
THEMATIC PROGRAMS 

February 8, 2016  
Thematic Program on Quantitative
Finance:

June 21, 2010

Supported by :

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The industrial Academic (IA) forum is intended to allow University researchers in the field of finance & insurance mathematics (FinSurance) to interact with practitioners in the field. The morning (9am to 12:30pm) will consist of a number of highprofile plenary talks of approximately 60 minutes each, with amply time allocated to questions and answers as well as dialogue with the audience. These presentations will be accessible and aimed at a wide audience, with particular emphasis on risk evaluation and management for insurance companies issuing longdated guarantees.
The afternoon session will consist of 57 shorter technical presentation
of approximately 25 minutes each, primarily from junior researchers
(post docs, PhD. students), also in the field of FinSurance. The
organizers are currently soliciting speakers for the afternoon session
and anyone interested in presenting is welcome to contact any one
of the members of the organizing committee.
Arthur Fliegelman
A. Fliegelman & Associates, LLC
What Have We Learned in the Last Two Years: We Have Learned Haven't We?
Two years into the most severe financial crisis since the Great Depression, what have we learned, and for how long have we learned it? The “Great Moderation” led society into believing that economic cycles had been tamed or possibly even eliminated, and that associated risk levels had consequently declined to unprecedentedly low levels. We now know that economic cycles had not been tamed and that risk levels were actually much higher than were then believed with the resulting economic storm requiring virtually unprecedented levels of government intervention in worldwide financial systems. American life insurers as a whole, while very much impacted by the resulting economic tsunami, nonetheless performed better than many other types of financial institutions during this very difficult period. Insurers that were most adversely affected by the crisis typically exhibited the following behaviors:
(1) insufficient available liquidity especially at the holding company level;
(2) an expectation that high levels of equity risk could be managed through hedging or other similar programs; (3) a belief that increased levels of return on equity could be earned without incurring associated higher levels of risk; and (4) that excess capital could safely be used to fund stock repurchases or business acquisitions. Conversely, the most successful companies during this period were those that remained conservatively managed with sufficient internal flexibility to manage through extreme events without inflicting excessive harm on the organization.
Stanley R. Pliska
coauthors: I. Duarte, D. Pinheiro, and A. A. Pinto
Optimal Life Insurance, Consumption and Investment
We consider the problem of a wage earner who wants to make optimal, lifetime financial planning decisions for his family. With a random lifetime and given a specified income stream, he wants to purchase life insurance to protect his family against his death before retirement. He also invests a portion of his salary in a riskless asset as well as in a number of risky assets, and the balance of his income is consumed. The wage earner's problem is to find the optimal consumption, investment, and insurance purchase decisions in order to maximize the expected utility of (1) consumption, (2) the size of his estate in the event of premature death, and (3) the size of the estate at the time of retirement if he lives that long. With the risky securities modeled as multidimensional geometric Brownian motion, dynamic programming methods are used to obtain explicit solutions in the case of constant relative risk aversion utility functions, and some new results are presented together with the corresponding economic interpretations.
Robert R. Reitano
Professor of the Practice in Finance, Brandeis University
Risk Management of Long Liabilities in Insurance and Pensions
We begin with a brief introduction to the most common types of long financial liabilities and their risk attributes, as well as to the risk attributes of potential funding assets, and quantify how these risk attributes can compound or counteract in terms of the associated effects on risk to economic capital. These general ideas will then be illustrated in detail with two examples. The first example will focus on risk assessment and management for a Long Term Care (LTC) insurance block, focusing on an interest rate "hedging" model. Specifically, we illustrate an attempted interest rate hedge of a closed LTC block acquisition, evaluating the strengths and shortcomings of potential hedging approaches, and discuss the additional challenges associated with the issuance and hedging of incremental new business. The second example will address risk assessment and management for a Pension Plan, focusing on a "nonhedging funding" model. We begin with a hedge model assessment of risks, but focus on risk assessment within various typical “nonhedging” funding models. Within this framework, the focus is not on hedging the risks associated with guarantees, but on estimating the risk of plan failure.
