Archive for the ‘Interesting External Papers’ Category

Liquidity and the US Treasury Market

Thursday, October 26th, 2023

I often stress the importance of liquidity – and the liquidity premium! – in financial markets and every now and then somebody scoffs that the concept of liquidity is completely bogus.

So I’m bookmarking this paper by Darrell Duffie, titled Dealer capacity and US Treasury market functionality, for future reference:

Summary
Focus
We investigate the dynamics of liquidity in the US Treasury market. In particular, we focus on the relationship between yield volatility and Treasury market illiquidity and highlight how limited dealer intermediation capacity worsens market illiquidity beyond yield volatility, but only at high levels of dealer balance sheet utilisation, as in March 2020.

Contribution
The status of US Treasury securities as the world’s premier safe haven rests in part on the depth and liquidity of the market in which they are traded. Our results shed new light on the dependence of market liquidity on asset volatility and dealer intermediation capacity, and adds focus to ongoing policy efforts to improve the resilience of the US Treasury market, an anchor of global capital markets.

Findings
This study combines highly relevant data on dealer-level balance sheet positions and comprehensive transaction-level Treasury security trades, among other data sets, to show that there is a significant loss in US Treasury market functionality when intensive use of dealer balance sheets is needed to intermediate bond markets, as in March 2020. While yield volatility explains most of the variation in Treasury market liquidity over time, when dealer balance sheet utilisation reaches sufficiently high levels, liquidity is much worse than predicted by yield volatility alone. This is consistent with the existence of occasionally binding constraints on the intermediation capacity of bond markets.

Abstract
We show a significant loss in US Treasury market functionality when intensive use of dealer balance sheets is needed to intermediate bond markets, as in March 2020. Although yield volatility explains most of the variation in Treasury market liquidity over time, when dealer balance sheet utilization reaches sufficiently high levels, liquidity is much worse than predicted by yield volatility alone. This is consistent with the existence of occasionally binding constraints on the intermediation capacity of bond markets.

 
 

Hedge Funds and GOC Liquidity

Tuesday, August 1st, 2023

A recurring problem I have is explaining to retail that liquidity is a Thing, and further that it’s a Thing that affects all markets, including government bonds and explains much of the spread between corporate and government bonds.

I am told quite often that liquidity is not a Thing in government bond markets because my interlocuter has never had any problems getting his $25,000 orders filled.

So I was pleased to see Staff Analytical Note 2023-11 published by the Bank of Canada, written by Jabir Sandhu and Rishi Vala, titled Do hedge funds support liquidity in the Government of Canada bond market? – the best part was:

Two-sided markets can help dealers more easily fulfill the transactions of their different clients, potentially supporting market liquidity. To assess whether the transactions of hedge funds promote two-sided markets, we estimate the extent to which hedge funds trade GoC bonds in the opposite direction to other clients. Our measure of opposite direction transactions is the ratio of hedge funds’ net daily transaction volume for each GoC bond relative to that of other clients. We calculate the average of this ratio across bonds on each day. We exclude the period between March 9 and 20, 2020, to get a better idea of the typical behaviour of hedge funds. Our measure does not consider whether hedge funds initiate a transaction. It is plausible that hedge funds demand liquidity while they transact in the opposite direction of other clients. Nevertheless, our measure is useful for assessing hedge funds’ contributions to two-sided markets.

Chart 2 shows the median of the opposite direction ratio over our sample period for hedge funds and for other types of clients. Hedge funds have a median ratio of around -14%, which means that hedge funds typically trade 14% of the volume of GoC bonds transacted by other clients, but in the opposite direction to other clients. Another interpretation is that, all else being equal, without hedge funds, dealers would have to intermediate an additional 14% of transaction volume from other clients, using their own balance sheets. Most other types of clients’ transactions are typically either not in the opposite direction or have smaller opposite direction ratios.

I remember being told by the chief bond trader at a major bank that my old firm was very helpful to him in the course of day to day operations, because our trading was generally counter-flow, helping him to turn over his inventory a little more quickly (it also reduces the need for hedging). Naturally, a discount has to be applied to what a dealer says because half their job is to tell clients how smart they are, but the words made sense at the time and make sense now.

Another gem is:

We follow the methodology of Czech et al. (2021) to construct a GoC bond portfolio based on the bonds that hedge funds bought and sold the most on each day they transact in the opposite direction of other clients. We then calculate the excess returns of each day’s portfolio over different horizons (see the Appendix for details). This approach is only a proxy to assess hedge funds’ excess returns because their strategies may involve assets other than GoC bonds. Nevertheless, the approach is useful to assess whether hedge funds are capitalizing on imbalances in the GoC bond market.

Chart 3 shows the excess returns from hedge funds’ GoC bond transactions over a 1-, 5- and 10-day horizon. These excess returns are statistically significant and increase up to the 5-day horizon but lose significance and return close to zero at the 10-day horizon. These results suggest that on days when hedge funds transact in the opposite direction, they could be capitalizing on temporary supply and demand imbalances because their transactions generate excess returns over a short horizon and then decline toward zero.

This ties in with my essay titled ‘Naive Hedge Funds’.

At my old firm, our holding period was much longer than the very short intervals studied in this chart, but that is because we weren’t, technically, a hedge fund (except for a little bit with a specialty product); we were investment managers, seeking to hold the cheapest portfolio of cash-flows that we could, subject to various constraints on portfolio composition that essentially made us more of an ‘index-plus’ firm rather than a classical hedge fund.

The authors conclude:

While GoC bond transactions of hedge funds are typically in the opposite direction to those of other market participants, we find that during the peak period of market turmoil in March 2020, hedge funds sold GoC bonds, just as other market participants did. This shows that hedge funds can at times contribute to one-sided markets and amplify declines in market liquidity. These results help to advance Bank staff’s understanding of the asset management sector and of asset managers’ behaviour in periods of market turmoil.

TIPS Liqudity

Friday, February 10th, 2023

Here’s an interesting paper partly about TIPS liqudity, titled The Microstructure of the TIPS Market by Michael J. Fleming and Neel Krishnan:

  • • The potential advantages of Treasury inflationprotected securities have yet to be fully realized, mainly because TIPS are not as liquid as nominal Treasury securities.
  • • The less liquid nature of TIPS may adversely affect prices relative to those of nominal securities, offsetting the benefits of TIPS having no inflation risk.
  • • A study of TIPS, using novel tick data from the interdealer market, provides new evidence on the liquidity of the securities and how liquidity differs from that of nominal securities.
  • • Analysis of various liquidity measures suggests that trading activity and the incidence of posted quotes may be better cross-sectional gauges of TIPS liquidity than bid-ask spreads or quoted depth.
  • • Differences in intraday trading patterns and announcement effects between TIPS and nominal securities likely reflect the different use, ownership, and cash-flow attributes of the securities


These potential benefits have not been fully realized, mainly because TIPS lack market liquidity compared with nominal securities.{2} This lack of liquidity is thought to result in TIPS yields having a liquidity premium relative to nominal securities, which offsets the inflation risk premium.{3} Similarly, the presence of a liquidity premium in TIPS yields complicates inferences of inflation expectations, particularly if the premium changes over time. However, despite the importance of TIPS liquidity and the market’s large size ($728 billion as of November 30, 2011), there has been virtually no quantitative evidence on the securities’ liquidity.

Footnote 2: Market liquidity is defined here as the cost of executing a trade, which can depend on the trade’s size, timing, venue, and counterparties. It is often gauged by various measures, including the bid-ask spread, the price impact of trades, quoted depth, and trading activity.

