First Annual China Futures and Derivatives Markets · PDF fileFirst Annual China Futures and...

First Annual China Futures and Derivatives Markets (CDMC)


Table of Contents

Introduction ................................................................................................................................... 1

Conference Schedule .................................................................................................................... 1

Presentation Sessions ................................................................................................................ 4

Session 1: Trading, quantitative methods and modeling of derivatives .................................. 4

Session 2: Hedging and risk management; Credit Spreads ..................................................... 4

Session 3: Trading, portfolio and hedge fund strategies .......................................................... 5

Session 4: Trading rules, derivative design and trading patterns; modeling; information

linkages among financial markets; hedge fund strategies ..................................... 5

Session 5: Market microstructure, liquidity and volatility; modeling ..................................... 6

Session 6: Credit spreads; policy reform and institutional constraints in Chinese markets .... 6

Session 7: Information linkages among markets; modeling; trading ...................................... 7

Session 8: Information linkages; Credit spreads; modeling of derivatives ............................. 7

Session 9: Modeling of derivatives; risk management ............................................................ 8

Session10: Hedging and risk management; hedge fund strategies .......................................... 8

Panel Discussion: Chinese Derivatives Markets ....................................................................... 8

Keynote Speakers ......................................................................................................................... 9

Conference Organization .............................................................................................................. 12

Detailed Information on Paper Presentations ............................................................................... 14

Introduction of International Business School of Suzhou (IBSS) ................................................ 26

Maps of Shanghai and Suzhou Regions, Suzhou, IBSS, Hotel .................................................... 27

CDMC Travel directions ............................................................................................................... 29


1

Introduction

The Journal of Futures Markets (JFM), the China Finance Review International (CFRI), the Review of Futures

Markets (RFM), the International Business School Suzhou (IBSS) and the Research Institute of Quantitative Finance

(RIQF) of Xi’an Jiaotong - Liverpool University (XJTLU), the Volatility Institute at New York University Shanghai

(VINS), and China Financial Futures Exchange (CFFEX) are jointly organizing a conference on derivative securities

and markets. Academic research on derivative securities and markets will be presented. This conference aims to

bring academics, financial professionals and policy makers from China and abroad together to discuss current research

on futures, options and other derivative markets. Papers will be presented in English, and selected papers will be

published on special/regular issues of the JFM, the CFRI and the RFM. In addition to the paper presentation sessions,

a panel discussion will be held in the afternoon of May 20, 2016. The panel, consisting of well-regarded academics,

seasoned practitioners and senior policy makers, will offer in-depth perspectives on China’s derivatives markets.

Conference Schedule – Registration

Pre-Conference Registration (May 18, 2016 – May 19, 2016)

Time

Program

Venue

2016.5.18

13:30-18:30 Registration XJTLU Conference Centre

2016.5.18

18:30-20:00 Dinner Buffet XJTLU Conference Centre

2016.5.19

08:30-11:30 Registration XJTLU Conference Centre /BA216


2

Conference Schedule – Day One

Conference – Day One (May 19, 2016)

Time Program Venue

08:30-11:30 Registration XJTLU Conference Centre/BA216

08:30-08:35 Qi Deng

Opening BA216

08:35-08:50 Xiaogang Zhang

Welcome Speech BA216

08:50-09:10 Photo Taking Main entrance to IBSS building

09:10-09:40

Hong Yan

Keynote Speech,

Shanghai Advanced

Institute of Finance

(SAIF), Shanghai

Jiaotong University

(SJTU)

BA216

9:40-10:10

Robert I. Webb

Keynote Speech,

University of Virginia,

Editor of Journal of

Futures Markets

BA216

10:10-10:30 Tea Break BA216

10:30-12:30 Session 1 + 2 BA216/BA104

12:30-14:00 Luncheon XJTLU Conference Centre

14:00-15:30 Session 3 + 4 BA216/BA104

15:30-15:45 Tea Break BA216

15:45-17:15 Session 5 + 6 BA216/BA104

17:15-17:30 Break XJTLU Conference Centre

17:30-20:00 Dinner Banquet XJTLU Conference Centre


3

Conference Schedule – Day Two

Conference – Day Two (May 20, 2016)

Time Program Venue

09:00-09:30

Jianye Wang

Keynote Speech,

Volatility Institute at

NYU Shanghai, Silk

Road Fund Co., Ltd.

BA216

9:30-10:00

Stephen Figlewski

Keynote Speech,

New York University

Stern School of

Business

BA216

10:00-10:30 Tea Break

10:30-12:30 Session 7 + 8 BA216/BA104

12:30-14:00 Luncheon XJTLU Conference Centre

14:00-15:00 Session 9 + 10 BA216/BA104

15:00-15:15 Tea Break BA216

15:15-16:15 Panel Discussion BA216


4

Presentation Sessions – Morning, May 19, 2016

Session 1: Trading, quantitative methods and modeling of derivatives

Time: 10:30-12:30, May 19 Venue: BA216 Chair: Michael Chng

1-1

10:30-11:00

Title Exercise Boundary Violations in American-Style Options: The Rule, not the

Exception

Speaker Stephen Figlewski, New York University Stern School of Business

Discussant Michael Chng, Xi’an Jiaotong - Liverpool University

1-2

11:00-11:30

Title Option Return Predictability

Speaker Cao Jie, Chinese University of Hong Kong

Discussant Stephen Figlewski, New York University Stern School of Business

1-3

11:30-12:00

Title Empirical Patterns of Time Value Decay in Options

Speaker Ryan McKeon, University of San Diego

Discussant Yiling Zha, School of Social Sciences, University of Dundee, UK

1-4

12:00-12:30

Title Explicit Solutions for Dynamic Portfolio Choice in Jump-Diffusion Models

with Multiple Risky Assets and State Variables and Their Applications

Speaker Yi Hong, Xi’an Jiaotong - Liverpool University

Discussant Bin Li, Economics and Management School, Wuhan University

Session 2: Hedging and risk management; Credit Spreads

Time: 10:30-12:30, May 19 Venue: BA104 Chair: Zhenlong Zheng

2-1

10:30-11:00

Title Stochastic Seasonality in Commodity Prices: The Case of U.S. Natural Gas

Prices

Speaker Zhen Zhu, University of Central Oklahoma

Discussant Yingying Wu, Xi’an Jiaotong - Liverpool University

2-2

11:00-11:30

Title Corporate Governance and Default Risk in Financial Firms over the Post

Financial Crisis Period: International Evidence

Speaker Lorne Switzer, Concordia University

Discussant Rong Chen, Department of Finance, Xiamen University

2-3

11:30-12:00

Title Dynamic dependence structure between agricultural commodity futures in

China: a copula approach based on high-frequency data

Speaker Steven Li, RMIT University

Discussant Tomáš Tichý, VŠB-Technical University of Ostrava, Czech Republic

2-4

12:00-12:30

Title AVIX A New VIX based on Stochastic Interest Rates and Adaptive Screening

Mechanism

Speaker Zhenlong Zheng, Department of finance, Xiamen University

Discussant Xiaoquan Liu, University of Nottingham Ningbo


5

Presentation Sessions – Afternoon One, May 19, 2016

Session 3: Trading, portfolio and hedge fund strategies

Time: 14:00-15:30, May 19 Venue: BA216 Chair: Ahmet Goncu

3-1

14:00-14:30

Title Order imbalance and futures price in high-frequency trading: China's

experience

Speaker Bohan Li, School of Finance, Central University of Finance and

Economics

Discussant Juan Tao, IBSS, Xi’an Jiaotong - Liverpool University

3-2

14:30-15:00

Title Foreign Indexes trading in SGX: An empirical analysis on price discovery

Speaker Sili Zhou, Singapore Management University

Discussant Canlin Li, Federal Reserve Board

3-3

15:00-15:30

Title Pairs Trading with Commodity Futures

Speaker Ahmet Goncu, Xi’an Jiaotong - Liverpool University

Discussant Yahua Xu, Auckland University of Technology

Session 4: Trading rules, derivative design and trading patterns; modeling; information linkages among

financial markets; hedge fund strategies

Time: 14:00-15:30, May 19 Venue: BA104 Chair: Zhongguo Zhou

4-1

14:00-14:30

Title Option Pricing with Realized EGARCH Model: An Analytical

Approximation Approach

Speaker Tianyi Wang, University of International Business and Economics, China

Discussant Hui Lin, School of Business, Nanjing University

4-2

14:30-15:00

Title The Effect of Options on Liquidity and Asset Returns

Speaker Cheng Zhang, London School of Economics

Discussant Mick Swartz, Marshall School of Business, University of Southern California

4-3

15:00-15:30

Title A VECM/VAR-DCC/ADCC framework and its application in

Black-Litterman portfolio optimization

Speaker Sven Meyer, Xi’an Jiaotong - Liverpool University

Discussant Lorne Switzer, Concordia University


6

Presentation Sessions – Afternoon Two, May 19, 2016

Session 5: Market microstructure, liquidity and volatility; modeling

Time: 15:45-17:15, May19 Venue: BA216 Chair: Xingguo Luo

5-1

15:45-16:15

Title Do Chinese bond ETFs improve the liquidity of the underlying bonds?

