From 04af56dfe45dedd42149608168d426dcaf2c7e2e Mon Sep 17 00:00:00 2001
From: Chen Yang <yc4646@gmail.com>
Date: Sat, 20 Apr 2024 17:42:23 +0800
Subject: [PATCH] Added localvol paper.

---
 index.html | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/index.html b/index.html
index 7a8443f..a430731 100644
--- a/index.html
+++ b/index.html
@@ -107,6 +107,15 @@ Stochastic Control; Market Frictions; Market Microstructure; FinTech; Deep Learn
         </div>
     </li><br>
 
+    <li>Calibration of Local Volatility Models under the Implied Volatility Criterion (with Xinfu Chen, <a href="https://sites.google.com/view/mindai/home" target="_blank">Min Dai</a>, and Zhou Yang).<br>
+    <b>submitted</b>. [<a href="" onclick="toggleAbstract('abs_LocalVol');return false">Abstract</a>|<a href=" https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4801520" target="_blank">SSRN</a>]<br>
+        <div style="display:none" id="abs_AMM">
+        <hr>
+        <i>We study non-parametric calibration of local volatility models, which is formulated as an inverse problem of partial differential equations with Tikhonov regularization. In contrast to the existing literature minimizing the distance between theoretical and market prices of options as a calibration criterion, we instead minimize the distance between theoretical and market implied volatilities, complying with market practices. We prove that our calibration criterion naturally leads to the well-posedness of the calibration problem. In particular, comparing to Jiang and Tao (2001), we obtain a global uniqueness result, where no additional weight functions are required. Numerical results reveal that our method achieves a better trade-off between minimizing calibration errors and reducing overfitting.</i>
+        </div>
+    </li><br>
+
+
     <li>Patience is a Virtue: Optimal Investment in the Presence of Market Resilience (with <a href="https://www1.se.cuhk.edu.hk/~nchenweb/index.htm" target="_blank">Nan Chen</a>, <a href="https://sites.google.com/view/mindai/home" target="_blank">Min Dai</a>, and Qiheng Ding).<br>
     <b>submitted</b>. [<a href="" onclick="toggleAbstract('abs_LOB');return false">Abstract</a>|<a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4671774" target="_blank">SSRN</a>]<br>
         <div style="display:none" id="abs_LOB">