Footnote 3: D’Amico, Kim, and Wei (2008) estimate that the liquidity premium was about 1 percent in the early years of the TIPS program. Pflueger and Viceira (2011) find that the liquidity premium is around 40 to 70 basis points during normal times, but was more during the early years of TIPS and during the 2008-09 financial crisis. Sack and Elsasser (2004) argue that TIPS have not reduced the Treasury’s financing costs because of several factors, including lower liquidity. Roush (2008) finds that TIPS have saved the government money, except during the early years of the program. Dudley, Roush, and Ezer (2009) show that the ex ante costs of TIPS issuance are about equal to the costs of nominal securities issuance.

Our study proceeds as follows. Section 2 discusses institutional features of the market for TIPS. In Section 3, we describe the tick data used in our empirical analysis. Section 4 reports our empirical results, including trading activity by sector, the liquidity of on-the-run and off-the-run securities, price impact estimates, intraday patterns in trading activity and liquidity, and the effects of major announcements. Section 5 concludes.

Our analysis of the TIPS market identifies several microstructure features also present in the nominal Treasury securities market, but several unique features as well. As in the nominal market, there is a marked difference in trading activity between on-the-run and off-the-run TIPS, as trading drops sharply when securities go off the run. In contrast to the nominal market, there is little difference in bid-ask spreads or quoted depth between these securities, but there is a difference in the incidence of posted quotes. The results suggest that trading activity and quote incidence may be better crosssectional measures of liquidity in the TIPS market than bid-ask spreads or quoted depth.

Intraday patterns of trading activity are broadly similar in the TIPS and nominal markets, but TIPS activity peaks somewhat later, likely indicating differences in the use and ownership of these securities. Announcement effects are also different, probably reflecting the types of information most important to the particular securities. The employment report is the most important announcement in the nominal market, but it elicits relatively little response in the TIPS market in terms of trading activity. In contrast, announcements of the consumer price index and the results of TIPS auctions precipitate significant increases in TIPS trading activity, likely indicating these announcements’ particular importance to TIPS valuation

There’s also Trading Activity and Price Transparency in the Inflation Swap Market by Michael J. Fleming and John R. Sporn:

  • • Liquidity and price transparency in derivatives markets have become increasingly important concerns, yet a lack of transaction data has made it hard to fully understand how the inflation swap and other derivatives markets work.
  • • This study uses novel transaction data to shed light on trading activity and price transparency in the rapidly growing U.S. inflation swap market.
  • • It reveals that the market is reasonably liquid and transparent, despite its over-the-counter nature and low level of trading activity. Transaction prices are typically near widely available end-of-day quoted prices and realized bid-ask spreads are modest.
  • • The authors also identify concentrations of activity in certain tenors and trade sizes and among certain market participants as well as point to various attributes that explain trade sizes and price deviations.


Several recent studies have compared the inflation swap rate with breakeven inflation as calculated from Treasury inflationprotected securities (TIPS) and nominal Treasury bonds.1 The two market-based measures of expected inflation should be equal in the absence of market frictions. In practice, inflation swap rates are almost always higher, with the spread exceeding 100 basis points during the recent financial crisis.

Our data set contains 144 U.S. dollar zero-coupon inflation swap transactions, or an average of 2.2 transactions over the 65 trading days in our sample.9 Daily notional trading volume is estimated to average $65 million. Three-quarters (108/144) of the transactions are new trades, 24 percent (35/144) are assignments of existing transactions (whereby one counterparty to a swap steps out of the deal and assigns its position to a new counterparty), and 1 percent (1/144) are cancelations. One new transaction has a forward start date, for which the accrual period begins two years after the trade date, with the remaining 107 new transactions starting two or three business days after the trade date.

We also identify a concentration of activity among certain market participants. In particular, 54 percent (78/144) of our trades are between G14 dealers, 39 percent (56/144) are between G14 dealers and customers, and 7 percent (10/144) are between customers. Of the new trades between G14 dealers and customers, the G14 dealer receives fixed 63 percent (19/30) of the time and pays fixed 37 percent (11/30) of the time.11 New trades in which dealers receive fixed are larger, so that dealers receive fixed for 81 percent of new contract volume. That is, dealers are largely paying inflation and receiving fixed in their interactions with customers.

Our analysis of a novel transaction data set uncovers relatively few trades—just over two per day –in the U.S. zero-coupon inflation swap market. Trade sizes, however, are large, averaging almost $30 million. Sizes are generally larger for new trades, especially if they are bulk and allocated across subaccounts, and tend to decrease with contract tenor. We also identify concentrations of activity—with 45 percent of trades at the ten-year tenor, and 36 percent of all trades (and 48 percent of new ones) for a notional amount of $25 million. Over half the trades (54 percent) are between G14 dealers, 39 percent are between G14 dealers and other market participants, and 7 percent are between other market participants. We identify just eighteen market participants during our study’s sample period, made up of nine G14 dealers and nine other market participants.

Despite the low level of activity in this over-the-counter market, we find that transaction prices are quite close to widely available end-of-day quoted prices. The differential between transaction prices and end-of-day quoted prices tends to decrease with tenor and increase with trade size and for customer trades. By comparing trades for which customers pay fixed with trades for which they receive fixed, we are able to infer a realized bid-ask spread for customers of 3 basis points, which is consistent with the quoted bid-ask spreads reported by dealers.

In sum, the U.S. inflation swap market appears reasonably liquid and transparent despite the market’s over-the-counter nature and modest activity. This likely reflects the fact that the market is part of a larger market for transferring inflation risk that includes TIPS and nominal Treasury securities. As a result, inflation swap positions can be hedged quickly and with low transaction costs using other instruments, and prices of these other instruments can be used to efficiently price inflation swaps, despite modest swap activity

Not exactly the world’s biggest market! I looked up inflation swaps because I was interested in the question “Who the hell pays inflation”, which came to mind due to this article in the Globe, The government ditched inflation-protected bonds – companies should start issuing their own by JOHN H. COCHRANE AND JON HARTLEY:

If the government won’t do it, corporations, banks and financial institutions should issue these bonds themselves rather than just complain. Not every asset must be provided by the government.

If the government won’t do it, however, there is no reason that the government’s critics can’t issue them. Companies can issue real return bonds, as they already issue U.S. dollar bonds. Banks can offer real return accounts and certificates of deposit.

If the government steps out of the market, there’s all the more demand for private issuers to step in. Pension funds desperate to replace vanishing inflation-indexed government bonds are natural clients. Company profits rise and fall with inflation, so they have a natural incentive to issue bonds whose payments rise and fall with inflation. Even mortgage rates could rise and fall with an index of wages.

Why not? Broadly, this reluctance seems one more symptom of an overleveraged, overregulated, government-dependent and not very competitive or innovative banking and financial system. Banks and other financial institutions only want to issue or expand a new product if they can quickly lay off the risk onto the government, and earn steady fees. The model of issuing equity to bear risk and then offering a profitable innovative product to consumers is too out of fashion.

Frankly, I thought the article was naive, but thought: “Who the hell would issue these things? Who’s got a natural hedge against inflation that they might want to offload? Assuming they can recover the ultra-massive liquidity premium there’s gonna be on a, say, 1-billion long-term linker issue from a corporation, that is.” All I could think of was utility companies who have long-term assets currently financed by long-term nominal bonds, with the assets producing commodity-linked revenue. Maybe they could finance with linkers instead? Maybe pipelines? So, I started looking for information on inflation swaps …

I can’t answer the question definitively. The authors of the swaps paper didn’t investigate where the open interest is lodged. But there is enough information in the paper that I’m willing to bet a nickel (a full nickel, mind you, not just a few pennies) that it’s the dealers. The dealers will pay inflation and they buy TIPS to hedge. BORRRRRRR-ING! And it doesn’t work without government-issued linkers.