Speaker Xingguo Luo, Zhejiang University

Discussant Xintong Zhan, Chinese University of Hong Kong

5-2

16:15-16:45

Title Implied Risk Aversion under Different Utility Functions: Influential Factors

and Implicit Information

Speaker Rong Chen, Department of Finance, Xiamen University

Discussant Cheng Zhang, London School of Economics

5-3

16:45-17:15

Title An Option Pricing Model with Liquidity Adjustment of Underlying Asset

Speaker Hui Lin, School of Business, Nanjing University

Discussant Jie Zhang, Xi’an Jiaotong - Liverpool University

Session 6: Credit spreads; policy reform and institutional constraints in Chinese markets

Time: 15:45-17:15, May 19 Venue: BA104 Chair: Rachid Belhachemi

6-1

15:45-16:15

Title A New Take on the Relationship between Interest Rates and Credit Spreads

Speaker Xioaquan Jiang, Department of Finance, Florida International

University

Discussant Feng Jiao, McGill University

6-2

16:15-16:45

Title The Performance of China’s Stock Market Price Limits: Noise Mitigator or

Noise Maker?

Speaker Juan Tao, Xi’an Jiaotong - Liverpool University

Discussant Vito Mollica, Macquarie University

6-3

16:45-17:15

Title Index Futures Trading and Spot Market Liquidity: Evidence from the Recent

Chinese Stock Market Crisis

Speaker Qian Han, Xiamen University

Discussant Yi Hong, Xi’an Jiaotong - Liverpool University


7

Presentation Sessions – Morning, May 20, 2016

Session 7: Information linkages among markets; modeling; trading

Time: 10:30-12:30, May 20 Venue: BA216 Chair: Ping Li

7-1

10:30-11:00

Title Pricing of Contingent Convertible Bonds Based on Copulas and Bivariated

Simulation

Speaker Ping Li, Beihang University

Discussant Cihangir Kan, Xi’an Jiaotong - Liverpool University

7-2

11:00-11:30

Title Multi-Market Trading and Cross-Asset Integration

Speaker Feng Jiao, McGill University

Discussant Han Qian, Xiamen University

7-3

11:30-12:00

Title Margin Policy in Futures Trading: The American System vs. The Chinese

System

Speaker Canlin Li, Federal Reserve Board

Discussant Xiaoye Bao, Shanghai Normal University

7-4

12:00-12:30

Title Asymmetry in the Permanent Price Impact of Block Purchases and Sales

Theory and Empirical Evidence

Speaker Vito Mollica, Macquarie University

Discussant Sili Zhou, Singapore Management University

Session 8: Information linkages; Credit spreads; modeling of derivatives

Time: 10:30-12:30, May 20 Venue: BA104 Chair: Biao Guo

8-1

10:30-11:00

Title The Relationship among China's Fuel Oil Spot, Futures and Stock Markets

Speaker Ziyi Zhang, School of Economics and Management, Beihang University,

China

Discussant Shilei Niu, Xi’an Jiaotong - Liverpool University

8-2

11:00-11:30

Title Variance and Skew Risk Premiums for the Volatility Market: The VIX

Evidence

Speaker Yahua Xu, Auckland University of Technology

Discussant Xiaoquan Jiang, Florida International University

8-3

11:30-12:00

Title Does the Introduction of One Derivative Affect Another Derivative? The

Effect of Credit Default Swaps Trading on Equity Options

Speaker Xintong Zhan, Chinese University of Hong Kong

Discussant Ahmet Goncu, Xi’an Jiaotong - Liverpool University

8-4

12:00-12:30

Title Within-sector credit risk spillover in Asia

Speaker Yiling Zha, School of Social Sciences, University of Dundee, UK

Discussant Yun Yin, University of East Anglia


8

Presentation Sessions – Afternoon, May 20, 2016

Session 9: Modeling of derivatives; risk management

Time: 14:00-15:00, May 20 Venue:BA216 Chair: Matthew Richardson

9-1

14:00-14:30

Title Volatility forecasting in the Chinese commodity futures market with intraday

data

Speaker Xiaoquan Liu, University of Nottingham Ningbo

Discussant Steven Li, RMIT University

9-2

14:30-15:00

Title Timing and hedging portfolio strategies with derivatives

Speaker Tomáš Tichý, VŠB-Technical University of Ostrava, Czech Republic

Discussant Zhen Zhu, University of Central Oklahoma

Session 10: Hedging and risk management; hedge fund strategies

Time: 14:00-15:00, May 20 Venue:BA104 Chair: Shuguang Zhang

10-1

14:00-14:30

Title Three Novel Factors Affecting Hedge Fund Returns

Speaker Mick Swartz, Marshall School of Business, University of Southern

California

Discussant Jie Cao, Chinese University of Hong Kong

10-2

14:30-15:00

Title Do Trend Following Strategies Work in China Futures Markets

Speaker Bin Li, Economics and Management School, Wuhan University

Discussant Ryan McKeon, University of San Diego

Panel Discussion: Chinese Derivatives Markets

Time: 15:30-16:30, May 20 Venue: BA216 Chair: Xin Zhou

15:30-16:30

Topic Chinese Derivatives Markets

Speakers Hualiang Chen, Jeff Nie, Qi Wang, Zhenlong Zheng

Moderator Xin Zhou, Volatility Institute of New York University Shanghai (VINS)


9

Keynote Speakers

Robert I. Webb

Robert I. Webb is a professor of Finance at the University of Virginia and a Paul

Tudor Jones II Research Professor at the McIntire School of Commerce of the

University of Virginia. Additionally, he is the editor of the Journal of Futures

Markets, an academic finance journal that specializes in articles on derivative

securities and markets. He received his PhD and MBA degree at University of

Chicago, and he got his BBA degree from University of Wisconsin at Eau Claire.

Previously he was a consultant to the World Bank and he traded on the floor at the

Chicago Mercantile Exchange. He also served as a Senior Financial Economist at

both the Executive Office of the President, Office of Management and Budget, and the Commodity Futures

Trading Commission.

Hong Yan

Hong Yan is Professor of Finance at Shanghai Advanced Institute of Finance (SAIF),

Shanghai Jiao Tong University, where he served as Deputy Dean and as Deputy

Director for China Academy of Financial Research. Before he joined SAIF full-time,

Prof. Yan was a tenured faculty member at the Moore School of Business in the

University of South Carolina, USA. Previously, he was on the faculty at the

University of Texas at Austin and spent a year as a visiting academic scholar at the

U.S. Securities & Exchange Commission (SEC). Professor Yan holds a Ph.D. in

Finance from the University of California, Berkeley, and a Ph.D. in Applied Physics

from the University of Michigan. His research focuses on the areas of credit risk, asset pricing, derivatives

securities, portfolio choice, and risk management. He also studies financial intermediaries such as mutual funds,

hedge funds and financial analysts. Prof. Yan has published in top academic journals such as Journal of Finance,

Journal of Financial Economics, and Review of Financial Studies. Professor Yan currently serves as a Managing

Editor of the International Review of Finance.

