JAFEE-ISM International
Symposium:
In Celebration of 30th Anniversaryof the Foundation of JAFEE(Revised Version, as of August 17)

Dates
August 18 (Fri) and 19 (Sat), 2023
Venue
Surugadai Campus, Chuo University 3-11-5 Kandasurugadai,
Chiyoda-ku, Tokyo 101-8324, Japan
https://www.chuo-u.ac.jp/english/visit/surugadai/
Supported by
The Institute of Statistical Mathematics, Chuo University, The Korean
Association of Financial Engineering, JSPS KAKENHI Grant Numbers
JP22H00834, JP22H00862, JST PRESTO Grant Number JPMJPR2029

Invited Speakers
Plenary session:
Beatrice Accaio (ETH Zurich), Richard Davis (Columbia University), Freddy
Delbaen (ETH Zurich, emeritus), Marie Kratz (ESSEC Business School),
Shigeo Kusuoka (University of Tokyo, emeritus), Doojin Ryu (Sungkyunkwan
University)
Parallel session:
Takuji Arai (Keio University), Henry Chiu (Imperial College London), Pierre Del
Moral (INRIA), Jin-Chuan Duan (National University of Singapore), EmmanuelGobet (Ecole Polytechnique), Mei-Hui Guo (National Sun Yat-sen University),
Tomoyuki Ichiba (University of California, Santa Barbara), Ajay Jasra (KAUST),
Hyeongjun Kim (Yeungnam University), Takuya Kiriu (Osaka University), Hyeng
Keun Koo (Ajou University), Daisuke Kurisu (University of Tokyo), Andrew Lim(National University of Singapore), Alex McNeil (University of York), Stefan
Mittnik (Ludwig-Maximilians-University Munich, emeritus), Seongju Moon
(Gyeongsang National University), Teppei Ogihara (University of Tokyo), Marc
Paollela (University of Zurich), Hyungbin Park (Seoul National University), Jun
Sekine (Osaka University), Julian Sester (National University of Singapore), Soo
Young Song (Chung-Ang University), Marko Weber (National University ofSingapore), Kazuhiro Yasuda (Hosei University), Ju-Yi Yen (University ofCincinnati)
Organizing committee
Hideatsu Tsukahara (Seijo University, Chair), Jiro Akahori (Ritsumeikan
University), Kengo Kamatani (Institute of Statistical Mathematics), Teruo
Nakatsuma (Keio University), Hidetoshi Nakagawa (Hitotsubashi University), Jun
Hironaka (Wealth Square), Hiroshi Ishijima (Chuo University), Seisho Sato
(University of Tokyo), Taiga Saito (Senshu University), Takaki Hayashi (Keio
University)
Scientific Program Committee
Hiroshi Tsuda (Doshisha University), Hiroaki Yamauchi (MTEC), Yoshifumi Muroi(Tohoku University), Ken-ichi Arakawa (Risona Bank), Takashi Omoto (Nomura
Asset Management), Kazuhiro Yasuda (Hosei University), Yuta Koike (Universityof Tokyo), Toshihiro Yamada (Hitotsubashi University)August 18 (Friday)
9:00-

Registration

10:00

Room 401
10:00-

Presidential address

10:10
10:10-

Keynote talk 1 (Chair: Hideatsu Tsukahara)

10:50

Richard Davis (Columbia University)
Time series estimation of the dynamic effects of disaster-type shocks

10:50-

Break

11:00
11:00-

Keynote talk 2 (Chair: Hideatsu Tsukahara)

11:40

Shigeo Kusuoka (University of Tokyo, emeritus)
Machine learning and regularization function

11:40-

Lunch break

13:10

Room 802

Room 803

Session 1 (Chair: Yuji Yamada)Session 2 (Chair: Toshinao Yoshiba)

13:10-

Marko Weber (National University of

Emmanuel

13:40

Singapore)

Polytechnique)

Holding stocks, trading bonds

Structured dictionary learning of rating
migration

Gobet

matricesfor(Ecolecreditrisk

modeling
13:40-

Julian Sester (National University of

Jin-Chuan Duan (National University of

14:10

Singapore)

Singapore)

Robust deep hedging

Machine learning interpretable models
via sequential Monte Carlo optimization

14:10-

Henry Chiu (Imperial College London)

Alex McNeil (University of York)

14:40

Mathematical

Vine-copula models for financial andprobability

financewithout

macroeconomic time series14:40-Coffee break at Good View Dining on the 19th floor of Surugadai Campus building