Bank of Canada Studies Other Central Banks

Wednesday, January 11th, 2023

The Bank of Canada has released Staff Discussion Paper 2023-2, by Monica Jain, Walter Muiruri, Jonathan Witmer, Sharon Kozicki & Jeremy Harrison titled Summaries of Central Bank Policy Deliberations: A Canadian Context:

This paper provides the context, rationale and key considerations that informed the Bank of Canada’s decision to publish a summary of monetary policy deliberations. It includes an analysis of how other central banks disclose minutes and summaries of their monetary policy deliberations.

Most other central banks surveyed publish some sort of summary of deliberations. The Bank of Canada’s existing communications already include aspects of these summaries. However, the Bank does not normally provide some information that they contain, such as:

  • • a review of the policy choices that were discussed
  • • a diversity of viewpoints on the economic outlook and policy choices
  • • the perspectives of individual members

Publishing a summary of deliberations could enhance transparency, accountability and credibility and also reinforce the Bank’s independence. However, these benefits must be balanced against the potential for constraints on internal debate or the sending of mixed messages about the Bank’s outlook and decisions. The Bank of Canada Act empowers the Governor to make decisions, but in practice, decisions are made by consensus among members of the Bank’s Governing Council. This decision-making by consensus could have implications for what could or should be included in a summary.

In the Canadian context, assuming the Bank will provide additional information, we also discuss some advantages and disadvantages of providing a summary of deliberations as a separate communication product or as an enhancement to current communications products.

The material in the paper originally served as background information for internal discussions at the Bank of Canada around publishing a summary of policy deliberations. Following those discussions, the International Monetary Fund (IMF) published a review of the Bank of Canada’s transparency, concluding that the Bank “… sets a high benchmark for transparency” (IMF 2022). In that review, the IMF provided a recommendation on how the Bank could further improve its transparency by providing more information on its monetary policy deliberations. In response to the IMF review and internal discussions at the Bank, the Bank has publicly committed to providing a summary of its policy deliberations beginning in February 2023.

The most desperately needed disclosure is – as Assiduous Readers will be sick to death of me complaining – voting records. So here’s a table comprised of their summaries of voting records:

Country Policy
Canada The BoC follows a consensus-based decision-making approach so does not disclose voting records.
New Zealand The RBNZ follows a consensus-based decision-making process so does not disclose voting records.
Australia The RBA follows a consensus-based decision-making approach so does not disclose voting records.
Norway Norges Bank follows a consensus-based decision-making approach so does not disclose voting records.
United States of America The Fed lists all the members (by name) who voted for and against the proposed policy at the meeting.
England The BoE lists all the members (by name) who voted for and against the proposed policy at the meeting.
Sweden In the opening few sentences of their monologue, each Committee member states whether they voted for or against the proposed policy at the meeting.
Europe Although the ECB follows a voting-based decision-making approach, it does not disclose the voting records.
Japan The BoJ lists all the members (by name) who voted for and against the proposed policy at the meeting.

Consensus is for second-raters and time-servers. A confident, intelligent person will not feel any shame about being in the minority, even if on a repeated basis. Hell, Leon Trotsky was a proud member of the Menshevik (minority) Party and he got a lot of respect in his day! I take issue with the following quotation from the abstract:

However, these benefits must be balanced against the potential for constraints on internal debate or the sending of mixed messages about the Bank’s outlook and decisions.

Dammit, I want mixed messages! Only idiots will take the view that monetary policy is a puzzle with only one answer – it’s complex and is concerned exclusively of forecasts about the future that are, we hope, backed up by excellent data and analysis of current conditions. While the consensus phrase ‘risks to the forecast include…’ may attempt to give a sense of the uncertainty, it is nowhere near as useful as ‘so-and-so was so concerned about the potential for X that he voted against the policy decision! He put his name on it! He stepped up and advocated an unpopular position for no other reason than he thought it was right! Pay attention, people!’

I will also take issue with the other justification put forward, that increased transparency (such as publicizing voting records) will constrain internal debate. OK, I say, relative to what? People will feel constrained from vigorously asserting their views for all sorts of stupid reasons and I will suggest that the necessity for eventual consensus is a greater constraint that the publication of a dissenting vote with a brief note of explanation. Arse-kissers and group-thinkers thrive in an environment in which they are explicitly expected to agree with the loudest voice in the meeting, and we don’t want any of them setting monetary policy!

Other data compared in the tables are disclosures of:

  • Discussion of risks
  • Data and projections
  • Financial developments
  • Economic developments
  • Areas of discussion in deliberations specified
  • Detail of meeting transcript/summary
  • Diversity of views
  • Indications of future policy interest rate decisions
  • Indications of future non-interest-rate policy decisions
  • Publishes a monetary policy report
  • Discusses conflicts in policy decisions

Gilt Market Break: Charlatans & Leverage

Monday, November 7th, 2022

Sarah Breeden, the Bank of England’s Executive Director for Financial Stability Strategy and Risk, has delivered a speech titled Risks from leverage: how did a small corner of the pensions industry threaten financial stability?:

But in the days leading up to that fateful Wednesday and following the announcement of the Government’s growth plan on 23 September, long-dated gilt yields in particular had moved with extraordinary and unprecedented scale and speed.

Now volatility itself does not warrant Bank of England intervention. Indeed, it’s essential that market prices are allowed to adjust to changes in their fundamental determinants efficiently and without distortion.

However, some liability-driven investment (LDI) funds were creating an amplification mechanism in the long-end of the gilt market through which price falls had the potential to trigger forced selling and thereby become self-reinforcing. Such a self-reinforcing price spiral would have resulted in even more severely disrupted gilt market functioning. And that would in turn have led to an excessive and sudden tightening of financing conditions for households and businesses.

In response to this threat, the Bank of England intervened on financial stability grounds. But what led to that intervention?

The root cause is simple – and indeed is one we have seen in other contexts too – poorly managed leverage.

Many UK DB pension schemes have been in deficit, meaning their liabilities – their commitments to pay out to pensioners in the future – exceed the assets they hold. DB pension schemes invest in long-term bonds to hedge the interest rate and inflation risk that arises from these long-term liabilities. But that doesn’t help them to close their deficit. To do that, they invest in ‘growth assets’, such as equities, to get extra return to grow the value of their assets. An LDI strategy delivers this, using leveraged gilt funds to allow schemes both to maintain material hedges and to invest in growth assets. Of course that leverage needs to be well managed.

The rise in yields in late September – 130 basis points in the 30-year nominal yield in just a few days – caused a significant fall in the net asset value of these leveraged LDI funds, meaning their leverage increased significantly. And that created a need urgently to delever to prevent insolvency and to meet increasing margin calls.

The funds held liquidity buffers for this purpose. But as those liquidity buffers were exhausted, the funds needed either to sell gilts into an illiquid market or to ask their DB pension scheme investors to provide additional cash to rebalance the fund. Since persistently higher interest rates would in fact boost the funding position of DB pension schemes[1], they generally had the incentive to provide funds. But their resources could take time to mobilise.

The issue was particularly acute for one small corner of the LDI industry – pooled funds. In these funds, which make up around 10-15% of the LDI market, a pot of assets is managed for a large number of pension fund clients who have limited liability in the face of losses. The speed and scale of the moves in yields far outpaced the ability of the large number of pooled funds’ smaller investors to provide new funds who were typically given a week, in some cases
two, to rebalance their positions. Limited liability also meant that these pooled fund investors might choose not to provide support. And so pooled LDI funds became forced sellers of gilts at a rate that would not have been absorbed in normal gilt trading conditions, never mind in the conditions that prevailed during the stressed period.

