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Updated DEX arbitrage SSRN link and LOB abstract.

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Chen Yang
2025-07-11 15:39:12 +08:00
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<ol>
<li>Arbitraging on Decentralized Exchanges (with <a class="authorlink" href="https://sites.google.com/site/xuedonghepage/home"
target="_blank">Xuedong He</a> and <a class="authorlink" href="https://hk.linkedin.com/in/yutian-zhou-555870189" target="_blank">Yutian Zhou</a>).<br>
Working Paper. <span class="links">[<a class="paperlink" href="" onclick="toggleAbstract('abs_arbDEX');return false">Abstract</a>]</span><br>
Working Paper. <span class="links">[<a class="paperlink" href="" onclick="toggleAbstract('abs_arbDEX');return false">Abstract</a>|<a class="paperlink"
href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5347630" target="_blank">SSRN</a>]</span><br>
<div class="fade-in" style="display:none" id="abs_arbDEX">
<hr>
Decentralized exchanges (DEXs) are alternative venues to centralized exchanges to trade
cryptocurrencies (CEXs) and have become increasingly popular. An arbitrage opportunity arises when
Decentralized exchanges (DEXs) are alternative venues to centralized exchanges (CEXs) for trading
cryptocurrencies and have become increasingly popular. An arbitrage opportunity arises when
the exchange rate of two cryptocurrencies in a DEX differs from that in a CEX. Arbitrageurs can then
trade on the DEX and CEX to make a profit. Trading on the DEX incurs a gas fee, which determines the
priority of the trade being executed. We study a gas-fee competition game between two arbitrageurs
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effects of three key features of market microstructure --- market tightness, market depth, and
finite market resilience --- on the investor's decision. By employing a Bachelier process to model
the dynamic of the fundamental value of the asset and assuming CARA-type utility for the investor,
we manage to obtain the investor's optimal dynamic trading strategy in closed form by solving the
we obtain the investor's optimal dynamic trading strategy in closed form by solving the
resulting high-dimensional singular control problem. Furthermore, we extend the model to incorporate
return-predicting signals and utilize an asymptotic expansion approach to derive approximate optimal
trading strategies. The theoretical and numerical results emphasize the vital role of patience.
Specifically, rather than dispersing small trades continuously over time as advocated by the
existing literature, our findings suggest that investors should strategically time their trading
activities to align with the aim portfolio in the presence of market resilience. To quantify this
activities jointly based on market liquidity and market signal. To quantify this
timing decision, we introduce a patience index that enables investors to strike a balance among
various competing goals, including achieving currently optimal risk exposure, incorporating signals
about future predictions, and minimizing trading costs, by leveraging market resilience.
about future predictions, and minimizing trading costs, by leveraging market resilience. We also
demonstrate how to implement our patient trading strategy using real-life market data.
</div>
</li><br>