15:15
Session 3 (Chair: Kazuhiro Yasuda)

Session 4 (Chair: Takaki Hayashi)

15:15-

HyungbinHyeongjun Kim (Yeungnam University)

15:45

University)

Forecasting

A sensitivity analysis of the long-term

recoveryexpected utility of optimal portfolios

approaches

15:45-Jun Sekine (Osaka University)

Stefan

16:15

Many-player stochastic games under

University Munich, emeritus)

Epstein-Zin preferences and relative

A risk-based investment strategy for

performance criteria

fundedPark(SeoulNational

studentwithMittnik

loan

machine(Ludwig

pensioncreditlearning

Maximilians

plans

with

intergenerational payout smoothing
16:15-Tomoyuki

16:45

California Santa Barbara)
Stochastic

Ichiba(University

differentialofMarc Paollela (University of Zurich)Momentum without crashes

games

on

random directed networks
16:45-

Break

17:00

Room 401
17:00-

Keynote talk 3 (Chair: Yuri Imamura)

17:40

Doojin Ryu (Sungkyunkwan University)
Financial market microstructure and beyond

18:00-

Reception at Good View Dining on the 19th floor of Surugadai Campus buildingAugust 19 (Saturday)
9:00-

Registration

10:00

Room 401
10:00-

Keynote talk 4 (Chair: Jiro Akahori)

10:40

Marie Kratz (ESSEC Business School)
Automatic threshold selection for extreme value regression models of tail risks

10:40-

Break

10:50
10:50-

Keynote talk 5 (Chair: Jiro Akahori)

11:30

Beatrice Accaio (ETH Zurich)
Quantifying arbitrage

11:30-

Lunch break

13:10

Room 802

Room 803

Session 5 (Chair: Yuta Koike)

KAFE

Session1(Chair:Teruo

Nakatsuma)
13:10-

Mei-Hui Guo (National Sun Yat-sen

Soo

13:40

University)

University) House price cycle, credit-

Dynamic portfolio strategy utilizing

drivennetwork-based approaches

special purpose investment vehicle

13:40-

Ju-Yi Yen (University of Cincinnati)

Takuya Kiriu (Osaka University)

14:10

Mathematical analysis of automatedThe

market makers

Young

Song

(Chung-Ang

demand and Jeonse as a

University)
macroeconomic Constrained
announcements on risk, preference, optimal stopping

impactofand risk premium
14:10-

Teppei Ogihara (University of Tokyo)

14:40

Asymptoticallyefficientparameter

estimation for jump-diffusion processes

Takuji Arai (Keiounder a regime-

Seongju Moon (Gyeongsang National switching model
University)
The

dynamic

relation

betweeneconomic policy uncertainty and stock
market volatility in two countries:
United States and S. Korea

14:40-Coffee break at Good View Dining on the 19th floor of Surugadai Campus building

15:15
ISM Session (Chair: Kengo Kamatani)

KAFE Session 2 (Chair: Doojin Ryu)

15:15-

Pierre Del Moral (INRIA)

Kazuhiro Yasuda (Hosei University)

15:45

A backward Ito-Ventzell formula with anAnapplication to stochastic interpolation

investment

optimalconsumption
problem

and

with

an

exponential utility under a stochastic
factor model
15:45-

Ajay Jasra (KAUST)

Daisuke Kurisu (University of Tokyo)

16:15

Unbiased estimation using

Adaptive deep learning for nonlinear

underdamped Langevin dynamics

time series models

16:15-

Andrew Lim (National University ofHyeng Keun Koo (Ajou University)

16:45

Singapore)

An anticipatory social preference with

Optimal trade execution and learning

sustainability constraint

when price impact is uncertain
16:45-

Break

17:00

Room 401
17:00-

Keynote talk 6 (Chair: Jun Sekine)

17:40

Freddy Delbaen (ETH Zurich, emeritus)
Insurance premiums when default is taken into account