Yuxiang Chong (University of Toronto)
Pricing catastrophe options under a regimeswitching model
The catastrophe options distinguish between a loss period [0,T1],during
which the catastrophes may happen, and a development period [T1,T2],
during which losses entered before T1 are reestimated. In this paper,
we will model cumulative catastrophe loss before T1 as a doubly
stochastic Poisson process. In order to incorporate the seasonal
effect on the occurrence of catastrophe events, we will let both
the intensity of Poisson process and the distribution of jump size
depend on the state of a continuous time Markov chain. During the
development period, losses are reestimated by a geometric Brownian
motion. In this setting we derive partial integrodifferential equations
for the prices of catastrophe options. Using Fourier transform techniques,
we are able to provide analytical pricing formulas for catastrophe
options.
Jessica Tsang Kwai Kew (York University)
Asset Allocation and Efficient Frontiers for Mortality Linked
Securities
Investment in mortalitylinked securities such as baskets of life
settlements offer investors returns that are largely uncorrelated
with other asset classes. The absence of academic work on the investment
characteristics of such baskets is an obstacle to institutional
investors entering this market. A Monte Carlo approach is used to
quantify the diversification benefits in incorporating life settlement
policies in a portfolio. The efficient frontier as well as questions
concerning an optimum asset allocation when we combine these securities
with equities and bonds in a portfolio is examined. In this presentation,
I will introduce life settlement which is a financial arrangement
when a third party buys the rights to the benefits of a life insurance
policy from an insured individual or policyholder. I will also present
some preliminary simulation results on how I model the behavior
of a portfolio over a period of 10 years when we vary the initial
allocation of equities/bonds and life settlements in the portfolio.
Finally, I discuss what I plan to do next to extend this basic model
into a more realistic model.
Zhongxian Men (University of Waterloo)
Multivariate stochastic volatility models: A Gibbs approach under
the inverse Wishart distribution
Multivariate stochastic volatility (MSV) models have been intensively
studied in the past several years. For the general case of the MSV
models, there are not many methods having been proposed. In this
talk, correlations are permitted between the innovations of the
asset returns and those of the volatility dynamics. We look at the
MSV model in a Bayesian framework by applying an inverse Wishart
distribution. The multistage slice sampler within the Gibbs algorithm
is proposed to sample the persistent parameters and latent variables.
Since the MetropolisHastings (MH) method is avoided, our algorithm
is more efficient and easier to operate.
Eddie Ng (University of Toronto)
Kernelbased Copula Processes
The field of timeseries analysis has made important contributions
to a wide spectrum of applications such as tidelevel studies in
hydrology, natural resource prospecting in geostatistics, speech
recognition, weather forecasting, financial trading, and economic
forecasts and analysis. Nevertheless, the analysis of the nonGaussian
and nonstationary features of timeseries remains challenging for
the current stateofart models.
This work proposes an innovative framework which leverages the theory
of copula, combined with a probabilistic framework from the machine
learning community, to produce a versatile tool for multiple timeseries
analysis. I coined this new model Kernelbased Copula Processes
(KCPs). Under the new proposed framework, various idiosyncracies
can be modeled parsimoniously via a kernel function for individual
timeseries, and longrange dependency can be captured by a copula
function. The copula function separates the marginal behavior and
serial dependency structures, thus allowing them to be modeled separately
and with much greater flexibility. Moreover, the codependent structure
of a large number of timeseries with potentially vastly different
characteristics can be captured in a compact and elegant fashion
through the notion of a binding copula. This feature allows a highly
heterogeneous model to be built, breaking free from the homogeneous
limitation of most conventional models. The KCPs have demonstrated
superior predictive power when used to forecast a multitude of data
sets from meteorological and financial areas. Finally, the versatility
of the KCP model is exemplified when it was successfully applied
to nontrivial classification problems unaltered.
Jinlian Wang (York University)
Ruin probability under stochastic mortality
Human beings are living longer than in the past. Their life expectancy
has been improved significantly since last century. The demise of
Defined Benefit Pensions forces more retirees to use defined contribution
pension plan to hedge the longevity risk. So it's possible for the
retirees to run out of wealth before run out of life while their
current standard of living is maintained. Hence to provide retirement
advice, life ruin probability becomes very important.