Jian-Ye Wang

Jian-Ye Wang is Professor of Economics and Director of the Volatility Institute at

NYU Shanghai. Currently, Professor Wang is also the Managing Director, Silk Road

Fund Co., Ltd. Prior to joining NYU Shanghai, he was Economic Counselor and

Chief Economist of the Export-Import Bank of China (China Exim Bank), held

various positions at the International Monetary Fund, and also served as Adjunct

Professor of Economics at Peking University’s School of Economics. He holds a

PhD and MA in Economics from Columbia University and a BA from Peking University. Professor Wang’s

research areas include macroeconomics, international economics, and development economics. Recent

publications include Debt, Currency and Related Reforms (China Financial Publishing House, 2012).


10

Stephen Figlewski

Stephen Figlewski is a professor of finance at New York University's Stern School

of Business. In addition to working at NYU - Stern, Professor Figlewski was a Vice

President at the First Boston Corporation, responsible for research on equity

derivatives products, and has been a market maker in stock index options at the New

York Stock Exchange. He was also a Visiting Associate Professor at the University

of California at Berkley. Professor Figlewski received his Bachelor of Arts in

Economics, summa cum laude, from Princeton University and his Doctor of

Philosophy in Economics from the Massachusetts Institute of Technology. His

primary research interests include Derivatives, Risk Management, and Financial Markets. Professor Figlewski

has published several books, including Risk Management: The State of the Art, Financial Options: From Theory

to Practice, and Hedging with Financial Futures for Institutional Investors: From Theory to Practice. He is the

founding editor of Journal of Derivatives, and has been published in many others, including the Journal of

Finance and the Journal of Financial Economics.

Conference Organizers

Xiaogang Zhang

Xiaogang Zhang is currently Executive Vice President, China Financial Futures

Exchange. Mr. Zhang started his career in futures industry in 1993 in then

Shanghai Metal Exchange. From 1993 to 1998, he worked as analyst in Information

Department of Shanghai Metal Exchange. When Shanghai Metal Exchange merged

with other two exchanges in 1999 to form the current Shanghai Futures Exchange, he

worked in R&D department as senior analyst, and since 2002, as the director of

Strategic Planning Department. In 2006, Mr. Zhang joined the newly established

China Financial Futures Exchange as the head of R&D Department. Since October 2015, Mr. Zhang has been

appointed as EVP, taking charge of the strategic planning, research, and also the international communication

and cooperation.

Xin Zhou

Xin Zhou is currently the Executive Director of Volatility Institute at New York

University at Shanghai. Prior to joining NYU Shanghai, Dr. Zhou was an Assistant

Professor of Finance at Fudan University from 2009 to 2014. Before that, he was the

portfolio manager of an incubator fund specializing in option trading on currency and

commodity futures at Cogent Partners from 2007 to 2009. Dr. Zhou’s research

interests include empirical asset pricing, market anomalies, risk modeling and

management, and private equity investment. His research has been published in

multiple finance journals. Dr. Zhou received his Ph.D. in Finance and MA in

Electrical Engineering from the University of Texas at Dallas, MA and BS in Physics from the University of

Science and Technology of China.


11

Qi Deng

Qi Deng is an associate professor of finance with IBSS-XJTLU. He co-founded

Altobeam, a multi-million dollar technology firm based in Beijing, and leads its

financial and business development activities. He serves as the chief quantitative

scientist for Quanpass, a Shanghai-based multi-million dollar quantitative hedge

fund. His research interests are quantitative modeling, trading and hedging with a

variety of securities. Prof. Deng earned a BS in Physics from Peking University,

a MBA from University of Michigan, and a DBA from Grenoble Ecole de

Management. He holds a Certificate in Quantitative Finance (CQF).

Sven Meyer

Sven Meyer is the Event officer for the 1

st China Derivatives Markets Conference.

He is a PhD candidate in quantitative finance with IBSS-XJTLU, and a managing

partner at Zhan'ao Investment Management Ltd. Sven has been in China for over

20 years on business and work assignments and has attained near native Chinese

language skills and experienced acculturation by Chinese society. Previously, Sven

was employed as the Director of Derivatives Innovation at Shanghai Institute of

Futures and Derivatives (Shanghai Futures Exchange), where he co-organized two

global derivatives markets research conferences (ICFDM).

Ahmet Goncu

Ahmet Goncu is as an associate professor in financial mathematics and serving as

the Director of the Institute of Quantitative Finance at Xian Jiaotong Liverpool

University. Dr. Goncu received his PhD in Financial Mathematics from Florida

State University. He has extensive research experience in quantitative finance and

financial engineering with 25 publications in respected mathematics, finance and

economics journals including Quantitative Finance, Review of Futures Markets,

Journal of Applied and Computational Mathematics, Applied Economics, etc. His recent research publications

focus on model driven statistical arbitrage trading and stochastic modeling in finance.

Rachid Belhachemi

Rachid Belhachemi is a lecturer with the Mathematical Sciences department of

XJTLU. His research interests and recent publications are in statistics, pension

modeling, asset pricing and risk management. Dr. Belhachemi earned a M.Sc in

Statistics from University of Toronto, and a PhD in Mathematics from Oklahoma

State University.


12

Conference Organization

Hosting Institutions

International Business School Suzhou (IBSS), Xi’an Jiaotong - Liverpool University (XJTLU)

Research Institute of Quantitative Finance (RIQF), XJTLU

Volatility Institute at NYU Shanghai (VINS)

China Financial Futures Exchange (CFFEX)

Sponsoring Journals

Journal of Futures Markets (JFM)

China Finance Review International (CFRI)

Review of Futures Markets (RFM)

Conference Organizers

Xiaogang Zhang China Financial Futures Exchange

Xin Zhou Volatility Institute of New York University Shanghai

Qi Deng Xi'an Jiaotong - Liverpool University

Sven Meyer Xi'an Jiaotong - Liverpool University

Ahmet Goncu Xi'an Jiaotong - Liverpool University

Rachid Belhachemi Xi'an Jiaotong - Liverpool University

Program Committee

Rachid Belhachemi Xi'an Jiaotong - Liverpool University

Michael Chng Xi'an Jiaotong - Liverpool University

Qi Deng Xi'an Jiaotong - Liverpool University

Sven Meyer Xi'an Jiaotong - Liverpool University

Ahmet Goncu Xi'an Jiaotong - Liverpool University

Biao Guo Renmin University of China

Yi Hong Xi'an Jiaotong - Liverpool University

Jari Kappi Xi'an Jiaotong - Liverpool University

Ping Li Beihang University

Xingguo Luo Zhejiang University

Athanasios Pantelous University of Liverpool

Juan Tao Xi'an Jiaotong - Liverpool University

Yingying Wu Xi'an Jiaotong - Liverpool University

Hong Yan Shanghai Advanced Institute of Finance, Shanghai Jiao Tong University

Shuguang Zhang University of Science and Technology China

Zhenlong Zheng Xiamen University

Xin Zhou Volatility Institute of New York University Shanghai

Zhong-guo Zhou California State University Northridge


13

Contacts

Sven Meyer, (86)-18930891320 Shi Meiting, (86)-18862282896

E-mail: [email protected]


http://www.xjtlu.edu.cn/en/departments/academic-departments/international-business-school-suzhou/

mailto:[email protected]


14

Detailed Information on Paper Presentations

Session 1: Trading, quantitative analysis, methods and modeling of derivatives

1-1. Exercise Boundary Violations in American-Style Options: The Rule, not the Exception

Robert Battalio1, Stephen Figlewski

2, Robert Neal

3*

1Mendoza School of Business, University of Notre Dame*

2Stern School of Business, New York University

3Kelley School of Business, Indiana University, Indianapolis, IN

* E-mail: [email protected]

Abstract: An exercise boundary violation (EBV) occurs when the current best bid price in the market for

an American option is below intrinsic value. Selling at an EBV price leaves money on the table and the

buyer receives an arbitrage profit, since intrinsic value can be recovered immediately by exercising the

option. In a frictionless market, a call on a non-dividend paying stock will always be priced above intrinsic

value prior to expiration, so early exercise of an American call is always irrational. But we find that EBVs

in the real world are pervasive, large, and highly persistent throughout the trading day. Tick data for option

prices during March 2010 show that 48.6% of all bid quotes for in-the-money calls were below intrinsic

value, rising to nearly 100% for deep in-the-money contracts near to expiration. In such cases, if an investor

needs to liquidate an option position before maturity, the only way to recover even intrinsic value is by

exercise. It can become irrational not to exercise, and traders appear to do so in response to EBVs. More

EBV trades occur when the violations are narrow, and there are more exercises when the discrepancy is

wide, although 11.5% of trading volume for in-the-money options during the month still occurred below

intrinsic value, producing an opportunity loss for the sellers of about $39 million.