17:4017:50

Closing address

JAFEE-ISM International Symposium: Abstract
August 18, 2023 (Fri)
10:10-10:50

Keynote talk 1, Room 401

Time series estimation of the dynamic eﬀects of disaster-type shocks
Richard Davis (Columbia University)
This paper provides three results for SVARs under the assumption that the primitive shocks aremutually independent. First, a framework is proposed to accommodate a disaster-type variable with
inﬁnite variance into a SVAR. We show that the least squares estimates of the SVAR are consistent but
have non-standard asymptotics. Second, the disaster shock is identiﬁed as the component with the largest
kurtosis. An estimator that is robust to inﬁnite variance is used to recover the mutually independent
components. Third, an independence test on the residuals pre-whitened by the Choleski decomposition
is proposed to test the restrictions imposed on a SVAR. The test can be applied whether the data have
fat or thin tails, and to over as well as exactly identiﬁed models. Three applications are considered.
In the ﬁrst, the independence test is used to shed light on the conﬂicting evidence regarding the roleof uncertainty in economic ﬂuctuations. In the second, disaster shocks are shown to have short term
economic impact arising mostly from feedback dynamics. The third uses the framework to study the
dynamic eﬀects of economic shocks post-covid. (This is joint work with Serena Ng.)

11:00-11:40

Keynote talk 2, Room 401

Machine learning and regularization function
Shigeo Kusuoka (University of Tokyo, emeritus)
The regularization is used to avoid overﬁtting in machine learning or in statistics. However, as far
as the author knows, it is not discussed what kind of regularization function is suitable. The author
discusses this question from the view point of uniform law of large number.

13:10-14:40

Session 1, Room 802

Holding stocks, trading bonds
Marko Weber (National University of Singapore)
In an economy with random growth, several long-lived agents with heterogeneous risk aversions, timepreferences, and personal income streams make consumption and investment decisions, trading stocks
and a consol bond, and borrowing from and lending to each other. We ﬁnd in closed form equilibrium
stock prices, interest rates, consumption, and trading policies. Agents do not trade stocks, although
their returns are time-varying and predictable. Agents dynamically trade the consol bond in response to
growth shocks, as to hedge their eﬀect on interest rates, dividends, and personal incomes. Static fundseparation holds if agents have also access to a linear bond and two additional hedges for dividend andgrowth shocks. Such additional assets can be dynamically replicated with the stock and the consol bond.
No representative agent exists.

Robust deep hedging
Julian Sester (National University of Singapore)
We study pricing and hedging under parameter uncertainty for a class of Markov processes which
we call generalized aﬃne processes and which includes the Black-Scholes model as well as the constantelasticity of variance (CEV) model as special cases. Based on a general dynamic programming principle,
we are able to link the associated nonlinear expectation to a variational form of the Kolmogorov equation
which opens the door for fast numerical pricing in the robust framework. The main novelty of the paper
is that we propose a deep hedging approach which eﬃciently solves the hedging problem under parameter
uncertainty. We numerically evaluate this method on simulated and real data and show that the robust
deep hedging outperforms existing hedging approaches in highly volatile periods. *(This is joint workwith Eva Lütkebohmert and Thorsten Schmidt)*

Mathematical ﬁnance without probability
Henry Chiu (Imperial College London)
We present a pathwise approach to continuous-time ﬁnance based on causal functional calculus. Our
framework does not rely on any probabilistic concept. We introduce a deﬁnition of continuous-time
self-ﬁnancing portfolios, which does not rely on any integration concept and show that the value of aself-ﬁnancing portfolio belongs to a class of nonanticipative functionals, which are pathwise analogs ofmartingales. We show that if the set of market scenarios is generic in the sense of being stable undercertain operations, such self-ﬁnancing strategies do not give rise to arbitrage. We then consider the
problem of hedging a path-dependent payoﬀ across a generic set of scenarios. Applying the transition
principle of Rufus Isaacs in diﬀerential games, we obtain a pathwise dynamic programming principle forthe superhedging cost. We show that the superhedging cost is characterized as the solution of a pathdependent equation. For the Asian option, we obtain an explicit solution. (Joint-work with Rama Cont)

13:10-14:40

Session 2, Room 803

Structured dictionary learning of rating migration matrices for credit risk
modeling
Emmanuel Gobet (Ecole Polytechnique)Rating Migration Matrix is a crux to assess credit risks. Modeling and predicting these matrices
are then an issue of great importance for risk managers in any ﬁnancial institution. As a challenger
to usual parametric modeling approaches, we propose a new structured dictionary learning model with
auto-regressive regularization that is able to meet key expectations and constraints: small amount of
data, fast evolution in time of these matrices, economic interpretability of the calibrated model. To show
the model applicability, we present a numerical test with real data and a comparison study with thewidely used parametric Gaussian Copula model: it turns out that our new approach based on dictionary
learning signiﬁcantly outperforms the Gaussian Copula model.