Other LDI funds, with segregated mandates, were more easily able to raise funds from their individual pension scheme clients. However, given their scale, at 85-90% of the market, some of these funds were also contributing to selling pressure, making the task at hand for pooled LDI funds even harder. And of course if the pooled funds had defaulted, the large quantity of gilts held as collateral by those that had lent to the funds would potentially be sold on the market too.

With the gilt market unable to absorb such forced sales, yields would have been pushed even higher, making the scale of the selling need even larger still. This is the self-reinforcing spiral that the Bank intervened to prevent.

The Bank’s 13 day and £19.3 billion intervention was made on financial stability grounds. It was the first example of us acting to deliver our financial stability objective through a temporary, targeted intervention in the gilt market.

But let me emphasise: the asset purchases were a means to an end. They were designed to create the right conditions in the right part of the gilt market for long enough so that the LDI funds could build resilience so that their leverage would be well managed once the asset purchases had ceased and should gilt market instability return.

A common factor across all the uses of leverage I have just described is that it can increase the exposure of the leverage taker to underlying risk factors – whether that be house prices, earnings, interest rates, currencies or asset prices. It follows therefore that leverage can amplify shocks to each of these risk factors. And in a stress, that can lead both to sudden spikes in demand for liquidity – either to support the financing of leveraged positions or as deleveraging leads to forced sales – and a corresponding contraction in liquidity supply, with potentially systemic consequences.

Leverage is of course not the only cause of systemic vulnerability in the non-bank system – as we have seen with liquidity mismatch driving run dynamics in money market funds (MMFs) and open-ended funds (OEFs) during the dash for cash.[4] But it is important where any form of leverage is core to a non-bank’s business and trading strategy. Indeed what happened to LDI funds is just the latest example of poorly managed non-bank leverage throwing a large rock into the pool of financial stability. From Long Term Capital Management in 1998; to the 2007 run on the repo market; to hedge fund behaviour in the 2020 dash for cash; and the failure of Archegos in 2021.

These episodes highlight the need to take into account the potential amplifying effect of poorly managed leverage, and to pay attention to non-banks’ behaviours which, particularly when aggregated, could lead to the emergence of systemic risk.

Regulators worked with LDI funds during the Bank’s operations to ensure greater resilience for future stresses. And in aggregate, intelligence suggests that LDI funds raised over £40 billion in funds and made over £30 billion of gilt sales during our operations, both of which have contributed to significantly lower leverage.

As a result, LDI funds report that their liquidity buffers can withstand very much larger increases in yields than before, well in excess of the previously unprecedented move in gilt yields. And so the risk of LDI fund behaviour triggering ‘fire sale’ dynamics in the gilt market and self-reinforcing falls in gilt prices is – for now at least – significantly reduced. It is important that it stays that way.

I’m sure there will be more material on this liquidity black hole to follow, but for now I’ll just register my continuing disgust with the charlatans and nincompoops who are such a feature of the investment management industry.

MMFs with Floating vs. Fixed Share Prices

Tuesday, July 19th, 2022

A discussion on an unrelated thread regarding historical pricing on brokerage statements for GICs eventually expanded to include historical pricing for Money Market Funds. As MMFs are marketable instruments, there are wider implications of this policy than there are for GICs.

Jonathan Witmer of the BoC wrote a working paper in 2012 titled Does the Buck Stop Here? A Comparison of Withdrawals from Money Market Mutual Funds with Floating and Constant Share Prices:

Recent reform proposals call for an elimination of the constant net asset value (NAV) or “buck” in money market mutual funds to reduce the occurrence of runs. Outside the United States, there are several countries that have money market mutual funds with and without constant NAVs. Using daily data on individual fund flows from these countries, this paper evaluates whether the reliance on a constant NAV is associated with a higher frequency of sustained fund outflows. Preliminary evidence suggests that funds with a constant NAV are more likely to experience sustained outflows, even after controlling for country fixed effects and other factors. Moreover, these sustained outflows in constant NAV money market funds were more acute during the period of the run on the Reserve Primary fund, and were subdued after the U.S. Treasury guarantee program for money market funds was put in place. Consistent with the theory that constant NAV funds receive additional implicit support from fund sponsors, fund liquidations are less prevalent in funds with a constant NAV following periods of larger outflows.

This paper is the first to examine the usage of a constant NAV structure across countries. It is well known that money market funds in some countries, such as the United States, employ a constant NAV structure. It is less well known to what extent other countries use a different structure. The main difference between floating NAV and constant NAV money market funds is the use of amortized cost accounting. Floating NAV money market mutual funds measure the value of their positions using fair value or market prices. For constant NAV money market funds, the value is recorded as the initial cost, plus the straight line amortization of the position’s premium or discount at the time of purchase through to the position’s maturity date. This paper shows that many European countries have a mixture of both fund types.

Here’s the interesting bit – how predatory traders are able to fleece naive investors:

This paper also contributes to the broader literature that examines the relation between stale share prices, illiquid fund holdings, and fund flows in equity and bond mutual funds. Arbitrageurs can take advantage of stale prices in illiquid mutual funds at the expense of the remaining shareholders. These apparent arbitrage opportunities induce a change in flows in these mutual funds. The paper by Lyon (1984) finds this arbitrage activity dilutes other shareholders in money market funds by an estimated 10 bps per year. This dilution is even larger in international equity mutual funds, where dilution can be upwards of 1% per year (e.g., Greene and Hodges, 2002; Zitzewitz, 2003).

During the first part of September 2008 when there was a run on the Reserve Primary Fund, constant NAV money market funds experienced more outflows than did floating NAV money market funds. Further, after the U.S. Treasury implemented its guarantee program for money market funds, constant NAV U.S.-domiciled U.S. dollar funds performed much better and sustained a decrease in prolonged outflows during the guarantee period, relative to non-U.S. domiciled U.S. dollar funds.

After the crisis, the SEC amended rule 2a-7 to improve the resiliency of money market mutual funds. These amendments included tighter restrictions on the credit quality, maturity, and liquidity of portfolio holdings for money market funds. The maximum dollar-weighted average maturity was reduced to 60 days, and a maximum dollar-weighted average life to maturity was introduced and set at 120 days. As for the liquidity requirements, a minimum of ten percent of a fund’s portfolios must be invested in “Daily Liquid Assets” and a minimum of thirty percent must be invested in “Weekly Liquid Assets”. The amended rule 2a-7 also requires monthly website disclosure of portfolio holdings, including information

The author concludes, in part:

This paper has several important policy implications. There is an active push to reform money market mutual funds in the wake of the financial crisis and more specifically following the run on the Reserve Primary Fund and subsequent government support of money market funds in the United States. One of the primary proposals is to move away from the CNAV money market fund structure and towards the VNAV structure. Some observers have contended that such a move does little to reduce the occurrence of runs in money market mutual funds, based on anecdotal evidence of run behaviour in ultrashort bond funds in the United States and enhanced money market funds in Europe, both of which maintain a VNAV structure (Investment Company Institute, 2011; HSBC, 2011). These funds, however, are not subject to the same liquidity, credit, and maturity restrictions as money market funds. This paper compares a large number of money market mutual funds across several countries and finds that, on the contrary, the VNAV structure is less susceptible to run-like behaviour relative to CNAV money market funds.