Our results reverse standard theory on American option valuation and optimal exercise strategy: Early

exercise is very often rational in the real world and an American option on a non-dividend paying stock

should therefore command a premium over the equivalent European contract because of its greater liquidity.

Our results also suggest that for estimating implied volatility, marking an in-the-money option position to

market, and evaluating customary measures of market liquidity, it makes sense to pay attention to EBVs,

perhaps by using an option's intrinsic value in place of an EBV bid quote.

1-2. Option Return Predictability

Jie Cao1*, Bing Han

2, Qing Tong

3, Xintong Zhan

4

1The Chinese University of Hong Kong

2University of Toronto

3Singapore Management University

4The Chinese University of Hong Kong


Abstract: We show the cross-section of equity option returns can be predicted by a variety of underlying

stock characteristics and firm fundamentals, including idiosyncratic volatility, past stock returns,

profitability, cash holding, new share issuance, and dispersion of analyst forecasts. Such predictability is not

mechanically inherited from the stock market because these variables do not significantly predict stock



15

returns in our sample, and our results hold for delta-hedged calls and puts in the same directions. We

document new option trading strategies that are profitable even after transaction costs. These profits are

robust across different market conditions and subsamples. They cannot be explained by existing stock

market risk factors including market volatility risk or tail risk, or individual stock volatility risk premium,

jump risk and option illiquidity. These systematic patterns in the relative valuation of options and the

underlying stocks have important implications for option valuation and option market efficiency.

1-3. Empirical Patterns of Time Value Decay in Options

Ryan McKeon

Department of Finance, School of Business Administration, University of San Diego


Abstract: I conduct and empirical analysis of the pattern of time value decay in listed equity options,

considering both call and put options and various degrees of moneyness for both classes of options. I find

that empirical patterns differ from model or theory based predictions in important ways. While model-based

simulations commonly predict slow decay in time value with rapidly accelerating decay as the option

approaches expiry, the empirical results show that time value decay is often very rapid early in the holding

period of an option. This result holds regardless of holding period duration (30, 60 and 90 days are

considered) and moneyness. Further analysis reveals the differences between empirical results and

model-based predictions are largely due to changes in stock price during the holding period, with deviations

in the market price of the option from model-based prices being a minor factor. The only options to exhibit

rapidly accelerating time value decay are out-the-money calls or puts purchased in low-volatility

environments. I discuss the implications of these results for the management of option positions.

1-4. Explicit Solutions for Dynamic Portfolio Choice in Jump-Diffusion Models with Multiple

Risky Assets and State Variables and Their Applications

Yi Hong1*, Xing Jin

2

1International Business School Suzhou, Xi'an Jiao tong-Liverpool University

2Warwick Business School, University of Warwick


Abstract: This paper studies the optimal portfolio selection problem in jump-diffusion models where there

are potentially a large number assets and/or state variables. More specifically, we derive closed form

solution for the optimal portfolio weights up to solving a set of ordinary differential equations (ODEs). First,

our results generalize Liu (2007) models by incorporating jumps in both stock returns and state variables.

Second, we extend Jin and Zhang (2012) results by including jumps in state variables. To examine the

effects of jump on an investor's behavior, we then apply our results to two examples. In the first application,

we propose a particularly tractable self-exciting jump intensity process in a double jump model and

explicitly solve the optimal investments in variance swaps. The second application investigate the impact of

jump in stock return on cash-bond-stock mix by revisiting the bond/stock ratio puzzle in a jump-diffusion

model.

Session 2: Hedging and risk management; Credit Spreads

2-1. Stochastic Seasonality in Commodity Prices: The Case of U.S. Natural Gas Prices

Song Zan Chiou-Wei1, Zhen Zhu

2*


16

1Professor, National Kaohsiung University of Applied Sciences Kaohsiung, Taiwan

2 *Professor, Department of Economics, University of Central Oklahoma Edmond, OK, USA


Executive Summary: Many commodity forward and futures prices exhibit seasonable patterns, not just in

the term structure, but also in the evolution of the prices. Existing theories on commodity prices assumes

deterministic seasonality in the forward curve. Our study intends to provide stylized facts on commodity

price seasonality and explain the seasonality based on economic theory, with an application to an energy

commodity – natural gas. Our analysis suggests that there are some major differences between the

seasonality pattern in the spot price and futures prices. One of the evidence is that the seasonality in spot

prices appears to be non-deterministic and this observation is consistent with the argument that seasonality

should be stochastic rather than deterministic as the stochastic nature of the market demand and supply

should lead to stochastic seasonality in commodity prices. However, this is at odds with the assumption of

the deterministic seasonality in futures prices as futures prices are based on spot prices in commodity

pricing models. The seasonality in the spot markets should have a direct relationship with the seasonality in

the futures prices. Therefore, our first attempt, after providing the stylized facts about seasonality, is to test

whether seasonality is deterministic or stochastic using the HEGY seasonal unit root test applied to the

monthly data. This is important for the correct theoretical modeling and understanding of the commodity

prices.

Furthermore, we intend to explain the sources of the stochastic seasonality. This, in a sense, provides the

explanations as to why seasonality is not deterministic but rather stochastic. As a first step, in this paper, we

examine the stochastic nature of several fundamental variables including production, consumption, natural

gas underground storage and weather variables such as HDD and CDD. The stochastic nature of these

variables are expected to largely influence seasonality and cause seasonality to be stochastic rather than

deterministic. Our future studies will investigate the cointegration nature of the gas price and these

fundamental variables. In addition, we intend to document the changing seasonal spreads as another

dimension of the seasonal characteristics of the natural gas prices.

This paper contributes to the literature of commodity pricing by providing a more comprehensive study of

the commodity price seasonality by first examining the stylized facts on seasonality and then provide an

explanation of the sources of the seasonality. This represents, to our knowledge, one of the first such

attempts at the specific reasons behind the seasonal variation of the commodity prices.

Our main focus is on the understanding of the causes of seasonal variations based on economic theory of

storable commodities. A better understanding of the seasonality in commodity prices contributes to the

successful modeling of both the spot and futures prices, as well as the prices of commodity options. It also

has significant impact on trading strategies and risk management practices for market participants in the

commodity markets.

2-2. Corporate Governance and Default Risk in Financial Firms over the Post Financial

Crisis Period: International Evidence

Lorne N. Switzer1*, Jun Wang

2, Qiao Tu

3 *


Abstract: This paper looks at the relationship between default risk and corporate governance for financial


17

firms domiciled outside of the US in the post financial crisis period, and default risk is measured by both

credit default swap spreads (CDS) and Merton default probabilities. Institutional ownership (board size) is

negatively (positively) related to default risk using both measures across countries; board independence

(insider holdings and CEO duality) lowers (increases) default probabilities but not CDS. Moreover, we find

that the impacts of various governance variables on firm default risk are continent-specific: most

governance variables are significantly related to default risk for Asian firms; for European firms, however,

only board size and institutional holdings are significant.

2-3. Dynamic dependence structure between agricultural commodity futures in China: a

copula approach based on high-frequency

Ke Yang1, Steven Li

2*, Feng ping Tian3, Lang nan Chen

4

1College of Economics and Management, South China Agricultural University, China

2 Graduate School of Business and Law, RMIT University, Australia

3 International School of Business and Finance, Sun Yat-sen University, China

4Lingnan College, Sun Yat-sen University, China


Abstract: We propose a semi-parametric copula model based on high-frequency data to examine the daily

and intraday dependence structure between four major agricultural commodity futures in China over the

period of 2006-2014. This paper employs the copula approach with high frequency data and reveals several

stylized facts regarding the Chinese agricultural commodity futures market. First, we find that the daily

dependence between the agricultural commodity futures in China is time-varying, slightly asymmetric and

significant most of the time during the sample period. Second, this dependence and its asymmetry are more

pronounced during the world food crisis (2007-2008) and the global financial crisis (2008-2011). Third, the

intraday dependence structure between the major agricultural commodity futures often exhibits a lopsided

inverted U-shaped pattern with relatively lower dependence levels around the opening and the closing time,

and a peak around the midday.