Machine learning interpretable models via sequential Monte Carlo optimization
Jin-Chuan Duan (National University of Singapore)
Utilizing big data becomes realistic with machine learning. However, black-box machine learning
typically fails to meet managerial needs and/or compliance requirements. Without any doubt, theseimportant considerations can be better served by intepretable models, but such models are often illequiped to handle high-dimensional features. This talk is about expanding the realm of interpretablemodels with machine learning by sequential Monte Carlo (SMC) optimization. First, I will illustrate
the general idea behind SMC optimization and show its working using a simple example. Next, I will
demonstrate how to greatly improve the performance of a hedonic housing price model through ﬁnding
an optimal stable subset of interpretable features from over 150,000 potential variables arising frominteraction terms.
Background reading: ”Sequential Monte Carlo optimization and statistical inference” Duan, J.-C., Li,
S., & Xu, Y. (2022). Wiley Integrative Reviews: Computational Statistics, e1598.
https://doi.org/10.1002/wics.1598.

Vine-copula models for ﬁnancial and macroeconomic time seriesAlex McNeil (University of York)We present some new approaches to modelling and forecasting macroeconomic and ﬁnancial time series using stationary d-vine (s-vine) copula processes. We show how non-Gaussian extensions of ARMA
processes can be constructed to model data that have non-Gaussian marginal distributions and/or nonGaussian and non-linear serial dependence structures. We also show how these models can be combined with uniform-measure-preserving transformations known as v-transforms to construct processes
for volatile ﬁnancial return series which can outperform classical econometric models in certain cases.
Methods will be illustrated with a variety of applications to data.

15:15-16:45

Session 3, Room 802

A sensitivity analysis of the long-term expected utility of optimal portfolios
Hyungbin Park (Seoul National University)
This paper discusses the sensitivity of the long-term expected utility of optimal portfolios for an investor
with constant relative risk aversion. Under an incomplete market given by a factor model, we consider
the utility maximization problem with long-time horizon. The main purpose is to ﬁnd the long-term
sensitivity, that is, the extent how much the optimal expected utility is aﬀected in the long run for smallchanges of the underlying factor model. The eigenpair does not only characterize the long-term behaviorof the optimal expected utility but also provides an explicit representation of the expected utility on aﬁnite time horizon. We conclude that this eigenpair therefore determines the long-term sensitivity.

Many-player stochastic games under Epstein-Zin preferences and relative
performance criteria
Jun Sekine (Osaka University)
In a multidimensional Black-Scholes model, Lacker and Soret (2020) studied the N-player stochastic
game, where each competitive agent (eg., fund manager) maximizes the expected CRRA utilities ofconsumption streams and the terminal wealth with respect to the benchmarks, the averages among all
agents. For the problem, the explicit expression of its Nash equilibrium was obtained, and, moreover,the mean-ﬁled limit was computed, letting N → ∞. In this talk, we pursue their exact analysis with thefollowing generalizations: (i) Employing the Epstein-Zin recursive utility. (ii) Considering the agents’portfolio constraints. (iii) Employing a regime-switching model. The talk is based on a joint work with
Assil Fadle of Ecole Polytechnique, Paris.

Stochastic diﬀerential games on random directed networks
Tomoyuki Ichiba (University of California, Santa Barbara)
We consider stochastic diﬀerential games on random directed networks with mean-ﬁeld interactions,where the network of countably many players is formulated randomly in the beginning and each player in
the network attempts to minimize the expected cost with a ﬁnite time horizon. Here, the cost function
is determined by the network. Under the setup of the linear quadratic stochastic game with directed
chain graph, we solve explicitly for an open-loop Nash equilibrium for the system and we ﬁnd that the
dynamics under the equilibrium is an inﬁnite-dimensional Gaussian process associated with a Catalan
Markov chain. We extend it to the random directed tree structure and discuss convergence results fromthe ﬁnite player game to inﬁnitely many player game via Fourier transforms. This is joint work with
Yichen Feng and Jean-Pierre Fouque.

