Has Liquidity in Canadian Government Bond Markets Deteriorated?

Introduction

Liquid financial markets promote the efficient allocation of resources in the economy and support economic growth. Nevertheless, market liquidity can suddenly evaporate, with adverse effects on financial stability and real activity. In the aftermath of the financial crisis, authorities introduced regulations to strengthen the resilience of the financial system and reduce the risk of a future financial crisis. These changes have raised concerns that market liquidity may worsen as dealers and market-makers adapt.

This note presents measures of liquidity used by the Bank of Canada to monitor market conditions and discusses recent trends in Government of Canada (GoC) fixed‐income market liquidity.1 Our results indicate that the Bank’s measures have improved since the financial crisis. Furthermore, GoC market liquidity deteriorated following several stressful events: the euro crisis in 2011, the taper tantrum in 2013 and the oil price shock in 2015. In all three cases, the deterioration remained within historical norms and liquidity returned to normal levels afterwards.

What Is Liquidity?

An asset is considered to be liquid if it can be traded at low cost in large quantities in a short period of time without having a significant impact on its price. The seminal papers by Black (1971) and Kyle (1985) together identify four main dimensions of liquidity:

• Depth is the ability to trade an asset in large volumes without affecting its price.
• Tightness refers to the cost of turning around a position over a short period of time. Tightness is sometimes referred to as “width.”
• Resilience is the speed of price recovery from a temporary imbalance caused by uninformed traders or by a large trade. An asset or a market is resilient if temporary price changes due to order imbalances quickly attract new orders that restore fair values.2
• Immediacy is the speed with which a trade of a given size and cost can be arranged.

Data

We focus on GoC bonds with 2, 5, 10, and 30 years until maturity. We use intraday data from two different sources: CanDeal and the Canadian Depository for Securities (CDS). CanDeal is an online exchange that provides institutional investors with access to liquidity supplied by broker‐dealers of Canadian fixedincome securities. The CanDeal data cover the period from January 2007 to December 2016 and capture intraday quotes of bid and ask prices as well as transaction information (such as price, quantity and trade direction) for trades conducted between institutional investors and dealers.

CDS provides depository, clearing and information services for market participants; nearly all Canadian fixed‐income trades are settled through its systems. The transactions data from CDS include trade price and traded quantity over the period from October 2009 to January 2017.

Each database has its distinct advantage. Together, both databases are good complements to monitor market liquidity conditions. The CDS data cover client‐to‐dealer and dealer‐to‐dealer transactions, while CanDeal covers only a subset of transactions conducted between clients and dealers. Moreover, CanDeal data are heavily weighted toward benchmark bonds, capturing 30 to 40 per cent of the volume of benchmark bond trading, but only 1 to 2 per cent of the volume of non‐benchmark bond trading. However, CanDeal provides more information, including bid and ask prices and trade direction.

Liquidity Measures

While relatively easy to define, liquidity is not easy to measure. Indeed, the academic literature has proposed a wide variety of proxies to measure liquidity. Using intraday data, we compute measures of bid‐ask spreads to capture tightness, of price impact to capture depth, and of trading volume and trade size to capture market activity (see the appendix for the computational details of the liquidity measures).

We employ two measures for the bid‐ask spread of GoC bonds. First, the quoted bid‐ask spread for a security is calculated as the ratio of the spread between the bid and ask prices to the midpoint between bid and ask prices. This measure is obtained using the CanDeal quotes data and reflects the transaction cost for a typical small‐size trade. Under normal conditions, when market‐makers can easily execute a trade, the bid‐ask spread remains tight, indicating a high level of market liquidity.

Second, we compute Roll’s (1984) effective spread proxy using transactions data from CDS. The Roll measure reflects the average transaction cost across observed trades of all sizes. For each measure, the intraday spread is aggregated to a daily frequency by calculating the average every day. The quoted bidask spread, which uses CanDeal data, is available for a longer sample period, including the pre‐crisis period, and it better captures the cost of trades for benchmark bonds. Roll’s effective spread proxy, which uses CDS data, is available for a shorter sample period but has more comprehensive coverage of both benchmark and non‐benchmark securities.

We also use two measures of price impact to capture market depth. Price impact is especially relevant to those executing large trades or a series of trades over a short period of time. First, using CanDeal transactions data, we compute Kyle’s (1985) lambda, which employs regression analysis to measure how much a security’s price falls (rises) in response to a seller‐initiated (buyer‐initiated) trade. Second, we compute Amihud’s (2002) illiquidity ratio as a price‐impact proxy using CDS transaction data. Intuitively, the illiquidity ratio is the average ratio of the price change to the trading volume over a day.

Has GoC Bond Market Liquidity Deteriorated?

The first two charts present the liquidity metrics from CanDeal data. Chart 1 shows daily average quoted bid‐ask spreads for benchmark and non‐benchmark bonds.3 As expected, we find that the average bid‐ask spread widened markedly during the financial crisis, especially for non‐benchmark bonds. At the peak of the crisis, spreads reached 6.9 and 5.3 basis points (bps) for non‐benchmark and benchmark bonds, respectively. After the crisis, bid‐ask spreads returned to their normal levels, averaging between 4 and 5 bps.