2-4. AVIX: A New VIX based on Stochastic Interest Rates and Adaptive Screening

Mechanism

Rong Chen1, Zheng yun Jiang

2, Zhen long Zheng

3*

123Xiamen University, Economics School, Department of Finance, China.

E-mail : [email protected]

Abstract: We propose a new VIX index, AVIX by introducing stochastic interest rate process and an

adaptive option filtering mechanism. We find that the option filter is able to choose the most informative

option data automatically to calculate our model-free implied volatility index, thus making the most use of

the information content implied by option market. Our empirical study based on China SSE 50ETF options

demonstrates that compared with the traditional VIX, AVIX is a more efficient forecast for future realized

volatility, and AVIX is a better reflection of implied volatility and market sentiment, as a more informative,

sensitive and forward-looking market volatility index.

Session 3: Trading, portfolio and hedge fund strategies

3-1. Order imbalance and futures price in high-frequency trading: China's experience

Rui Chen1, Li Bohan

2*, Xu Wei3


18

123School of Finance, Central University of Finance and Economics, China


Abstract：Using the high-frequency data of China’s commodity futures, we find that there is significant

correlation between order imbalance and futures prices: The greater the order imbalance is, the greater the

probability of a price change. Under different time scales, the impact of order imbalance shows different

characteristics. By establishing trading strategies, we find that the impact of order imbalance is temporary

and limited. The reasons for this phenomenon is that in China's futures market, investors can only get a

snapshot of price information and this kind of information sometimes deviates from the real market status.

High-frequency traders are inclined to trade opposite on the market based on it and shock the price.

3-2. Foreign Indexes trading in SGX: An empirical analysis on price discovery

Xiaoming Wang1, Nelson K.L. Yap

2, Sili Zhou

3*

1Institute of Finance and Economics, Shanghai University of Finance and Economics

23Lee Kong Chian School of Business, Singapore Management University


Abstract: One of the most interesting features of the Singapore Exchange (SGX) is the trading of foreign

index futures. In this paper, we examine four popular indices - Nikkei 225 Index, MSCI Taiwan Index,

CNX Nifty Index and the FTSE China A50 Index - and estimate the contribution SGX gives to the price

discovery of these indices. In contrary to standard theory and evidence, we show that smaller bid-ask spread,

lower minimum lots and cheaper transaction cost do not necessary improve information efficiency. These

results may shed some light on the usefulness of the role of an international financial center on the price

discovery of foreign indices.

3-3. Pairs Trading with Commodity Futures

Ahmet Goncu¹*, Yurun Yang²

¹Institute of Quantitative Finance, XJTLU, China

²Xi’an Jiaotong Liverpool University, Suzhou, China

Email: [email protected]

Abstract: In this study we compare the probability of different pairs selection and optimal trading methods

using the dataset of the Chinese commodity futures. Optimal pairs trading rules that are recently developed

in the literature are compared in terms of the statistical arbitrage profits generated via an out-of-sample back

testing. We extend the univariate mean-reverting stochastic spread models used in the literature to the

multidimensional case, where the parameter estimation problem reduces to a vector autoregressive model in

discrete time. In the multidimensional setting the allocation of wealth into different spread positions can be

done using the mean-variance portfolio optimization framework. Furthermore, the investor's choice for

investment in the market portfolio versus the pairs trading (statistical arbitrage) fund depends on the

investment horizon. Our analysis implies that a hedge fund with short investment horizon finds statistical

arbitrage trading optimal, whereas a pension fund's longer investment horizon implies a buy and hold

strategy as the optimal choice.

Session 4: Trading rules, derivative design and trading patterns; modeling;

information linkages among financial markets; hedge fund strategies



19

4-1. Option Pricing with Realized EGARCH Model: An Analytical Approximation Approach

Zhuo Huang1*, Tianyi Wang

2

1National School of Development, Peking University, Beijing, China

2School of Banking and Finance, University of International Business and Economics, Beijing, China

* Email: [email protected]

Abstract: Under the recently proposed Realized EGARCH modeling framework, this paper develops the

pricing formula for European options by using an analytical approximation. The approximation, based on

the Edgeworth expansion for the density of the cumulative return up to 6th order, is computationally fast

and empirically accurate. Compared with the existing discrete-time option pricing models which also

include realized volatility, our model is in the log-linear form, non-affine and generates flexible leverage

effect. We conduct an extensive empirical analysis on S&P500 index options and the results show that our

model outperforms several competing models in terms of both in-sample and out-of-sample pricing errors.

4-2. The Effect of Options on Liquidity and Asset Returns

Cheng Zhang

Department of Finance, FMG, and SRC, London School of Economics


Abstract: This paper examines how introducing an options market affects the liquidity and expected returns

of underlying assets when the economy features asymmetric information. Options provide hedging benefits

and increase the risk sharing between liquidity demanders and liquidity suppliers, although these two agent

types experience unequal welfare improvement. I show that introducing financial derivatives can have

opposite effects on underlying asset prices: doing so increases (resp., reduces) those prices when the market

has relatively more liquidity suppliers (resp., liquidity demanders). Thus the non-monotonic effects of

derivatives on underlying assets could reconcile the mixed empirical evidence on options listing effects.

Introducing derivatives reduces the price impact of liquidity demanders' trades on the underlying risky asset

but has no effect on its price reversal dynamics. The mechanism proposed here is robust to endogenous

participation and to derivatives with a general payoff structure. Finally, I provide new implications that can

be tested empirically.

4-3. A VECM/VAR-DCC/ADCC Framework and its application in Black-Litterman

portfolio optimization

Qi Deng1*, Xin Zhou², Sven Meyer

3

13Xi’an Jiaotong Liverpool University, Suzhou, China

²Volatility Institute of New York University Shanghai (VINS)


Abstract: The existing literature on Black-Litterman portfolio optimization model does not offer adequate

guidance on how to generate investors’ views in an objective manner. This paper establishes a generalized

VECM/VAR-DCC/ADCC framework that can be utilized to model multivariate financial time series in

general, and produce objective views to the Black-Litterman model in particular. To test the

VECM-DCC/ADCC preconditioned Black-Litterman model’s practical utility, it is applied to a five-asset

China portfolio. For the VECM/VAR-DCC/ADCC fit, the portfolio’s volatility is found persistent over

time, while insensitive to the most recent shocks and the signs of the individual asset returns, which

provides useful insight on portfolio performance forecast. For the Black-Litterman portfolio optimization,



20

with a properly chosen view confidence parameter, the VECM/VAR-DCC/ADCC preconditioned

Black-Litterman model offers clear advantage on portfolio optimization over normal mean-variance

optimized portfolios. Because the VECM/VAR-DCC/ADCC framework improves the objectiveness of the

inputs to the Black-Litterman model, its usage in the Black-Litterman model provides an automated

portfolio optimization alternative approach to the classic Black-Litterman method.

Session 5: Market microstructure, liquidity and volatility; modeling

5-1. Do Chinese bond ETFs improve the liquidity of the underlying bonds?

Yang Bai1, Xingguo Luo

2*

12Zhejiang University


Abstract: The launch of exchange-traded funds (ETFs) makes it convenient for small investors to make

diversified investments and provides more arbitrage opportunities. In this paper, we set the Boshi SSE

Corporate Bond 30 ETF as an example to study the liquidity changes of the underlying bonds with and

without the bond ETF. We construct a comprehensive liquidity indicator that reflect the liquidity of the

Chinese exchange traded bonds. We find that the introduction of the bond ETF can significantly improve

the liquidity of the underlying bonds in terms of spreads, trading frequencies and the turnover.

5-2. Implied Risk Aversion under Different Utility Functions: Influential Factors and

Implicit Information

Rong Chen1*, Yifeng Wang

2, Yichen Zhong

3

1The Department of Finance, Xiamen University.

2Shanghai Jiaotong University and ShenwanHongyuan Securities CO., LTD.

3Johns Hopkins University and ShenwanHongyuan Securities CO., LTD.

*Email: [email protected]

Abstract: We adopt power and exponential utility functions to extract implied risk aversion of Taiwanese

investors based on Maximum Likelihood Estimation (MLE) method, identify influential factors and implicit

information of implied risk aversion. Both utility functions demonstrate similar outcomes in our study.