15:15-16:45

Session 4, Room 803

Forecasting student loan credit recovery with machine learning approaches
Hyeongjun Kim (Yeungnam University)
This paper investigates the forecasting capability of diﬀerent machine learning approaches for student
loan credit recovery using micro-level data. We compare the performance of logistic regression, random
forest, support vector machine, and deep neural network algorithms in predicting student loan credit
recovery. By utilizing a dataset comprising detailed information on student loan credit recovery, the study
aims to enhance the accuracy of predictions, enabling proactive strategies to manage student loan credit
recovery. Each machine learning algorithm is implemented and evaluated using appropriate performance
metrics, including accuracy, precision, recall, and F1 score. In conclusion, this study underscores thesigniﬁcance of accurate forecasting in student loan credit recovery and showcases the applicability ofmachine learning algorithms in addressing this challenge. The ﬁndings have practical implications forstakeholders involved in managing student loans, facilitating better decision-making and risk mitigation.

A risk-based investment strategy for funded pension plans with intergenerational payout smoothing
Stefan Mittnik (Ludwig Maximilians University Munich, emeritus)
Interest in risk-adjusted investment strategies has grown in recent years as a result of major market
crashes since the turn of the century, such as the dot-com bubble burst at the beginning of the century, theﬁnancial crisis of 2008, and the Corona Crash of 2020. Yet the systematic application of such strategies
in practice is the exception rather than the rule. Here we present a risk-adjusted investment strategy inthe context of a pension scheme with intergenerational risk sharing. In this scheme, the intergenerationaltransfer of market risk is realized through the establishment of a collective reserve fund, the inﬂows andoutﬂows of which are managed through market risk. We show that the use of a risk-adjusted investment
strategy can improve the performance of the pension system and while reducing the risk of a negative
reserve in times of market crisis. In addition, we assess the implications of imposing diﬀerent levels of
minimum-diversiﬁcation requirements across the assets held.

Momentum without crashes
Marc Paollela (University of Zurich)
We construct a momentum factor that identiﬁes cross-sectional winners and losers based on a weighting
scheme that incorporates all the price data, over the entire lookback period, as opposed to only the ﬁrst
and last price points of the window. The weighting scheme is derived from the fractional-diﬀerence ﬁlter—a statistical transformation that preserves memory in the data, and has an economic interpretation ofcoherently combining reversal and momentum patterns in the returns. Our extensive out-of-sample
analysis shows that the new fractional momentum strategy not only achieves signiﬁcantly higher (riskadjusted) returns, but also mitigates the notoriously large drawdowns of the classical momentum andshort-term reversal strategies. The performance results are robust with respect to transaction costs andother real world frictions; excess returns are not explained by other asset pricing factors; and they arepervasive across diﬀerent asset universes and foreign markets.

17:00-17:40

Keynote talk 3, Room 401

Financial market microstructure and beyond
Doojin Ryu (Sungkyunkwan University)
I explain the brief history of ﬁnancial market microstructure. I introduce structural microstructure
models and their extensions and applications. I also illustrate the recent studies of empirical market
microstructure using high-frequency and high-quality datasets.

August 19, 2023 (Sat)10:00-10:40

Keynote talk 4, Room 401

Automatic threshold selection for extreme value regression models of tailrisks
Marie Kratz (ESSEC Business School)
We introduce a method to estimate simultaneously the tail and the threshold parameters of an extreme
value regression model. This standard model ﬁnds its use in ﬁnance to assess theeﬀect of market

variables on extreme loss distributions of investment vehicles such as hedge funds. However, a major
limitation is the need to select ex ante a threshold below which data are discarded, leading to estimation
ineﬃciencies. To solve these issues, we extend the tail regression model to non-tail observations with an
auxiliary splicing density, enabling the threshold to be selected automatically. We then apply an artiﬁcial
censoring mechanism of the likelihood contributions in the bulk of the data to decrease speciﬁcation issues
at the estimation stage. We illustrate the superiority of our approach for inference over classical peaksover-threshold methods in a simulation study. Empirically, we investigate the determinants of hedge
fund tail risks over time, using pooled returns of 1,484 hedge funds. We ﬁnd a signiﬁcant link between
tail risks and factors such as equity momentum, ﬁnancial stability index, and credit spreads. Moreover,sorting funds along exposure to our tail risk measure discriminates between high and low alpha funds,
supporting the existence of a fear premium. This is a joint work with Julien Hambuckers and Antoine
Usseglio-Carleve.