However, the VNAV structure does not fully eliminate this run-like behaviour. This is consistent with the model of Chen, Goldstein, and Jiang (2011), which shows that mutual funds holding illiquid assets experience more outflows following a period of poor performance, relative to funds holding liquid assets (their empirical examination focuses on equity mutual funds). That is, in their model investors may redeem on the self-fulfilling belief that others will be redeeming, imposing the costs of liquidating the fund’s illiquid assets on remaining shareholders. While money market funds generally hold liquid, shortterm assets, these assets may become illiquid during periods of stress or, put another way, during periods when there is a belief that a fire sale of some money market fund holdings may occur. Even during periods of stress, however, CNAV money market funds are more prone to run-like behaviours, relative to VNAV money market funds.

Given my own views on the subject, expressed in A Collateral Proposal and The Future of Money Market Regulation, I was most interested in his final paragraph:

Not only does the CNAV structure have a higher occurrence of sustained outflows, but also there is some evidence to suggest that it is associated with an implicit guarantee provided by fund sponsors. This implicit guarantee has both advantages and disadvantages. The presence of an implicit guarantee can reduce moral hazard and reduce risk-taking in money market mutual funds, since the fund sponsor would be concerned that the poor performance of the fund may have negative spillovers on the sponsor’s other businesses (Kazpercyk and Schnabl, 2012). The amount of risk-taking depends upon both the sponsor’s financial strength as well as the reputational concerns about the effect of “breaking the buck” on the rest of the sponsor’s fund and non-fund businesses. On the other hand, an implicit guarantee is a potential channel for contagion between the banking sector and money market mutual funds. Losses in a money market mutual fund may be passed onto the fund sponsors should they provide support to the fund. As well, a weakening of a fund sponsor could be passed onto the money market fund sector through a reduction in the value of the implicit guarantee.

A Primer on the Canadian Bankers’ Acceptance Market

Monday, June 18th, 2018

The Bank of Canada has released a staff discussion paper by Kaetlynd McRae and Danny Auger titled A Primer on the Canadian Bankers’ Acceptance Market:

This paper discusses how the bankers’ acceptance (BA) market in Canada is organized and its essential link to the Canadian Dollar Offered Rate (CDOR). Globally, BAs are a niche product used only in a limited number of jurisdictions. In Canada, BAs provide a key source of funding for small and medium-sized corporate borrowers that may not otherwise have direct access to the primary funding market because of their size and credit ratings. More recently, BAs have also become an increasingly important funding source for large corporate borrowers because of credit-rating downgrades in certain sectors and industry consolidation. With the market’s continued growth, BAs account for the greatest portion of money market instruments issued by non-government entities and are the second-largest money market instrument overall in Canada, averaging just over 25 per cent of the total domestic money market in 2017. For the investment community in Canada, BAs provide a source of short-term income and liquidity because of their relatively attractive yield, liquidity and credit ratings.

The BA market is intrinsically linked to CDOR, which was originally developed to establish a daily benchmark reference rate for BA borrowings. This rate is quite nuanced compared with rates in other jurisdictions in that it is not directly a bank borrowing rate. Instead, it is a committed lending rate at which banks are contractually willing to lend cash to corporate borrowers with existing BA facilities. CDOR is also used as the main interest rate benchmark for calculating the floating-rate component of both over-the-counter and exchange-traded Canadian-dollar derivative products. Another use of CDOR is to determine interest payments on floating-rate notes.

I admit to being a little disappointed that my concerns regarding the precise credit quality of BAs were not addressed in the paper. I would also have liked to see a discussion regarding the application of covered bond legislation to BAs.

October 26, 2017

Friday, October 27th, 2017

The winner of the Charles Brandes Prize, awarded by the Brandes Institute, has been announced and is an excellent effort by Samuel M. Hartzmark and David H. Solomon titled The Dividend Disconnect:

We show that many individual investors, mutual funds and institutions trade as if dividends and capital gains are separate disconnected attributes, not fully appreciating that dividends come at the expense of price decreases. Behavioral trading patterns (e.g. the disposition effect) are driven by price changes excluding dividends. Investors treat dividends as a separate stable income stream, holding high dividend-yield stocks longer and displaying less sensitivity to their price changes. We term this mistake the free dividends fallacy. Demand for dividends is systematically higher in periods of low interest rates and poor market performance, leading to high valuations and lower future returns for dividend-paying stocks. Investors rarely reinvest dividends into the stocks from which they came, instead purchasing other stocks. This creates predictable marketwide price increases on days of large aggregate dividend payouts, concentrated in stocks not paying dividends.

If investors are subject to the free dividends fallacy, viewing dividends as a distinct source of income, they should place a higher value on that perceived income stream when other options for income are less attractive. For an investor exhibiting the free dividends fallacy, perhaps the closest substitute for dividend income is from bonds. We nd that dividend demand is higher when the interest rate is low, consistent with the periodic payments from bonds appearing less attractive. In
the cross-section, demand is higher for stocks whose dividends are more stable, and whose dividends have increased in the recent past. In addition, the demand for dividends is lower when recent past market returns have been higher. In these times, the smaller predictable stream of payments from dividends is apt to appear less attractive compared with the large recent capital gains, if the two components are evaluated as separate alternative ways to make money on a stock.

I don’t agree with this bit:

Finally, if investors view dividend payments as being separate from the value of their position, they may not reinvest dividends into the stocks from which they came. This has been shown before for the case of individuals in Baker et al. (2007), who argued that dividends were financing consumption. We show that dividend reinvestment is also rare among mutual funds and institutions (similar to Kaustia and Rantapuska (2012) using Finnish data). As well as being more sophisticated than retail investors, most mutual funds and institutions lack the consumption motive of individuals, meaning that there must be other motives for their behavior. Using quarterly holdings, we examine how often dividend-paying holdings increase by approximately the number of shares that could be purchased with the dividend on the payment date (when reinvestment requires a non-trivial number of shares). We compare this to another benchmark for passive investing – holding exactly the same number of shares in the subsequent quarter, and leaving the dividend in cash or investing it elsewhere. We show that dividend reinvestment is only about 2.3% as common as zero holdings changes for the case of mutual funds, and 9.6% as common for institutional investors. If revealed preference is to be believed, the low level of dividend reinvestment implies that these investors have a desire to marginally reduce their portfolio weights by the exact amount of the dividend starting on the ex-dividend date. It seems more likely that these sophisticated investors are either not directly tracking which dividends correspond to which stocks for reinvestment purposes, or do not
care enough to maintain particular portfolio weights.

Portfolio cash flows are an excellent means to slowly rebalance portfolios. Any portfolio manager, good or bad, will have three categories of stocks: buy, hold, sell. When one of the ‘hold’ stocks pays a dividend, there is not necessarily any rational reason to reinvest the dividends in that issue; there will be at least some rationale to reinvest the dividend in one of the ‘buy’ stocks.

I’m also skeptical of this bit:

The disconnect between price changes and dividends also helps to unify a number of results that are puzzling under normal assumptions about returns. Baker et al. (2007) present evidence that individuals like to consume out of their dividends, consistent with the mental accounting distinctions between dividends and capital gains. Baker andWurgler (2004b) argue for a catering theory whereby investors have a general demand for dividends due to psychological or institutional reasons, though the psychology behind this is not discussed at length. The free dividends fallacy not only explains psychologically why dividends may be desirable, but also why the shifting attractiveness of capital gains and dividends can generate time-varying demand for dividends which rms respond to (Baker and Wurgler 2004a). Valuing dividends purely as an income stream can also help to explain the observed preference that older investors have for dividends documented in Graham and Kumar (2006) and Becker et al. (2011), and the fact that investors do not perceive the risk-reward tradeoff inherent in the change in leverage associated with a dividend, as shown in Welch (2016). An overall demand for dividends is consistent with Hartzmark and Solomon (2013), who document abnormally positive returns during dividend months linked to price pressure from dividend-demanding investors. Harris et al. (2015) show that mutual funds have a tendency to juice their dividend yield by trading in and out of dividend-paying stocks to increase the fund’s dividend yield at the expense of overall returns. These results all point to a generalized time-varying demand for dividends, but do not explain why dividends are desirable.

