Pricing Fixed Income with Liquidity: An Extended Merton Approach
This study extends the Merton model by integrating trading volume to predict corporate bond returns across maturities and market regimes.
What it examines
This paper develops an extended bond pricing model by integrating trading volume as a liquidity measure into the classical Merton framework. It examines how liquid asset proxies derived from daily trading data can better forecast short-term bond returns, comparing outcomes for short and long-maturity bonds.
What it concludes
The study demonstrates that incorporating trading volume improves bond pricing by capturing short-horizon liquidity risks. Results offer practical applications in risk management and trading strategies, and they encourage future research on refining asset and debt measures and exploring market regime interactions.
Evidence objects
Trading volume and its volatility significantly affect corporate bond pricing, especially short-maturity issues, as high overall volume correlates with higher returns while volatile trading can depress bond prices dramatically, notably.
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An innovative liquid Merton model extends the classic framework using daily trading data and rolling window estimations, outperforming traditional methods and improving return forecasts for short-term bonds, enhancing liquidity measurement.
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Combining extensive bond and equity data with regression analysis, the study innovates by integrating liquidity and credit risk, though it may oversimplify complex long-term market dynamics and overlook nuanced trends.
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Extending the classic Merton model by integrating liquidity measures and trading volume, the paper introduces innovative methodology and empirical validation that challenges traditional fixed income pricing approaches. Its original, novel perspective provides compelling insights for quantitative finance, making it a thought-provoking and significant evolutionary advancement worthy of careful academic attention.
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Raw abstract and provenance
- … bond market respond to different risk measures and modeling assumptions. In short, where the p values are highly significant (Method 1 for longest maturity, Methods 2 …
Source row: 1602 · abstract type: snippet