Taiwanese investors are risk-averse under most circumstances, while they sometimes are willing to take

risks. Two significant influential factors are lag of implied risk aversion and sentiment. Wealth affects risk

aversion under the assumption of exponential utility function. Furthermore, implied risk aversion shows

strong relationship with ex-post higher moments risk premiums, thus, helps to predict future trends of

Taiwanese market.

5-3. An Option Pricing Model with Liquidity Adjustment of Underlying Asset

LIN Hui & YANG Yang*

School of Business, Nanjing University, China

*E-mail: [email protected]

Abstract: An important assumption of the Black and Scholes option pricing model in 1973 is that

underlying stock market liquidity has full liquidity. However, if the liquidity is not sufficient, the behavior

of the investors, replicating an option by self-financing portfolio, will cause the underlying stock price

changes and therefore result in the systematic bias between BS option price and real option price. According

to the no arbitrage equilibrium principle, this paper added liquidity premium created by option replication to


21

the geometric Brownian motion of underlying asset, then deduced the liquidity-adjusted generalized option

pricing differential equation, and we proved that the equation has a unique analytical solution. Since an

explicit analytical solution cannot derived from the liquidity-adjusted generalized option pricing differential

equation, the finite difference method is used to solve the numerical solution of the model, and we proved

that the numerical solution is stable. Through empirical research on Shanghai 50 ETF options market, we

find that the numerical solution is more close to the real option price than the BS model.

Session 6: Credit spreads; policy reform and institutional constraints in Chinese

markets

6-1. A New Take on the Relationship between Interest Rates and Credit Spreads

Brice Dupoyet¹, Xiaoquan Jiang2*

12Florida International University, Department of Finance, College of Business


Abstract: We revisit the link between interest rates and corporate bond credit spreads by applying

Rigobon’s (2003) heteroscedasticity identification methodology to their relation. This novel approach

allows us to account for endogeneity problems and to conclude that credit spreads respond negatively to

interest rates, a result consistent with the implications of Merton’s (1974) structural model. The negative

relation is robust to macroeconomic shocks, interest rates characteristics, different volatility regimes, bond

ratings, and call ability features. Finally, we show that business cycles could quite plausibly be responsible

for the negative relationship, significantly more so than the option feature embedded in callable corporate

bonds.

6-2.The Performance of China’s Stock Market Price Limits: Noise Mitigator or

Noise Maker?

Juan Tao1* & Jingyi Zhang

2

12Xi’an Jiaotong Liverpool University, Suzhou, China


Abstract: Event study methodologies were employed to examine the price limit effect on volatilities in

China’s stock market. Daily closing time price-limit-hit events were categorized into fundamental

volatility-driven hits and transitory volatility–driven hits, based on whether price continuations or price

reversals were observed during the subsequent observation periods: overnight, one trading day or two

trading days. Two sample periods were studied: the main sample period of 2009-2012 and the financial

crisis year 2008. The results with the three different observation windows and in the two sample periods are

consistent overall. The price limit effect on volatilities is asymmetric for upward and downward price

movements, and for small and large firm stocks. Specifically, the lower price limit has reduced transitory

volatilities for small firm stocks but not for large firm stocks. However, the upper price limit has failed to

mitigate stock volatilities of any source for any firm size.

6-3. Index Futures Trading and Spot Market Liquidity: Evidence from the Recent

Chinese Stock Market Crisis

Qian Han1*, Jufang Liang

2

1Xiamen University, China



22

2Wang Yanan Institute for Studies in Economics (WISE), MOE Key Lab of Econometrics and Fujian Key Lab of

Statistics, Xiamen University, China.


Abstract: Using a difference-in-difference approach, we find that restrictions placed on index futures

trading during the recent Chinese stock market crisis deteriorated spot market liquidity. This is more so for

the CSI 300 index component stocks and for the September trade restrictions announcement. Robustness

checks further confirm our main results. Our findings are consistent with the contention of Banerjee and

Grave line (2014) that during a market crash instituting bans or position limits on derivatives trading may

increase the underlying assets scarcity.

Session 7: Information linkages among markets; modeling; trading

7-1. Pricing of Contingent Convertible Bonds Based on Copulas and Bivariate

Simulation

Ping LI1, Fangfang LI

1, Jing-hua YIN

2

1School of Economics and Management, Beihang University, China

2Guangfa Securities, China


Abstract: CoCo bond (Contingent Convertible bond) is a new kind of bond issued after the subprime crisis

in 2008. CoCoCo bond (Contingent Conversion Convertible bond) is a kind of CoCo bond with Conversion

option. In this paper we use a Copula function to characterize the dependence structure between the stock

return and the Core Tier 1 Ratio (CTR), and simulate the stock price and the CTR simultaneously to price

the CoCo and CoCoCo bonds. In the model, the stock price is assumed to follow Heston’s stochastic

volatility model with jump sand the CTR follows the Vasicek model with jumps. Kolmogorov-Smirnov test

is employed to choose the best fitting Copula to depict the dependence between the stock price and the CTR.

Then we use the CECS issued by the Bank of Cyprus to test the performance of our model. After that we

design the CoCo and CoCoCo bonds for China Bank of Communication (CBC) according to the terms of

existing international CoCo and CoCoCo bonds Combined with the requirements of China Banking

Regulatory Commission (CBRC) for write-down debts, and then give the price for them. Empirical pricing

results show that our model considering the dependence between the stock price and the CTR performs

better than the existing pricing models assuming independence.

7-2. Multi-Market Trading and Cross-Asset Integration

Patrick Augustin¹, Feng Jiao¹, Sergei Sarkissian¹ and Michael J. Schill²

¹McGill University, Canada;

²University of Virginia, USA.


Abstract: We study how trading in multiple markets affects the integration of a firm’s capital structure.

Using daily data on cross-listed securities and credit default swaps (CDS) traded around the world, we find

that foreign listing improves the synchronicity between firm stock and CDS returns. This effect is robust to

the inclusion of market and firm-level controls, and it manifests itself most profoundly among larger, more

liquid, better credit quality firms, as well as among firms with higher analyst coverage. Integration tests

reveal that, after foreign listing, firm-specific credit risk becomes more exposed to both world and local

equity market risks, with a larger change in the world market beta. Our results suggest that cross-listings



23

have an important impact on debt and equity market integration, and that this integration is more easily

attained for securities of more visible firms.

7-3. Margin Policy in Futures Trading: The American System vs. The Chinese

System

Haiwei Chen1, Canlin Li

2*, Nicholas Valerio III

3

1School of Management ,University of Alaska Fairbanks*

2Monetary and Financial Market Analysis Section, Federal Reserve Board

3Goizueta Business School, Emory University


Abstract: It is shown that the price-dependent Chinese margin system differs from the fixed-amount

American margin system by altering the risk of underlying positions, resulting in a more elastic demand by

long-term buyers but a more inelastic demand by long-term sellers, whereas demand by day traders is not

affected. The simulation results show a reduction in price volatility when trading is switched to the Chinese

floating system. It is found that futures price volatility in China is lower in general and on market-down

days than that in U.S., both of which are consistent with the predictions of the model.

7-4. Asymmetry in the Permanent Price Impact of Block Purchases and Sales: Theory and

Empirical Evidence

Alex Frino*¹, Mollica Vito¹, Maria Grazia Romano² and Zeyang Zhou1

¹ Macquarie Graduate School of Management, Australia

²University’ di Salerno, Italia


Abstract: Previous research has demonstrated information effects of buyer initiated trades are greater

vis-à-vis seller initiated trades. We develop a theoretical model that predicts buy trades are relatively more

informed in bear markets and sell trades are more informed in bull markets. Using a sample of block trades

executed on S&P 500 index futures and SPDR EFTs in selected bull and bear markets, we find evidence

consistent with our theoretical model. The theory also holds for bullish and bearish information days.