Prices are more volatile than dividends; it is therefore desirable, in a consumption situation such as retirement, to arrange one’s portfolio so that income is spent while the capital is untouched – this forms a part of ‘Sequence of Returns Risk’.

However, this bit is sobering:

Our results suggest that the free dividends fallacy is costly to investors because of the systematic nature of time-varying dividend demand. In addition to the direct costs and benefits associated with dividend paying stocks (such as taxes, trading costs and reinvestments), if investors buy dividend paying stocks when they are relatively over-priced due to a general demand for dividends, they will earn predictably lower returns. We estimate that investors buying dividend-paying stocks during times of high demand earn roughly 2-4% less per year in expectation. Thus an investor whose preferences for dividends cause him to shift into and out of dividend-paying stocks at the same time as other investors would lose a significant portion of the equity premium by doing so.

HIMIPref™ Preferred Indices
These values reflect the December 2008 revision of the HIMIPref™ Indices

Values are provisional and are finalized monthly
Index Mean
Current
Yield
(at bid)
Median
YTW
Median
Average
Trading
Value
Median
Mod Dur
(YTW)
Issues Day’s Perf. Index Value
Ratchet 0.00 % 0.00 % 0 0.00 0 -0.0169 % 2,424.1
FixedFloater 0.00 % 0.00 % 0 0.00 0 -0.0169 % 4,448.1
Floater 3.78 % 3.94 % 33,794 17.55 4 -0.0169 % 2,563.5
OpRet 0.00 % 0.00 % 0 0.00 0 -0.0132 % 3,078.7
SplitShare 4.74 % 4.70 % 67,888 4.35 6 -0.0132 % 3,676.6
Interest-Bearing 0.00 % 0.00 % 0 0.00 0 -0.0132 % 2,868.6
Perpetual-Premium 5.36 % -0.32 % 66,293 0.18 17 -0.0370 % 2,827.7
Perpetual-Discount 5.29 % 5.24 % 67,546 15.00 19 0.0693 % 2,977.5
FixedReset 4.24 % 4.24 % 146,536 4.52 99 -0.1374 % 2,480.1
Deemed-Retractible 5.06 % 5.48 % 99,735 5.98 30 0.0717 % 2,913.1
FloatingReset 2.74 % 2.78 % 45,576 4.03 8 0.2177 % 2,677.7
Performance Highlights
Issue Index Change Notes
MFC.PR.L FixedReset -2.09 % YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2025-01-31
Maturity Price : 25.00
Evaluated at bid price : 22.48
Bid-YTW : 5.67 %
SLF.PR.I FixedReset -1.18 % YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2025-01-31
Maturity Price : 25.00
Evaluated at bid price : 24.36
Bid-YTW : 4.52 %
TRP.PR.A FixedReset -1.14 % YTW SCENARIO
Maturity Type : Limit Maturity
Maturity Date : 2047-10-26
Maturity Price : 20.03
Evaluated at bid price : 20.03
Bid-YTW : 4.49 %
CM.PR.Q FixedReset -1.02 % YTW SCENARIO
Maturity Type : Limit Maturity
Maturity Date : 2047-10-26
Maturity Price : 23.15
Evaluated at bid price : 24.30
Bid-YTW : 4.41 %
IAG.PR.A Deemed-Retractible 1.39 % YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2025-01-31
Maturity Price : 25.00
Evaluated at bid price : 23.32
Bid-YTW : 5.84 %
PWF.PR.Z Perpetual-Discount 1.40 % YTW SCENARIO
Maturity Type : Limit Maturity
Maturity Date : 2047-10-26
Maturity Price : 24.22
Evaluated at bid price : 24.60
Bid-YTW : 5.24 %
Volume Highlights
Issue Index Shares
Traded
Notes
RY.PR.Q FixedReset 81,229 YTW SCENARIO
Maturity Type : Call
Maturity Date : 2021-05-24
Maturity Price : 25.00
Evaluated at bid price : 26.63
Bid-YTW : 3.44 %
SLF.PR.E Deemed-Retractible 51,200 YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2025-01-31
Maturity Price : 25.00
Evaluated at bid price : 21.79
Bid-YTW : 6.87 %
RY.PR.A Deemed-Retractible 51,002 YTW SCENARIO
Maturity Type : Call
Maturity Date : 2017-11-25
Maturity Price : 25.00
Evaluated at bid price : 25.31
Bid-YTW : -14.32 %
RY.PR.O Perpetual-Premium 42,123 YTW SCENARIO
Maturity Type : Call
Maturity Date : 2024-11-24
Maturity Price : 25.00
Evaluated at bid price : 25.22
Bid-YTW : 4.72 %
TRP.PR.C FixedReset 35,750 YTW SCENARIO
Maturity Type : Limit Maturity
Maturity Date : 2047-10-26
Maturity Price : 17.23
Evaluated at bid price : 17.23
Bid-YTW : 4.47 %
TRP.PR.E FixedReset 33,282 YTW SCENARIO
Maturity Type : Limit Maturity
Maturity Date : 2047-10-26
Maturity Price : 22.73
Evaluated at bid price : 23.06
Bid-YTW : 4.40 %
There were 20 other index-included issues trading in excess of 10,000 shares.
Wide Spread Highlights
Issue Index Quote Data and Yield Notes
TD.PF.I FixedReset Quote: 25.50 – 26.00
Spot Rate : 0.5000
Average : 0.3583

YTW SCENARIO
Maturity Type : Call
Maturity Date : 2022-10-31
Maturity Price : 25.00
Evaluated at bid price : 25.50
Bid-YTW : 4.06 %

MFC.PR.L FixedReset Quote: 22.48 – 22.86
Spot Rate : 0.3800
Average : 0.2495

YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2025-01-31
Maturity Price : 25.00
Evaluated at bid price : 22.48
Bid-YTW : 5.67 %

MFC.PR.C Deemed-Retractible Quote: 21.99 – 22.44
Spot Rate : 0.4500
Average : 0.3412

YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2025-01-31
Maturity Price : 25.00
Evaluated at bid price : 21.99
Bid-YTW : 6.74 %

GWO.PR.M Deemed-Retractible Quote: 26.05 – 26.28
Spot Rate : 0.2300
Average : 0.1426

YTW SCENARIO
Maturity Type : Call
Maturity Date : 2017-11-25
Maturity Price : 25.50
Evaluated at bid price : 26.05
Bid-YTW : -14.83 %

SLF.PR.I FixedReset Quote: 24.36 – 24.72
Spot Rate : 0.3600
Average : 0.2742

YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2025-01-31
Maturity Price : 25.00
Evaluated at bid price : 24.36
Bid-YTW : 4.52 %

CU.PR.I FixedReset Quote: 26.15 – 26.65
Spot Rate : 0.5000
Average : 0.4152

YTW SCENARIO
Maturity Type : Call
Maturity Date : 2020-12-01
Maturity Price : 25.00
Evaluated at bid price : 26.15
Bid-YTW : 3.18 %

October 12, 2017

Thursday, October 12th, 2017

There is a very good staff working paper published by the Bank of Canada, by Jean-Sébastien Fontaine and Guillaume Nolin titled Measuring Limits of Arbitrage in Fixed-Income Markets:

We use relative value to measure limits to arbitrage in fixed-income markets. Relative value captures apparent deviations from no-arbitrage relationships. It is simple, intuitive and can be computed model-free for any bond. A pseudo-trading strategy based on relative value generates higher returns than one based on the well-known noise measure. The relative value is therefore a better proxy for limits to arbitrage. We construct relative value indices for the US, UK, Japan, Germany, Italy, France, Switzerland and Canada. Limits to arbitrage increase with the scarcity of capital: we find that each index is correlated with local volatility and funding costs. Limits to arbitrage also exhibit strong commonality across countries, consistent with the international mobility of capital. The relative value indices are updated regularly and available publicly.