10:50-11:30

Keynote talk 5, Room 401

Quantifying arbitrage
Beatrice Accaio (ETH Zurich)
In this talk I will present a way to quantify arbitrage, that allows to deal with model uncertainty
without imposing the no-arbitrage condition. In markets that admit “small arbitrage”, we can still makesense of the problems of pricing and hedging. The pricing measures here will be such that asset priceprocesses are close to being martingales, and the hedging strategies will need to cover some additionalcost. I will show a quantitative version of the Fundamental Theorem of Asset Pricing and of the Superreplication theorem. I will then introduce a strong adapted Wasserstein distance between market models,that is suited for robustness of the amount of arbitrage and existence of respective pricing measures.
Based on joint work with Julio Backhoﬀ and Gudmund Pammer

13:10-14:40

Session 5, Room 802

Dynamic portfolio strategy utilizing network-based approaches
Mei-Hui Guo (National Sun Yat-sen University)
This study explores the application of ﬁnancial network models forconstructing dynamic investment
portfolios. We employ correlation coeﬃcients and the Gaussian kernel function to calculate similarity
matrices between stocks. The network model is constructed using the Minimum Spanning Tree and
the Planar Maximally Filtered Graph, with centrality measured using eigenvectors. Various stock selection and asset allocation methods, including high/low centrality selection, rho-dependent selection, andweighting based on risk ratios, are considered. A dynamically updated approach is utilized to adjust theinvestment strategy for each period. Empirical analysis using S&P100 stocks from 2014 to 2021 reveals
that the dynamic portfolio strategy based on the Gaussian kernel function network achieves superior
cumulative returns.

Mathematical analysis of automated market makers
Ju-Yi Yen (University of Cincinnati)
Automated market makers (AMMs) are examples of Decentralized Finance systems. Nowadays, AMMs
are dominated by the Constant Function Market Makers (CFMMs). CFMMs pool liquidity from its
takers and providers, and set the relative prices of the two assets within the pool by a mathematicalformula. The relative price is determined by the reserves of the two assets in the pool. Notice that theassets in the liquidity pool are risky assets, their performances are impacted by the market risk. In thistalk, we describe the stochastic process used for modeling the relation between the pool price and thecorresponding market price for assets traded via CFMMs, and present limit theorems of this stochastic
process. Our results are deduced from properties of the Brownian motion and its local time process.

Asymptotically eﬃcient parameter estimation for jump-diﬀusion processes
Teppei Ogihara (University of Tokyo)
We consider eﬃcient parameter estimation for a parametric jump-diﬀusion process model via local
asymptotic normality (LAN). LAN gives us a lower bound of asymptotic variance, and hence it enables
us to discuss asymptotic eﬃciency of parameter estimators for parametric models. To show LAN fordiﬀusion processes in Gobet (AIHP PS 2002), the inequalities (so-called the Aronson estimates) that thetransition density functions are bounded from below and above by Gaussian density functions up to a
constant were crucial in the proof. However, it is diﬃcult to obtain Aronson-type estimates for jumpdiﬀusion processes. Instead, we use a scheme with the L2 regularity condition developed in Jeganathan(Sankhya Ser. A 1982) that does not require Aronson-type estimates. The original scheme cannot
be applied for jump-diﬀusion processes because of their fat-tailed behaviors. Therefore, we extend thescheme so that it can be applied to jump-diﬀusion processes. Under LAN, we can discuss optimality of
estimators for the jump-diﬀusion model. We show that the quasi-maximum-likelihood and Bayes-type
estimators proposed in previous works attain the lower bound of asymptotic variances and consequently
these estimators are asymptotically eﬃcient in this model.

13:10-14:40

KAFE Session 1, Room 803

House price cycle, credit driven demand and Jeonse as a special purpose
investment vehicle
Sooyoung Song (Chung-Ang University)
Devastating eﬀect of real estate collapse has never been more reverberated throughout the advanced
economies not only on the domestic economy as well as the global economy than in the wake of Global
Financial Crisis since 2008. In contrast, the South Korean economy had retained relatively stable residential market and suﬀered from the moderate domestic economy downturn. Nevertheless, ironically indeed,
steeply surging residential house price in the wake of pandemic crisis has been accused, though groundless,as a primary culprit of government policy failure to control household debt in light of wealth inequality
as well as even income inequality, despite the better economic performance than most other advanced
economies. These recent episodes of housing market may indicate more persistent impact of real estate
on an economy than perceived. In such a parlance, this paper highlights the inherent feature of ﬁnancing
within the housing transaction focusing on the channels through which the overvaluation of house pricepersists. As the asset value of house, that is house price, is the source of leveraging, the fundamental valueof housing service helps formulate the expectation of housing price on which the overvaluation of housing
market price is evaluated. The higher the expected value of housing, the lower the overvaluation of housing market price, which in turn reduces the overvaluation concern and drives steep rise of housing price.
Regardless of whether housing is consumed via either owned house or rented one, the ﬁnancing source isprimarily from the ﬁnancial intermediary. Then such ﬁnancing is strictly regulated several measures suchas LTV or DTI. Nonetheless, South Korean housing owners ﬁnd an additional source,