Session 8: Information linkages; Credit spreads; modeling of derivatives

8-1. The Relationship among China's Fuel Oil Spot Futures and Stock Markets Li Ping*, Zhang Ziyi, Zeng Qingchao

School of Economics and Management, Beihang University, China


Abstract: This paper examines the relationship among China's stock, fuel oil spot and fuel oil futures

markets over the period from August 26, 2004 to January 21, 2016. For comparison, the international

markets (the United States and Britain) are considered. Modeling dynamic volatility with the dynamic

conditional correlation (DCC) GARCH model, we show that the conditional correlations between China's

fuel oil futures and stock evolve over time and are highly volatile, particularly since the 2008 financial crisis.

The conditional correlations between China's fuel oil spot and fuel oil futures, however, are slightly volatile

around a relatively low level. The result indicates that China's fuel oil futures indeed takes the role of price

discovery, while the spot market's trading volume is not great enough. In addition, using the

VAR-BEKK-GARCH model and the theory of hedging, we investigate the volatility spillover effect, the


24

optimal hedge ratios and the hedging effectiveness among the three China's financial markets.

8-2. Variance and Skew Risk Premiums for the Volatility Market: The VIX Evidence Jose Da Fonseca1*, Yahua Xu

2

12Auckland University of Technology, Business School, Department of Finance, New Zealand.


Abstract: We extract variance and skew risk premiums from volatility derivatives in a model-free way and

analyze their relationships along with volatility index and equity index returns. These risk premiums can be

synthesized through option trading strategies. Using a time series of option prices on the VIX, the most

liquid volatility derivative market, we find that variance swap excess return can be partially explained by

volatility index and equity index excess returns while these latter variables carry little information for the

skew swap excess return. The results sharply contrast with those obtained for the equity index option

market underlining very specific characteristics of the volatility derivative market.

8-3. Does the Introduction of One Derivative Affect Another Derivative?

The Effect of Credit Default Swaps Trading on Equity Options

Jie Cao¹*, Yong Jin², Neil D. Pearson³ and Dragon Yongjun Tang4

¹The Chinese University of Hong Kong 2University of Florida

³University of Illinois at Urbana Champaign 4The University of Hong Kong.


Abstract: There are numerous derivatives, often on the same underlying firms, in the financial markets.

However, little is known about the interactions between different types of derivatives. In this paper, we

show that the inception of credit derivatives, represented by credit default swaps (CDS), makes the equity

options on the same underlying firm more expensive. The expensiveness of option due to CDS trading is

not completely driven by the increased riskiness of the firm because the delta-hedged equity option returns,

which account for underlying stock price movement, are lower. The effect of CDS trading is more

pronounced for call options than put options. The evidence is consistent with the view that CDS trading

crowds out option trading, option dealers charge higher option premiums due to limited intermediation

capacity.

8-4. Within-sector credit risk spillover in Asia

Yiling Zha¹*, David Power¹ and Nongnuch Tantisantiwong²

¹School of Social Sciences, University of Dundee, UK

²Southampton Business School, University of Southampton, UK


Abstract: This paper investigates credit risk spillover effects among 126 Asian named credit default swap

(CDS) entities in China, Japan, Malaysia, Singapore and South Korea after the 2008 global financial crisis

by employing a bivariate GARCH- BEKK model. Reference entities are grouped into the sovereign ,

financial and non-financial categories to examine both domestic and regional credit risk transmissions at the

firm level for 1-year and 5-year CDSs. Evidence indicates that transmission of credit risk varies with


25

countries and sectors. Domestic spillovers and co-movements between CDS spreads in the non-financial

sector are greater than in the financial sector for China and Malaysia while the opposite result is uncovered

for Singapore and South Korea. Regarding regional spillovers, the financial sector in South Korea plays an

important role in the spill-over of credit risk while the sovereign and non-financial sectors in Japan

dominate credit risk transmission for those sector. By contrast, the Malaysian non-financial sector is

relatively sensitive to external shocks. For both short- term and long-term CDSs, the transmissions of

shocks and volatility are more pronounced than impacts from past CDS spread changes.

Session 9: Modeling of derivatives; risk management

9-1. Volatility forecasting in the Chinese commodity futures market with intraday data

Ying Jiang, Shamim Ahmed, Xiaoquan Liu*

Nottingham University Business School, China


Abstract: Given the unique institutional regulations in the Chinese commodity futures market as well as the

characteristics of the data it generates, we utilize contracts with three months to delivery, the most liquid

contract series, to systematically explore volatility forecasting for Aluminum, Copper, Fuel Oil, and Sugar

at the daily and three intraday sampling frequencies. We adopt popular volatility models in the literature and

assess the forecasts obtained via these models against alternative proxies for the true volatility. Our results

suggest that the long memory property is an essential feature in the commodity futures volatility dynamics

and that the ARFIMA model consistently produces the best forecasts or forecasts not inferior to the best in

statistical terms.

9-2. Timing and hedging portfolio strategies with derivatives

S. Ortobelli1, F. Petronio

2, T. Tichy

3*

1 University of Bergamo, via dei Caniana, 2, Bergamo, Italy;

23VŠB-Technical University of Ostrava, Ostrava, Czech Republic


Abstract: In this paper, we propose portfolio selection strategies with options. We generally assume that

the returns follow Markov processes that are approximated with proper Markov Chains. Then we

preliminary examine all the American options with underlying the components of the Dow Jones Industrial

index and we discuss different portfolio strategies based either on hedging the risk of the underlying or on

optimizing proper expected first passage times of the wealth at some benchmark levels. We focus on an

empirical comparison among different hedging and timing portfolio selection strategies. Specifically, we

compare the ex post wealth obtained by optimizing new performance strategies based either on hedging of

the portfolio risk properly or optimizing the average first passage time of the wealth at some benchmark

levels.

Session 10: Hedging and risk management; hedge fund strategies

10-1. Three Novel Factors Affecting Hedge Fund Returns

L. MICK SWARTZ

University of Southern California, Marshall School of Business, USA


Abstract: This paper has three major contributions to the literature. First, it analyzes the risk characteristics


26

for 55 hedge fund categories monthly returns which have never appeared in the previous hedge fund

literature. Second, this paper introduces three new factors, the D-Ratio, L-Ratio and the R-Ratio; that when

combined with the CAPM or Fama-French models are better risk measurements for hedge fund returns than

conventional variables. These strategy-specific models are more parsimonious and efficient than the Fung

and Hsieh 7-factor model in 50 out of 55 of the categories. Third, contrary to previous studies, many

categories do not mimic put-writing strategies.

10-2. Do Trend Following Strategies Work in China Futures Markets?

Di Zhang1, Bin Li

2* 12

Department of Finance, Economics and Management School, Wuhan University, P.R. China


Abstract: We examine the performance of trend following strategies in China futures markets. We find

evidences that some trend following based technical trading rules yield better performance than the buy and

hold strategy on both individual contracts and sorted portfolios between 2003 and 2015. Our findings are

insensitive to the data frequency, financial crisis, short sale constraint and spread adjustment methods.

Finally, our bootstrapping analysis shows that the performance depends on the underlying autocorrelation

structure of the contracts returns.

International Business School Suzhou (IBSS)

Tel：+86 (0)512 8188 0455 Mobile：+86-18930831320

Business Building (BA 104/216) , Xi’an Jiaotong - Liverpool University

111 Ren'ai Road Suzhou, China

http://www.xjtlu.edu.cn/en/departments/academic-departments/international-business-school-suzhou/


27

English annotated Chinese maps of Suzhou region, IBSS and

XJTLU Conference Hotel/Centre

Map of Suzhou and Shanghai region

Map of Suzhu region


28

Map of IBSS

The hotel is located just south of Ren’ai Rd in the right hand map, and the BA/BB buildings are south of

the hotel.

Conference dates: May 19th

2016 (Thursday) - May 20th

2016 (Friday)

Conference address:

International Business School Suzhou (IBSS)

Business Building (BA 104/216)

Xi’an Jiaotong - Liverpool University

111 Ren'ai Road

Suzhou, China

Hotel name and address:

XJTLU Conference Center

Ren'ai Road 99

Suzhou, China


29

CDMC Travel Directions

Suzhou is a major city located in the southeastern part of Jiangsu Province in China (100km/62mi) northwest of

Shanghai. Despite being an important tourist destination, Suzhou does not have its own airport at present. It is

best to use Shanghai Hongqiao Airport (SHA) and Shanghai Pudong Airport (PVG) to get to Suzhou. SHA is

86km/53mi away from Suzhou and PVG is located 120km/65mi from Suzhou.

