Canada Research Chair in Risk Management
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High price impact trades identification and its implication for volatility and price efficiency
We include Limit Order Book (LOB) matchedness as a new trade attribute to
identify High Price Impact Trades (HPITs). HPITs are trades associated with large price changes relative to their volume proportion. We show that the inclusion of matchedness provides a finer analysis of the relationship between price contribution and trade categories. We further verify that a stronger presence of HPITs leads to a decline in volatility and improves price efficiency, which suggests a link between HPITs and informed trades.
Average Intraday Dynamics of DAX Spread
Data source: Dionne and Zhou, Risk Sciences, 2024.
We investigate the causes of the gap in mergers and acquisitions (M&A) between life and nonlife insurers in the US from 1990 to 2022. Our DID analysis indicates a parallel trend between M&As in the life insurance and nonlife insurance sectors from 1990 to 2012, and a significant difference after 2012. There was a shock in the life insurance market that resulted in a reduction in M&As after 2012. Variable annuity sales in the life insurance sector declined after 2012. We find evidence that low interest rates observed during the implementation of the quantitative easing policy of the Fed from 2008 to 2012 caused the difference in M&As in the life sector after 2012.
Fed’s interest rate and 10-year T-bond interest rate
Data source: World Bank database.
Developments in risk and insurance economics: The past 50 years
The chapter reviews the evolution in risk and insurance economics over the past 50 years, first recalling the situation in 1973, then presenting the developments and new approaches that have flourished since then. We argue that these developments were only possible because steady advances were made in the economics of risk and uncertainty and in financial theory. Insurance economics has grown in importance to become a central theme in modern economics, providing not only practical examples and original data to illustrate new theories, but also inspiring new ideas that are relevant to the overall economy.
Climate risk is becoming a real challenge for the insurance industry.
Adverse selection in insurance
In this survey we present some of the more signi cant results in the literature on adverse selection in insurance markets. Sections 1 and 2 introduce the subject, and Section 3 discusses the monopoly model developed by Stiglitz (1977) for the case of single-period contracts, which has been extended by many authors to the multi-period case. The introduction of multi-period contracts raises issues that are discussed in detail; time horizon, discounting, commitment of the parties, contract renegotiation, and accident underreporting. Section 4 covers the literature on competitive contracts, where the analysis is more complicated because insurance companies must take competitive pressures into account when they set incentive contracts. As pointed out by Rothschild and Stiglitz (1976), there is not necessarily a Nash equilibrium when there is adverse selection. However, market equilibrium can be sustained when principals anticipate competitive reactions to their behavior. Multi-period contracting is discussed. We show that different predictions on the evolution of insurer profits over time can be obtained from different assumptions concerning the sharing of information between insurers about an individuals choice of contracts and accident experience. The roles of commitment and renegotiation between the parties to the contract are important. Section 5 introduces models that consider moral hazard and adverse selection simultaneously, and Section 6 covers adverse selection when people can choose their risk status. Section 7 discusses many extensions to the basic models such as risk categorization, multidimensional adverse selection, symmetric imperfect information, double-sided adverse selection, participating contracts, and nonexclusive contracting.
A pooling solution cannot be a Nash equilibrium.
Causality in empirical analyses with emphasis on asymmetric information and risk management
We discuss the difficult question of measuring causality effects in empirical analyses, with applications to asymmetric information and risk management. It is now well documented in the economic literature that policy analysis must be causal. Hence, the measurement of its effects must also be causal. After having presented the main frameworks for causality analysis, including instrumental variable, difference-in-differences, and generalized method of moments, we analyze the following questions: Does risk management affect firm value and risk? Do we face a moral hazard problem in the insurance data? How can we separate moral hazard from adverse selection and asymmetric learning? Is liquidity creation a causal factor for reinsurance demand? We show that residual information problems are often present in different markets, while risk management may increase firm value when appropriate methodologies are applied.
We observe a significant decrease in accidents three quarters after the policy change.
The profitability of lead–lag arbitrage at high frequency
Any lead-lag effect in an asset pair implies the future returns on the lagging asset have the potential to be predicted from past and present prices of the leader, thus creating statistical arbitrage opportunities. We utilize robust lead-lag indicators to uncover the origin of price discovery and we propose an econometric model exploiting that effect with level 1 data of limit order books (LOB). We also develop a high-frequency trading strategy based on the model predictions to capture arbitrage opportunities. The framework is then evaluated on six months of DAX 30 cross-listed stocks’ LOB data obtained from three European exchanges in 2013: Xetra, Chi-X, and BATS. We show that a high-frequency trader can profit from lead-lag relationships because of predictability, even when trading costs, latency, and execution-related risks are considered.