Jeonse (傳貰),

circumventing the existing regulation on the housing loan, which is not unique but peculiar feature ofresidential tenancy. Jeonse rather works as an instrument of ﬁnancing, i.e. middle-term (longer than oneyear) repurchase agreement, circumventing the micro prudential policy, which could amplify the housing
price ﬂuctuation. Thus it works like a Special Purpose Investment Vehicle with the margin account inwhich the leverage is given as a Jeonse price. The prevalence of Jeonse in the South Korea may indicate
the inclusive ﬁnance is still an elusive one. So the current paper sheds a new light on intertwined eﬀect
between the real estate policy and ﬁnancial policy.

The impact of macroeconomic announcements on risk, preference, and risk
premium
Takuya Kiriu (Osaka University)
This study examines the impact of macroeconomic announcements on the risk premium and its sources
under time-varying preference. We propose a novel method to decompose risk premium changes into therisk and preference components, which are estimated from option prices immediately before and after theannouncement using the Recovery Theorem. The results of the empirical analysis for the United States
stock market indicate that (1) the negative (positive) macroeconomic announcement surprise increases(decreases) the risk premium; (2) the risk component mainly drives the increase (decrease) in the risk
premium; and (3) the preference component has limited inﬂuence on the risk premium.

Constrained optimal stopping under a regime-switching model
Takuji Arai (Keio University)
We investigate an optimal stopping problem for the expected value of a discounted payoﬀ on a regimeswitching geometric Brownian motion under two constraints on the possible stopping times: only atexogenous random times and only during a speciﬁc regime. The main objectives are to show that an
optimal stopping time exists as a threshold type and to derive expressions of the value functions andthe optimal threshold. To this end, we solve the corresponding variational inequality and show that its
solution coincides with the value functions. Some numerical results are also introduced. Furthermore,
we investigate some asymptotic behaviors.

The dynamic relation between economic policy uncertainty and stock market
volatility in two countries : United states and S. Korea
Seongju Moon (Gyeongsang National University)
The ﬁnancial industry has become one of the most data-driven industries and the recent technologicaldevelopments in big data, ﬁnancial services, consumer-data management, social network services, andﬁltering data quality has made better interpretation of analyzed data possible in the ﬁnance industry.
Financial data consist of a huge volume of quotes, market data, historical trade data, and time-sequenced
transaction data used to model market and customer behavior. Many papers found that the stock market
Volatility and Economic Policy Uncertainty (EPU) are closely correlated. This paper looked at whether
the EPU index generated by big data has information capability in the volatility of the stock market andat whether there are existed the long-run equilibrium and short-run relation between the U.S. and S.
Korea stock market. This paper ﬁndings reveal that the EPU index in both the U.S. and S. Korea aﬀects
the volatility of the stock market signiﬁcantly, which in turn, indicates that the EPU index can provide
useful and meaningful information to investors in the stock and derivatives markets in both countries.

15:15-16:45

ISM Session, Room 802

A backward Ito-Ventzell formula with an application to stochastic interpolation
Pierre Del Moral (INRIA)
We discuss a novel backward Ito-Ventzell formula and an extension of the Aleeksev-Gröbner interpolating formula to stochastic ﬂows. We also present some natural spectral conditions that yield direct andsimple proofs of time uniform estimates of the diﬀerence between the two stochastic ﬂows when theirdrift and diﬀusion functions are not the same, yielding what seems to be the ﬁrst results of this type
for this class of anticipative models. We illustrate the impact of these results in the context of diﬀusion
perturbation theory, interacting diﬀusions and discrete time approximations.