This DIY travel guide will ensure that getting to Suzhou is a breeze and you will be in Suzhou in no time.

Our campus is located in Suzhou Industrial Park (SIP), to the east of the Suzhou’s city center. Here is the

complete address in both English and Chinese:

Xi'an Jiaotong - Liverpool Conference Centre

Xi’an Jiaotong - Liverpool University

99 Ren'ai Road

Suzhou Dushu Lake Science and Education Innovation District

Suzhou Industrial Park

Suzhou, 215123

P. R. China

中华人民共和国江苏省苏州市

工业园区独墅湖高教区仁爱路 99 号

西交利物浦国际会议中心西交利物浦大学

苏州 215123

Getting to Suzhou from Shanghai Pu Dong Airport (PVG)

1. By Airport Shuttle – There are regular shuttle buses that operate between Suzhou and PVG airport.

They are called “Long Distance Buses”. It usually takes 2.5 – 3 hours to get to Suzhou Yuan Qu station

(Suzhou SIP). The price is around 84 RMB.

The parking area of the shuttle bus is situated on the second floor of the airport building. The boarding

area is at the exit of the bus parking area. Please follow directions to the “Long Distance Buses”, after

checking out of the Airport.

Operating Time: 10:00 10:40 11:10 11:40 12:10 12:40 13:10 13:50 14:20 14:50 15:20 16:10 16:50 17:20

18:10

The shuttle bus follows the following route – PVG airport > SHA Airport > Suzhou Yuan Qu > Suzhou

Downtown station. Please de-board at Suzhou Yuan Qu Station [苏州园区, the first stop in Suzhou].

Taxis are easily available in Suzhou. The taxi fare should cost you 25 – 30 RMB to get from the bus stop

to the university campus.


30

Tip – Before you board the bus, the bus driver/helper shall help you load your luggage in the right

section of the bus [which depends upon the stop where you want to de-board i.e. Suzhou Yuan Qu or

Suzhou Downtown]. Please tell him that you would like to go Suzhou Yuan Qu [苏州园区站].

2. By High Speed Train – Regular High Speed trains are available from Shanghai station to Suzhou Yuan

Qu Station [苏州园区站] or Suzhou Central Station [苏州站 or simply 苏州]. It takes around 30

minutes to get to these stations from Shanghai. The cost is usually around 35 RMB.

If you are flying into Shanghai Pudong airport (PVG), you will need to travel to Shanghai Station [上海

站] or to the Shanghai Hongqiao Station [上海虹桥站] to catch a train to Suzhou.

Here is how you can get to the railway station –

Maglev – The High Speed Maglev operated between 7:02 TO 21:42. The fare for single trip is 50RMB

(economy) and 100RMB (VIP). Maglev operates between Pudong Airport [PVG] and Long Yang

Road Station [龙阳路]. Long Yang Road Station is connected to Line 2, Line 7 and Line 16 on the

Shanghai Metro.

Line 2 is colored Light Green on the system maps. Take Line 2 from Long Yang Road towards East

Xujing [徐泾东]. Change Trains at People’s Square (7 stops from Long Yang Road Station). Take Line 1

[Colored red] towards Fujin Road [富锦路]. Get off at Shanghai Railway Station [3 stops].

At Shanghai Railway Station, proceed to the exit towards Shanghai Railway Station South Square (上海

站南广场). Please Note that – If you do not hold a Chinese Citizen Card, You won’t be able to use the

automatic ticket machines. You will need to visit the ticket counter at the ticket office. The Ticket office

is situated 150 meters from the South Square. You can buy tickets to Suzhou Yuan Qu (苏州园区) or to

Suzhou Central Station (苏州站).

The Suzhou Yuan Qu station is closer to the university, but the frequency of trains between Shanghai

Station and Suzhou Yuan Qu is lower than that between Shanghai Station and Suzhou Central Station.

You can easily get a taxi from these stations to our campus. The taxi fare from Suzhou Yuan Qu station

is around (30 RMB) and from Suzhou Central Station is (50-70 RMB). You can show the following

text to the Taxi driver to make getting here simpler.

(我去苏州市，西交利物浦大学国际会议中心仁爱路 99 号)

You can also catch trains to Suzhou from Shanghai Hongqiao Train Station. Shanghai Hongqiao Train

Station is located on Line 2 of Shanghai Metro. You can take Line 2 from Long Yang Road station (after

de-boarding Maglev)

Also please note that, you could take Line 2 of Shanghai Metro directly at the Shanghai Pudong Airport

(PVG), instead of taking the Maglev then changing to Line 2. Please if you take this option, you


31

should be aware that you will need to de-board the metro train at Guanglan Road [广兰路], and change

to another Line 2 metro train towards East Xujing [徐泾东] on the same platform. The rest of the

directions are identical to the above.

3. By Taxi – Direct taxis are usually available from Shanghai Pudong Airport. It should take roughly 2

hours to get to University campus directly from the Pudong Airport. A direct taxi from Shanghai Pudong

to University campus should cost you between 400 – 600 RMB.

Getting to Suzhou from Shanghai Hong Qiao Airport (SHA)

1. By Airport Shuttle – There are regular shuttle buses that operate between Suzhou and SHA airport.

They are called “Long Distance Buses”. It usually takes 1.5-2 hours to get to Suzhou Yuan Qu station

(Suzhou SIP). The price is around 53 RMB. The boarding location is East of the Parking Area opposite

the exit of the airport building. Follow the directions to the “Long Distance Buses”.

Operating Time: 10:00 10:40 11:10 11:40 12:10 12:40 13:10 13:50 14:20 14:50 15:20 16:10 16:50 17:20

18:10

The shuttle bus takes the following route – PVG airport > SHA Airport > Suzhou Yuan Qu > Suzhou

Downtown station. Please de-board at Suzhou Yuan Qu Station. [ 苏州园区, the first stop in Suzhou]

Taxis are easily available in Suzhou. The taxi fare should cost you 25 – 30 RMB to get from Bus stop to

the university campus.

Tip – Before you board the bus, the bus driver/helper shall help you load your luggage in the right

section of the bus [which depend upon the stop where you want to de-board i.e. Suzhou Yuan Qu or

Suzhou Downtown]. Please tell him that you would like to go Suzhou Yuan Qu [苏州园区站].

2. By High Speed Train – The Hongqiao Airport is connected to the Hongqiao Railway station. Please

follow the directions to the “Hong Qiao Railway Station” at the airport exit.

You can buy tickets to Suzhou Yuan Qu (苏州园区) and to Suzhou Central Station (苏州站).

The Suzhou Yuan Qu station is closer to the university, but the frequency of trains between Shanghai

Hongqiao Station and Suzhou Yuan Qu is lower than that between Shanghai Hongqiao Station and

Suzhou Central Station.

You can easily get a taxi from these stations to our campus. The taxi fare from Suzhou Yuan Qu station

is around (30 RMB) and from Suzhou Central Station is (50-70 RMB). You can show the following

text to the Taxi driver to make getting here simpler.

(我去苏州市，西交利物浦大学国际会议中心仁爱路 99 号)

Please Note that – If you do not hold a Chinese Citizen Card, You won’t be able to use the automatic

ticket machines. You will need to visit the ticket counter at the ticket office


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3. By Taxi – Direct taxis are usually available from Shanghai Hongqiao Airport. It should take roughly 2

hours to get to University campus directly from the Hongqiao Airport. A direct taxi from Shanghai

Hongqiao Airport to University campus should cost you between 350-450 RMB.

Below is a map of the Metro Lines that you can take to get to Shanghai Station: The Longyang Road [龙

阳路]station is in the bottom right corner, and Shanghai Railway station is on the Upper Left Corner.

You can find a complete interactive map on http://www.exploreshanghai.com/metro/#

Explore Shanghai also offers a free Shanghai Metro Map, which can be very useful while making

transitions at Metro Stations.

If you need more assistance, you can visit the university page here -

http://www.xjtlu.edu.cn/en/about/visit-us, or you can call Sven Meyer at + 86- 18930831320.

We look forward to meeting you in Suzhou.

CDMC Organizing Committee

http://www.exploreshanghai.com/metro/

http://www.xjtlu.edu.cn/en/about/visit-us

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