Using a static parametric yield curve, Hu, Pan and Wang (2013) (HPW thereafter) show that an index of fitting errors—the “noise” measure—is priced in the cross-section of returns from hedge funds and carry trades. In other words, aggregating these deviations tends to reveal an important financial risk factor.

Measuring fitting errors against a parametric curve is a component of HIMIPref™ I dub “disparity”. The BoC paper then states:

We introduce a new measure of deviations based on the relative value of bonds. This measure is model-free, bypassing the need for preliminary parameter estimation. It is intuitive and easy to compute. For any bond in our sample, we use a small number of comparable bonds to form a replicating portfolio with the same duration and convexity. This bond and its replicating portfolio should have the same expected return. The relative value for that bond is the difference between its yield and that of the replicating portfolio.

So it’s a tightly constrained yield maximizer, also a component of HIMIPref™.

Extending the analysis to several other countries, we find that the relative value index is correlated with local equity market volatility indices and domestic interbank lending market conditions. In addition, the relative value indices exhibit a large degree of commonality across countries. These relative value indices are available publicly and will be regularly updated. We hope that these indices will help to answer a number of research questions. In addition, future research could apply our methodology to create relative value indices for supranational, sub-national or corporate bond markets.

I have a number of technical quibbles about their methodology, but it’s a worthy effort. The two problems that come immediately to mind are first, the quality of the market data (I haven’t seen a bond database yet that hasn’t been riddled with errors) and the fact that there’s no allowance for the cost of shorting. I found in the Treasury Market in the ’90’s that there were a lot of unusually rich issues (particularly in the short end) … and that almost every one of those had ‘gone special’ in the loans market, meaning they were expensive to short. And just try getting data for THAT!

But, I will admit, the part I like best about this paper is that it provides third party validation of my investing style … which is always a useful thing to have on hand when marketing one’s services!

HIMIPref™ Preferred Indices
These values reflect the December 2008 revision of the HIMIPref™ Indices

Values are provisional and are finalized monthly
Index Mean
Current
Yield
(at bid)
Median
YTW
Median
Average
Trading
Value
Median
Mod Dur
(YTW)
Issues Day’s Perf. Index Value
Ratchet 0.00 % 0.00 % 0 0.00 0 0.1857 % 2,420.4
FixedFloater 0.00 % 0.00 % 0 0.00 0 0.1857 % 4,441.3
Floater 3.77 % 3.93 % 30,155 17.60 4 0.1857 % 2,559.6
OpRet 0.00 % 0.00 % 0 0.00 0 -0.1647 % 3,069.5
SplitShare 4.75 % 4.87 % 76,109 4.38 6 -0.1647 % 3,665.7
Interest-Bearing 0.00 % 0.00 % 0 0.00 0 -0.1647 % 2,860.1
Perpetual-Premium 5.36 % -1.68 % 64,217 0.14 17 0.1366 % 2,820.2
Perpetual-Discount 5.35 % 5.31 % 61,187 14.94 19 0.1922 % 2,946.5
FixedReset 4.25 % 4.28 % 157,571 4.58 99 0.1981 % 2,474.7
Deemed-Retractible 5.08 % 5.58 % 101,454 6.01 30 0.1273 % 2,899.9
FloatingReset 2.77 % 2.77 % 50,717 4.06 8 -0.0326 % 2,675.7
Performance Highlights
Issue Index Change Notes
HSE.PR.G FixedReset -2.13 % YTW SCENARIO
Maturity Type : Limit Maturity
Maturity Date : 2047-10-12
Maturity Price : 23.16
Evaluated at bid price : 24.32
Bid-YTW : 5.30 %
PVS.PR.E SplitShare -1.34 % YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2022-10-31
Maturity Price : 25.00
Evaluated at bid price : 25.75
Bid-YTW : 4.98 %
MFC.PR.M FixedReset 1.03 % YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2025-01-31
Maturity Price : 25.00
Evaluated at bid price : 23.43
Bid-YTW : 5.16 %
MFC.PR.L FixedReset 1.09 % YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2025-01-31
Maturity Price : 25.00
Evaluated at bid price : 22.35
Bid-YTW : 5.79 %
SLF.PR.G FixedReset 1.10 % YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2025-01-31
Maturity Price : 25.00
Evaluated at bid price : 18.31
Bid-YTW : 7.70 %
HSE.PR.A FixedReset 1.11 % YTW SCENARIO
Maturity Type : Limit Maturity
Maturity Date : 2047-10-12
Maturity Price : 17.30
Evaluated at bid price : 17.30
Bid-YTW : 4.77 %
RY.PR.J FixedReset 1.18 % YTW SCENARIO
Maturity Type : Call
Maturity Date : 2020-05-24
Maturity Price : 25.00
Evaluated at bid price : 24.90
Bid-YTW : 3.97 %
TD.PF.A FixedReset 1.34 % YTW SCENARIO
Maturity Type : Limit Maturity
Maturity Date : 2047-10-12
Maturity Price : 23.12
Evaluated at bid price : 23.45
Bid-YTW : 4.24 %
BMO.PR.Q FixedReset 1.43 % YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2022-01-31
Maturity Price : 25.00
Evaluated at bid price : 22.67
Bid-YTW : 4.39 %
Volume Highlights
Issue Index Shares
Traded
Notes
TRP.PR.J FixedReset 115,286 YTW SCENARIO
Maturity Type : Call
Maturity Date : 2021-05-31
Maturity Price : 25.00
Evaluated at bid price : 26.71
Bid-YTW : 3.68 %
RY.PR.R FixedReset 113,400 YTW SCENARIO
Maturity Type : Call
Maturity Date : 2021-08-24
Maturity Price : 25.00
Evaluated at bid price : 26.95
Bid-YTW : 3.55 %
TD.PF.D FixedReset 108,102 YTW SCENARIO
Maturity Type : Call
Maturity Date : 2020-07-31
Maturity Price : 25.00
Evaluated at bid price : 24.57
Bid-YTW : 4.21 %
TD.PF.B FixedReset 105,581 YTW SCENARIO
Maturity Type : Limit Maturity
Maturity Date : 2047-10-12
Maturity Price : 22.99
Evaluated at bid price : 23.36
Bid-YTW : 4.27 %
NA.PR.Q FixedReset 104,275 YTW SCENARIO
Maturity Type : Call
Maturity Date : 2017-11-15
Maturity Price : 25.00
Evaluated at bid price : 24.97
Bid-YTW : 1.29 %
RY.PR.J FixedReset 84,784 YTW SCENARIO
Maturity Type : Call
Maturity Date : 2020-05-24
Maturity Price : 25.00
Evaluated at bid price : 24.90
Bid-YTW : 3.97 %
There were 57 other index-included issues trading in excess of 10,000 shares.
Wide Spread Highlights
Issue Index Quote Data and Yield Notes
NA.PR.W FixedReset Quote: 22.86 – 23.50
Spot Rate : 0.6400
Average : 0.3904

YTW SCENARIO
Maturity Type : Limit Maturity
Maturity Date : 2047-10-12
Maturity Price : 22.43
Evaluated at bid price : 22.86
Bid-YTW : 4.34 %