The stochastic hazard rate is studied when we compute life time
ruin probability. This is reasonable since the hazard rate is not
a constant, it has ups and downs. The problem is modeled using stochastic
differential equations, which is solved by converting the probability
into Partial Differential Equations (PDEs). Analytical solutions
can not be found to these highly nonlinear equations and numerical
methods are the only way to get the approximate ones. Alternative
Direction Implicit (ADI) method and Upwind Scheme are chosen to
solve the 2D ruin problem. These have significantly reduced the
computing time and saved lots of space.
The ruin probability under stochastic hazard rate and deterministic
hazard rate is compared. When the stochastic hazard rate collapses
to Gompertz Distribution, these two probabilities match very well.
The effect of the correlation between wealth and hazard rate is
studied. Our results show that when the correlation is positive,
the ruin probability is higher, which is consistent to the commonsense.
9:15  Opening remarks (Grasselli, Milevsky) 
9:2010:10  Robert R. Reitano Risk Management of Long Liabilities in Insurance and Pensions 
10:1511:05  Arthur Fliegelman What Have We Learned in the Last Two Years: We Have Learned Haven't We? 
11:1012:00  Stanley R. Pliska Optimal Life Insurance, Consumption and Investment 
121  Lunch 
1:00  1:20  Eddie Ng (University of Toronto) Kernelbased Copula Processes 
1:20  1:40  Jessica Tsang Kwai Kew (York University) Asset Allocation and Efficient Frontiers for Mortality Linked Securities 
1:40  2:00  Zhongxian Men (University of Waterloo) Multivariate stochastic volatility models: A Gibbs approach under the inverse Wishart distribution 
2:00  2:10  Break 
2:10  2:30  Jinlian Wang (York University) Ruin probability under stochastic mortality 
2:30  2:50  Yuxiang Chong (University of Toronto) Pricing catastrophe options under a regimeswitching model 
2:50  3:00  Discussion 
Full Name  University/Affiliation 
Bae, Tae Han  Algorithmics Inc. 
Chan, Paul  TD Securities 
Cheng, Wayne S.  TD Bank Financial Group 
Chong, Yuxiang  University of Toronto 
Dabrowski, Simon  York University 
Depoe, Kiel  York University 
Fahim, Arash  Ecole Polytechnique 
Fliegelman, Arthur  A. Fliegelman & Associates, LLC 
Fu, Stephen  TD Bank Financial Group 
Gapeev, Pavel  London School of Economics 
Grasselli, Matheus  McMaster University 
Grzesik, Robert  York University 
Guo, Weiwei  University of Toronto 
Habib, Faisal  QWeMA Group, Inc. 
Halaj, Grzegorz  ALM in Bank Pekao (UniCredit Group) 
Halaj, Grzegorz  ALM in Bank Pekao (UniCredit Group) 
Holm, Philip  TD Bank Financial Group 
Huang, Haohan  York University 
Huang, Huaxiong  York University 
Hughston, Lane  Imperial College 
Hurd, Tom  McMaster University 
Kang, John  York University 
Lin, X. Sheldon  University of Toronto 
Macqueen, Alexandra  QWeMA Group, Inc. 
Mausser, Helmut  Algorithmics Incorporated 
Milevsky, Moshe  York University, Schulich School of Business 
Mohandas, Deepesh  York University 
Ng, Eddie K.H.  University of Toronto 
O'Brien, Jonathan  University of Toronto 
Odette, Lou  Massachusetts Institute of Technology 
Peng, Xianhua  Fields Institute and York University 
Platen, Eckhard  University of Technology Sydney 
Pliska, Stanley R.  University of Illinois at Chicago 
Poon, Kingswood  TD Bank Financial Group 
Qiu, Daria  York University 
Reitano, Robert R.  Brandeis University 
Salisbury, Thomas  York University 
Shan, Yan  York University 
Sharma, Rita  York University 
Silla, Sebastiano  Polytechnic University of Marche 
Singh, Arjun  York University 
Straus, Daniel  York University 
Touzi, Nizar  Ecole Polytechnique 
Wang, Bei  York University 
Wang, Hao  York University 
Wang, Jinlian  York University 
Wiese, Anke  HeriotWatt University 
Wu, Panpan  University of Toronto 
Xue, Feifei  York University 
Yu, Ying  Boston University 
Zubelli, Jorge  IMPA 