BD21313_

Keio Symposium on Risk Assessment

BD21313_

Date:  September 21st, 2016
Place: BLDG 14 Room 201, Yagami campus, Keio University
Access (English) http://www.st.keio.ac.jp/english/access/index.html
(Japanese) http:/www.st.keio.ac.jp/access/index.html

10:25 - 10:30 Opening

10:30 - 11:00 Hiroshi Shiraishi, Keio University, Faculty of Science and Technology
"Nonparametric estimation for optimal dividend barrier based on  Laplace transformation"

11:00 - 11:30 Junichi Imai, Keio University, Faculty of Science and Technology
    "Enhancement of QMC: severity and tail dimension乭

11:30 - 11:50 Taka-aki Koike, Keio University, Graduate school of Science and Technology
"Efficient Computation of Risk Contributions by using MCMC乭

11:50 – 13:00 Lunch Break

13:00 - 14:00 Paul Embrechts, ETH Zürich, Department of Mathematics
"Bernoulli and tail-dependence compatibility"

14:00 - 14:20 Coffee Break

14:20 – 14:50 Takuji Arai, Keio University, Department of Economics
"Convex risk measures for cadlag processes on Orlicz hearts"

14:50 - 15:20 Takaki Hayashi, Keio University, Graduate School of Business Administration
  "On lead-lag analysis with high-frequency data"

15:20 - 15:30 Break

15:30 – 16:00 Mihoko Minami, Keio University, Faculty of Science and Technology
"Doubly Cyclic Smoothing Splines and Analysis of Seasonal Daily Pattern of CO2 Concentration in Antarctica"

16:00 - 16:20 Tomoshige Nakamura, Keio University, Graduate school of Science and Technology
"The Problem of Treating Imputed Data as Observed Data When We Estimate the Effect of Exposure to Particulate Matter乭

16:20 - 16:40 Ayato Kashiyama, Keio University, Graduate school of Science and Technology
"Annual Maximum Rainfall Analysis Using Extreme Value Theory乭

16:40 - 16:45 Closing

 

BD21313_
       
   Abstracts
BD21313_


Professor Paul
Embrechts, ETH

Title: "Bernoulli and tail-dependence compatibility"

Abstract: Based on a practical example from stress testing within a solvency study of an insurance company, in this talk I will present a so-called inverse-dependence problem. By this I mean a general class of problems where a specific dependence structure between the various components of a vector of risks is given; based on this information, one has to characterize the risk vectors exhibiting this dependence structure, if at all such vectors exist. In particular for the current example, given a symmetric dxd matrix with [0,1] entries, can this matrix result as the lower (or upper) asymptotic tail-dependence coefficient matrix of a d-dimensional risk vector? A full solution of this problem is given. It is shown that this problem is closely related to the determination of matrices of second order cross-moments of general Bernoulli vectors.

 

Takuji Arai

Title: Convex risk measures for cadlag processes on Orlicz hearts

Abstract: Our purpose is to study properties and representations of convex risk measures for possibly unbounded cadlag processes. As the underlying space on which we define convex risk measures we consider spaces of cadlag processes whose supremum belongs to an Orlicz heart. In order to obtain concrete representations for such convex risk measures, we shall investigate representations of continuous linear functionals on the underlying space. Moreover, many examples of such risk measures are introduced. In particular, we deal with risk measures associated with hedging and pricing problems for American claims. Among others, we look into shortfall risk measure in detail.

 

Takaki Hayashi

Title: On lead-lag analysis with high-frequency data

Abstract:Lead-lag analysis in the high-frequency domain has not been drawn attention more than it deserves in the literature, albeit its potential importance in practice. The talk will first introduce approaches proposed recently by some researchers, then will present our own (H. and Koike (2016)). As an empirical application of these methodologies, we explore lead-lag relationships between market prices of identical stocks traded concurrently on multiple trading venues. Results on such "cross-market, single-asset" analyses with Japanese stock market datasets will be presented. (Joint work with Yuta Koike, Tokyo Metropolitan University.)

Key words: high-frequency data; lead-lag analysis; limit order book; nonsynchronicity; quadratic covariation; timestamp.

 

Junichi Imai

Title: Enhancement of QMC: severity and tail dimension

Abstract: In this talk we discuss how to further enhance numerical efficiency of a quasi-Monte Carlo method.It is well-known that the quasi-Monte Carlo method often outperforms a Monte Carlo method, especially in applying it to lower dimensional problems.In the paper, we introduce new notions; severity control, tail dimension control,and provide new principles, based on the notions, that enable us to improve the numerical efficiency of the quasi-Monte Carlo methods.These principles are useful when a function of interest has a complex form and/or it contains multiple discontinuity/non-differentiability.


Ayato Kashiyama

Title: Annual Maximum Rainfall Analysis Using Extreme Value Theory 

Abstract: In recent years, natural disasters occur frequently caused by extreme weather events. Extreme value theory aims at modeling maximum or minimum data, and in meteorological data, such data corresponds when natural disaster occurs. In this presentation, I will talk about extreme value theory and show an analytical result of annual maximum daily rainfall data from a region of Japan.

 

Taka-aki Koike

Title: Efficient Computation of Risk Contributions by using MCMC 

Abstract: In most of financial institutions, the risk of their portfolios is measured by the economic capital. For the purpose of more detailed risk analysis, it is necessary to decompose the portfolio-wide economic capital into the sum of risk contributions by unit exposures. Despite high practical demands, computing the risk contributions is a challenging task in general. No explicit solutions are available for most risk models. In this talk, we will introduce a Markov chain Monte Carlo (MCMC)-based estimator of risk contributions when economic capital is computed by Value-at-Risk. We will demonstrate that the estimator is available and high-performing in a wide variety of risk models.

 

Mihoko Minami

Title: Doubly Cyclic Smoothing Splines and Analysis of Seasonal Daily Pattern of CO2 Concentration in Antarctica

In order to flexibly estimate a continuously varying daily pattern throughout a year, we propose a doubly cyclic smoothing spline method and present its properties. We apply the method along with other tensor product smoothing methods to estimate the daily pattern of CO2 concentration at Syowa station in Antarctica and the effects of wind direction and velocity to CO2 concentration.

 

Tomoshige Nakamura

Title: The Problem of Treating Imputed Data as Observed Data When We Estimate the Effect of Exposure to Particulate Matter 

Abstract: When we estimate the effects of exposure to particulate matters, community health survey data are often used. In such a case, information of the amount of the exposure of each subjects need to be provided. However, in many cases, the amount of exposure of some subjects cannot be observed, so estimated values computed by some methods are imputed to them, and the effects of particulate matter is analyzed as if they were observed. In this talk, I will discuss problems of estimating the effect of exposure to Particulate Matter using imputed values as observed values, which have not been paid much attention by environmental epidemiologists.

 

Hiroshi Shiraishi

Title: Nonparametric Estimation for Optimal Dividend Barrier based on Laplace Transformation

Abstract: Dividends are defined as premium income whenever the insurance surplus attains a barrier level. Under the aggregate claims process taken as a compound Poisson model, optimal dividend barrier is defined as a barrier level that maximizes the expectation of the discounted dividends until ruin. We derive the optimal dividend barrier as a solution of a function following Gerber and Shiu (1997, 1998). Then, we consider the non-parametric estimation based on the empirical version of the Laplace transformation.