HSE.PR.G FixedReset Quote: 24.32 – 24.80
Spot Rate : 0.4800
Average : 0.2958

YTW SCENARIO
Maturity Type : Limit Maturity
Maturity Date : 2047-10-12
Maturity Price : 23.16
Evaluated at bid price : 24.32
Bid-YTW : 5.30 %

IFC.PR.A FixedReset Quote: 20.20 – 20.50
Spot Rate : 0.3000
Average : 0.1906

YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2025-01-31
Maturity Price : 25.00
Evaluated at bid price : 20.20
Bid-YTW : 6.97 %

BAM.PF.J FixedReset Quote: 25.20 – 25.56
Spot Rate : 0.3600
Average : 0.2524

YTW SCENARIO
Maturity Type : Call
Maturity Date : 2022-12-31
Maturity Price : 25.00
Evaluated at bid price : 25.20
Bid-YTW : 4.68 %

BNS.PR.D FloatingReset Quote: 22.93 – 23.19
Spot Rate : 0.2600
Average : 0.1748

YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2022-01-31
Maturity Price : 25.00
Evaluated at bid price : 22.93
Bid-YTW : 4.02 %

GWO.PR.Q Deemed-Retractible Quote: 24.41 – 24.65
Spot Rate : 0.2400
Average : 0.1578

YTW SCENARIO
Maturity Type : Hard Maturity
Maturity Date : 2025-01-31
Maturity Price : 25.00
Evaluated at bid price : 24.41
Bid-YTW : 5.61 %

Forward Interest Rates

Tuesday, January 17th, 2017

Forward interest rates have emerged as a bone of contention in the analysis of the proposed TransAlta preferred share exchange offer, so as part of the preparation for my promised weekend post, I’ll post a few links to some papers that illustrate why the Expectations Hypothesis cannot be used as a predictor.

Joseph R. Dziwura and Eric M. Green wrote a paper in 1996 for the New York Fed titled Interest Rate Expectations and the Shape of the Yield Curve:

According to the rational expectations hypothesis of the term structure (REHTS) long term rates should reflect market expectations for the average level of future short-term rates. The purpose of this paper is to examine whether REHTS assumptions conform to the term structure of outstanding U. S. Treasury securities from 1973 to 1995, and to examine the behavior of term premiums and to what extent they influence the shape of the forward curve. REHTS assumptions are re-examined using familiar regression tests to determine the forecast power of forward rates for subsequent spot rates, and we use excess holding period returns, the extra return earned on a security sold prior to maturity, as the ex poste measurement of the term premium. We find that forward rates explain only some of the variance in future spot rates, the forecast power of forward rates varies with maturity, and the term premia is time-varying. We decompose the forward rate into the current spot rate, a term premium, and an expected interest rate change, where the term premium is the sum of a risk premium and a convexity premium. We find that on average term premiums have contributed more to the shape of the forward curve than have expected rate changes, and find that expected and past interest rate volatility, as well as the slope of the yield curve, may provide information on the size of expected term premiums.

Another paper was by Massimo Guidolin and Daniel L. Thornton of the St. Louis Fed, titled Predictions of Short-Term Rates and the Expectations
Hypothesis
:

Despite its role in monetary policy and finance, the expectations hypothesis (EH) of the term structure of interest rates has received virtually no empirical support. The empirical failure of the EH has been attributed to a variety of econometric biases associated with the single-equation models most often used to test it; however, none of these explanations appears to account for the massives [sic] failure reported in the literature. We note that traditional tests of the EH are based on two assumptions—the EH per se and an assumption about the expectations generating process (EGP) for the short-term rate. Arguing that convential [sic] tests of the EH could reject it because the EGP embedded in these tests is significantly at odds with the true EGP, we investigate this possibility by analyzing the out-of-sample predictive prefromance [sic] of several models for predicting interest rates and a model that assumes the EH holds. Using standard methods that take into account parameter uncertainty, the null hypothesis of equal predictive accuracy of each models relative to the random walk alternative is never rejected.

One may hope their work is more reliable than their proof-reading!

Intuitive Analytics is a financial software firm which has published a blog-post by Peter Orr titled 50 Years of UST Yields – How Well do Forwards Predict? that was exactly what I was looking for:

As we’ve written on these pages before, forecasting is a necessary evil in finance. It’s uncertain by nature and of course the longer the horizon, the more difficult the job. The theory that forward rates are good predictors of future realized rates is called the expectations hypothesis and as one MIT professor put it, “If the attractiveness of an economic hypothesis is measured by the number of papers which statistically reject it, the expectations theory of the term structure is a knockout.”

For fun (and to dust off my fast fading coding skills) I went back and looked at how US Treasury implied forward 10Y rates have done in forecasting realized 10Y UST yields from July, 1959 to the present. We used first of month data for 3, 6 and 12 month Tbills as zero rates (making the appropriate daycount adjustments of course) and then 2, 3, 5, 7, 10, 20, and 30-year UST coupon instruments for our implied 10Y forward calculations. And this is what we get…

ust_10y_yields-resized-600
Click for Big

The red line is the actual 10Y yield over the period and the “hair” is the implied 10Y par yield 1, 2, 3, and 5 years forward. The way to read this then is to look at how often the hair tracks with the actual realization of the 10Y yields as shown by the red line. In general, during this single big rate cycle we’ve seen over the last 50 years, forward rates have badly underpredicted when rates were going up (note the implied decreasing 10Y forwards during the 70s) and then overpredicted over the last 30 or so years as rates have fallen. How badly do forwards do? Well over this 50 year span, and this holds over most subperiods as well, you’d be better off as a forecaster just assuming today’s yield curve stays constant i.e. a perfectly random walk.

Update, 2024-1-18: See also Predictions of Short-Term Rates and the Expectations Hypothesis of the Term Structure of Interest Rates, Massimo Guidolin & Daniel L. Thornton:

Despite its important role in monetary policy and finance, the expectations hypothesis (EH) of the term structure of interest rates has received virtually no empirical support. The empirical failure of the EH was attributed to a variety of econometric biases associated with the single-equation models used to test it; however, none account for it. Moreover, Sarno, Valente, and Thornton (2006) find that the EH is readily rejected using more powerful multi-equation Lagrange Multiplier test developed by Bekaert and Hodrick (2001). The ubiquitous rejection of the EH raise the possibility that its failure is fundamental rather than econometric. This paper analyzes the EH by focusing on its fundamental tenet—the predictability of the short-term rate. This is done by comparing h-month ahead forecasts for the 1-month Treasury yield implied by the EH with the forecasts from random-walk, Diebold and Lei (2003), and Duffee (2002) models. The evidence suggests that the failure of the EH is likely a consequence of market participants’ inability to predict the short-term rate.

The evidence suggests that the theoretical forecasts implied by the EH do not differ appreciably from the random walk or term structure forecasts. Moreover, it is shown that, just as the EH implies, long-term rates reflect significant information about the markets’ expectation for the short-term rate. That is, to the extent that the market is able to forecast the future short-term rate, long-term rates reflect that information. The difficultly arises from the fact that the observed short-term rates are dominated by new information that appears to be difficult to forecast. For this reason, the spread between the long-term and short-term rate is a relatively poor predictor of the future short-term rate.

Hence, while the EH is fundamentally correct—longer-term rates incorporate the markets’ expectation for the future short-term rate—its usefulness for financial market analysts and policymakers is doubtful. Of course, policymakers targeting short-term interest rates might increase the predictability of the rate spread by making short-term rates more predictable. Indeed, some recent evidence (e.g., Lange, et al., 2003; Poole, et al., 2002; and Watson, 2002) indicates that the predictability of the federal funds rate has increased since the Fed began announcing its funds rate target in 1994.