Chart 2 shows the average price impact (Kyle’s lambda) of trades of $1 million. Similar to bid‐ask spreads, the price impact rose sharply during the financial crisis but has since returned to historical levels.

Chart 3 reports the price‐impact (Amihud’s measure) and bid‐ask (Roll’s measure) proxies based on CDS data. These proxies are more volatile.4 The price‐impact and bid‐ask proxies rose markedly during the euro crisis in 2011, the taper tantrum in 2013 and the oil price shock in 2015, but they exhibited gradual improvements following each event. Overall, the illiquidity proxies add to the evidence in Chart 1 and Chart 2 that the level of liquidity has not deteriorated, on average, for benchmark and non‐benchmark bonds. In unreported results, we reach the same conclusion when looking separately at bonds with different maturities.

Chart 4 shows average trading volume and trade size from CDS data. During our sample period, October 2009–January 2017, the average trade size for benchmark bonds increased from $15 million to $18 million, but it was mostly stable at around $11 to $12 million for non‐benchmark bonds. The increase in the average trade size suggests that, on average, investors are not dividing large trades into smaller ones to control the price impact. Time‐series patterns of trading volumes for benchmark and non-benchmark bonds are similar to those for trade sizes. This is consistent with surveys of market participants suggesting that trading activity is more concentrated in benchmark bonds.5

Conclusion

Overall, the measures of tightness and market depth suggest that aggregate GoC market liquidity and trading activity have improved since the financial crisis and have been stable since then. We find some differences in trading activity between non‐benchmark and benchmark bonds: average trade size and volume have been increasing since 2010 for benchmark bonds, but have remained stable for non-benchmark bonds. This is consistent with the recent survey conducted by the Canadian Fixed‐Income Forum, where market participants noted that they increased their allocations to benchmark or more-liquid bonds.

Other dimensions of liquidity require further analysis. For example, market participants reported that more time is needed to execute a large trade. This time dimension of liquidity is not well captured by the measures employed in this note. Market participants may be more concerned about liquidity risk (i.e., the risk that liquidity will be lower than expected in the future). Indeed, our liquidity proxies indicate adverse liquidity movements around stress events such as the euro crisis in 2011, the taper tantrum in 2013 and the oil price shock in 2015. We leave these important questions to future work.

Acknowledgements

We would like to thank Guillaume Bédard-Pagé, Jean-Sébastien Fontaine and Ian Christensen for helpful comments and suggestions. We would also like to thank Nadeem Merali for research assistance and Maura Brown for editorial assistance.

Appendix

Quoted spread

The quoted bid-ask spread for bond i at time t is defined as $$ QS_{it}= (P_{it}^A-P_{it}^B)/M_{it} ,$$where \(P_{it}^A\) and \(P_{it}^B\) are the bid and ask prices, respectively. \(M_{it}=(P_{it}^A+ P_{it}^B)/2\) is the quote midpoint. The intraday spread for each security is then aggregated to a daily frequency by calculating the average over each day.

Roll’s effective spread

Roll’s (1984) measure is computed as twice the square root of the negative covariance between adjacent price changes (which tend to be negatively correlated) $$Roll_{it}=2\sqrt{-Cov(r_{i,t},r_{i,t-1})},\,\,if\,Cov(r_{i,t},r_{i,t-1})<0,$$ where \(r_{i,t}\) is the return of bond \(i\) at time \(t\) computed from consecutive trades.

Kyle’s lambda

Kyle’s (1985) lambda \((λ_i)\) is estimated as the slope coefficient of the regression $$r_{it}= α_i+ λ_i I_{it} q_{it}+ ε_{it},$$ where \(r_{it}\) is the return of bond \(i\) at time \(t\) computed from consecutive trades; \(I_{it}\) is a sign indicator, which is positive for a buy order and negative for a sell order; and \(q_{it}\) is the trading volume. This regression is estimated each day using intraday data for individual bonds with at least six transactions on a given day.

Amihud’s illiquidity ratio

This is the ratio of the absolute value of the asset return to the dollar value of trading volume: $$Amihud_{id}=Average\left(\frac{|r_{it}|}{DVOL_{it}}\right),$$ where \(r_{it}\) and \(DVOL_{it}\) are the return and dollar value of trading volume for bond \(i\) at time \(t\), respectively. The transaction-level illiquidity ratio is averaged over a day d to find the daily illiquidity ratio. The higher the illiquidity ratio of a bond, the less liquid the asset is.

Endnotes

References

1. Amihud, Y. 2002. “Illiquidity and Stock Returns: Cross‐Section and Time‐Series Effects.” Journal of Financial Markets 5 (1): 31–56.
2. Black, F. 1971. "Toward a Fully Automated Exchange, Part I.” Financial Analysts Journal 27 (4): 28–35.
3. Gungor, S. and J. Yang. 2017. “Has Liquidity in Canadian Provincial and Corporate Bond Markets Bifurcated?” Bank of Canada Staff Analytical Note, forthcoming.
4. Kyle, A. S. 1985. “Continuous Auctions and Insider Trading.” Econometrica 53 (6): 1315–35.
5. Roll, R. 1984. “A Simple Implicit Measure of the Effective Bid‐Ask Spread in an Efficient Market.” Journal of Finance 39 (4): 1127–39.