Unbiased Estimation using Underdamped Langevin Dynamics
Ajay Jasra (King Abdullah University of Science and Technology)
In this work we consider the unbiased estimation of expectations w.r.t. probability measures that have
non-negative Lebesgue density, and which are known point-wise up-to a normalizing constant. We focus
upon developing an unbiased method via the underdamped Langevin dynamics, which has proven to be
popular of late due to applications in statistics and machine learning. Speciﬁcally in continuous-time, thedynamics can be constructed to admit the probability of interest as a stationary measure. We develop anovel scheme based upon doubly randomized estimation, which requires access only to time-discretizedversions of the dynamics and are the ones that are used in practical algorithms. We prove, under standard
assumptions, that our estimator is of ﬁnite variance and either has ﬁnite expected cost, or has ﬁnite costwith a high probability. To illustrate our theoretical ﬁndings we provide numerical experiments thatverify our theory, which include challenging examples from Bayesian statistics and statistical physics.

Optimal trade execution and learning when price impact is uncertain
Andrew Lim (National University of Singapore)
This paper considers the problem of optimal trade execution with a linear price impact function whenboth the drift (intercept) and permanent price impact (slope) parameters are uncertain. Since the trading
rate aﬀects the returns data that is used to learn the uncertain parameters, this problem is intrinsically a
tradeoﬀ between exploration and exploitation, and it is of particular interest to understand how the trader
goes about ”exploring” under the optimal policy. To sidestep the well-known challenges of solving the
dynamic programming equation, which is inﬁnite dimensional in general and has no explicit solution evenwhen the prior on both parameters is Gaussian, we consider the asymptotic regime where the learning
rate vanishes and use a Taylor Series expansion extract the exploration component of the optimal policy.
This exploration term can be easily characterized and computed, and provides surprising but ultimately

intuitive insights about the nature of exploration, its dependence on the time horizon, and the way itbalances between controling inventory and learning the drift and market impact parameters. (Joint workwith Galvin Ng).

15:15-16:45

KAFE Session 2, Room 803

An optimal consumption and investment problem with an exponential utility
under a stochastic factor model
Kazuhiro Yasuda (Hosei University)
We consider an optimal consumption and investment problem on a ﬁnite time horizon to optimize
the discounted expected exponential utility of consumption and terminal wealth. As our stock priceprocesses, we adopt a stochastic factor model that the mean returns of risky assets linearly depend onunderlying economic factors formulated as the solutions of linear stochastic diﬀerential equations. We
derive the corresponding Hamilton-Jacobi-Bellman equation and obtain an explicit solution. Using thesolution, we construct the optimal strategy and obtain the optimal value function explicitly. This is a
joint work with Hiroaki Hata (Hitotsubashi University).

Adaptive deep learning for nonlinear time series models
Daisuke Kurisu (University of Tokyo)
In this talk, we develop a general theory for adaptive nonparametric estimation of mean functions of
nonstationary and nonlinear time series using deep neural networks (DNNs). We ﬁrst consider two types of
DNN estimators, non-penalized and sparse-penalized DNN estimators, and establish their generalization
error bounds for general nonstationary time series. We then derive minimax lower bounds for estimating
mean functions belonging to a wide class of nonlinear autoregressive (AR) models that include nonlinear
generalized additive AR, single index, and threshold AR models. Building upon the results, we showthat the sparse-penalized DNN estimator is adaptive and attains the minimax optimal rates up to apoly-logarithmic factor for many nonlinear AR models. Through numerical simulations, we demonstrate
the usefulness of the DNN methods for estimating nonlinear AR models with intrinsic low-dimensional
structures and discontinuous or rough mean functions, which is consistent with our theory.

An anticipatory social preference with sustainability constraint
Hyeng Keun Koo (Ajou University)
We propose a social preference with sustainability constraint. We take a Rawlsian perspective andimpose a minimum welfare constraint for all future generations. Our preference is represented by asocial utility function which has the future minimum welfare level as a component. Thus our preferenceis a anticipatory preference. We provide an optimal consumption and investment policies with such apreference.

17:00-17:40

Keynote talk 6, Room 401

Insurance premiums when default is taken into accountFreddy Delbaen (ETH Zurich, emeritus)
Economic Agents buying insurance take into account that the insurance company can default. In thatcase they are not fully covered. The required capital for this insurance is used as a parameter. This
capital is the sum of the input provided by the shareholders of the insurance company and the premiums
paid for by the insured. The problem is to ﬁnd an equilibrium between these two contributions. A
premium allocation technique is used. This is joint work with Delia Coculescu, Institute of Mathematics,
University of Zurich and Philippe Artzner, Karl-Theodore Eisele, Université Louis Pasteur, Strasbourg.