represent a critical bridge between cryptocurrency and traditional finance, with Tether (USDT) dominating the sector as the largest stablecoin by market capitalization. By Q1 2025, Tether directly held approximately $98.5 billion in U.S. Treasury bills, representing 1.6% of all outstanding Treasury bills, making it one of the largest non-sovereign buyers in this crucial asset class, on par with nation-sta…
Lennart Ante, Aman Saggu, Ingo Fiedler
arXiv · arXiv q-fin · 2025
Digital Asset Treasury (DAT) companies, public firms that hold large crypto reserves as a core strategy, deliver levered exposure to digital assets but face acute downside risk when equity premia over net asset value multiples (mNAV) compress in bear markets. This paper develops a survival framework that couples conservative treasury policy with an operating line that monetizes holdings independent of mark-to-market …
Hongzhe Wen
arXiv · arXiv q-fin · 2020
This paper quantifies the effects of equity tail risk on the US government bond market. We estimate equity tail risk with option-implied stock market volatility that stems from large negative price jumps, and we assess its value in reduced-form predictive regressions for Treasury returns and a term structure model for interest rates. We find that the left tail volatility of the stock market significantly predicts one…
Mirco Rubin, Dario Ruzzi
arXiv · arXiv q-fin · 2024
We study a multivariate autoregressive stochastic volatility model for the first 3 principal components (level, slope, curvature) of 10 series of zero-coupon Treasury bond rates with maturities from 1 to 10 years. We fit this model using monthly data from 1990. Unlike classic models with hidden stochastic volatility, here it is observed as VIX: the volatility index for the S&P 500 stock market index. Surprisingly, th…
Jihyun Park, Andrey Sarantsev
arXiv · arXiv q-fin · 2018
We introduce simple cost and risk proxy metrics that can be attached to Treasury issuance strategy to complement analysis of the resulting portfolio weighted-average maturity (WAM). These metrics are based on mapping issuance fractions to their long-term, asymptotic portfolio implications for cost and risk under mechanical debt-rolling dynamics. The resulting mapping enables one to visualize tradeoffs involved in con…
Christopher Cameron
OpenAlex · The Journal of Finance · 2004 · cites 391
ABSTRACT We examine the role of price discovery in the U.S. Treasury market through the empirical relationship between orderflow, liquidity, and the yield curve. We find that orderflow imbalances (excess buying or selling pressure) account for up to 26% of the day‐to‐day variation in yields on days without major macroeconomic announcements. The effect of orderflow on yields is permanent and strongest when liquidity i…
Michael W. Brandt, Kenneth A. Kavajecz
OpenAlex · European Finance Review · 2005 · cites 189
Abstract This paper examines the price differences between very liquid on-the-run U.S. Treasury securities and less liquid off-the-run securities over the on/off cycle. Comparing pairs of securities in time-series regressions allows us to disregard any fixed cross-sectional differences between securities. Also, since the liquidity of Treasury notes varies predictably over time, we can distinguish between current and …
David Goldreich, Bernd Hanke, Purnendu Nath
OpenAlex · Journal of Financial and Quantitative Analysis · 2010 · cites 174
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…
George J. Jiang, Ingrid Lo, Adrien Verdelhan
OpenAlex · Econstor (Econstor) · 2001 · cites 125
This paper examines a comprehensive set of liquidity measures for the U.S. Treasury market. The measures are analyzed relative to one another, across securities, and over time. I find highly significant price impact coefficients, such that a simple model that explains price changes with net order flow produces an R² statistic above 30 percent for the two-year note. The price impact coefficients are highly correlated …
Michael J. Fleming
OpenAlex · The Journal of Finance · 1999 · cites 728
The arrival of public information in the U.S. Treasury market sets off a two‐stage adjustment process for prices, trading volume, and bid‐ask spreads. In a brief first stage, the release of a major macroeconomic announcement induces a sharp and nearly instantaneous price change with a reduction in trading volume, demonstrating that price reactions to public information do not require trading. The spread widens dramat…
Michael J. Fleming, Eli M. Remolona
arXiv · arXiv q-fin · 2025
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…
Rischan Mafrur
arXiv · arXiv q-fin · 2013
We give a detailed account of correlations between credit sector/quality and treasury curve factors, using the robust framework of the Barclays POINT Global Risk Model. Consistent with earlier studies, we find a strong negative correlation between sector spreads and rate shifts. However, we also observe that the correlations between spreads and Treasury twists reversed recently, which is likely attributable to the Fe…
Arthur M. Berd, Elena Ranguelova, Antonio Baldaque da Silva
arXiv · arXiv q-fin · 2013
The market practice of extrapolating different term structures from different instruments lacks a rigorous justification in terms of cash flows structure and market observables. In this paper, we integrate our previous consistent theory for pricing under credit, collateral and funding risks into term structure modelling, integrating the origination of different term structures with such effects. Under a number of ass…
Andrea Pallavicini, Damiano Brigo
OpenAlex · Speech · 2020 · cites 31
Remarks at Brookings-Chicago Booth Task Force on Financial Stability (TFFS) meeting, panel on market liquidity (delivered via videoconference).
Lorie Logan
arXiv · arXiv q-fin · 2025
This study introduces an interpretable machine learning (ML) framework to extract macroeconomic alpha from global news sentiment. We process the Global Database of Events, Language, and Tone (GDELT) Project's worldwide news feed using FinBERT -- a Bidirectional Encoder Representations from Transformers (BERT) based model pretrained on finance-specific language -- to construct daily sentiment indices incorporating mea…
Yuke Zhang
arXiv · arXiv q-fin · 2025
This paper mainly utilizes the ARDL model and principal component analysis to investigate the relationship between the volatility of China's Shanghai Composite Index returns and the variables of exchange rate and domestic and foreign bond yields in an internationally integrated stock market. This paper uses a daily data set for the period from July 1, 2010 to April 30, 2024, in which the dependent variable is the Sha…
Jingchu Zhang
arXiv · arXiv q-fin · 2015
We use machine learning for designing a medium frequency trading strategy for a portfolio of 5 year and 10 year US Treasury note futures. We formulate this as a classification problem where we predict the weekly direction of movement of the portfolio using features extracted from a deep belief network trained on technical indicators of the portfolio constituents. The experimentation shows that the resulting pipeline …
Abhijit Sharang, Chetan Rao
arXiv · arXiv · 2026
This paper compares different methods for forecasting the term structure of U.S. and European zero-coupon government bonds using both traditional econometric and Machine Learning (ML) approaches. We compare classical models (e.g., Dynamic Nelson-Siegel (DNS) and Principal Component Analysis (PCA)) with different Neural Network (NN) architectures, including those inspired by the classical models, on the U.S. Treasury …
Tobias Lausser, Joao Eduardo Vuolo, Rudi Zagst