PrefBlog

The Bank of England has released its 2011 Quarterly Bulletin, filled with the usual high quality analysis.

In addition to the Markets and Operations review, there are articles on:

• Understanding the recent weakness in broad money growth
• Understanding labour force participation in the United Kingdom
• China’s changing growth pattern
• Summaries of recent Bank of England working papers
  • Wage rigidities in an estimated DSGE model of the UK labour market
  • The contractual approach to sovereign debt restructuring
  • Are EME indicators of vulnerability to financial crises decoupling from global factors?
  • Low interest rates and housing booms: the role of capital inflows, monetary policy
    and financial innovation

  • Mapping systemic risk in the international banking network
  • A Bayesian approach to optimal monetary policy with parameter and model uncertainty

In the United Kingdom, despite the reduction in sterling corporate bond spreads, the cost of corporate bond finance for investment-grade non-financial companies increased slightly, on account of the rise in government bond yields. An indicative measure of the nominal cost of equity finance for UK companies had also risen slightly (Chart 13).

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The Bank for International Settlements has released the March 2011 BIS Quarterly Review. The cover story is “Inflation pressures rise with commodity prices”, with special features:

• Systemic importance: some simple indicators
• Inflation expectations and the great recession
• The use of reserve requirements as a policy instrument in Latin America
• Foreign exchange trading in emerging currencies: more financial, more
  offshore

and the usual “Highlights of the BIS international statistics”.

There was a great table:

As of the end of the third quarter of 2010, the total consolidated foreign exposures (on an ultimate risk basis) of BIS reporting banks to Greece, Ireland, Portugal and Spain stood at $2,512 billion (Table 1). At $1,756 billion, foreign claims were equal to approximately 70% of that amount. The remaining $756 billion was accounted for by other exposures (ie the positive market value of derivatives contracts, guarantees extended and credit commitments).

The New York Fed has released a staff report by Paolo Angelini, Laurent Clerc, Vasco Cúrdia, Leonardo Gambacorta, Andrea Gerali, Alberto Locarno, Roberto Motto, Werner Roeger, Skander Van den Heuvel, and Jan Vlček titled BASEL III: Long-Term Impact on
Economic Performance and Fluctuations:

We assess the long-term economic impact of the new regulatory standards (the Basel III reform), answering the following questions: 1) What is the impact of the reform on long-term economic performance? 2) What is the impact of the reform on economic fluctuations? 3) What is the impact of the adoption of countercyclical capital buffers on economic fluctuations? The main results are the following: 1) Each percentage point increase in the capital ratio causes a median 0.09 percent decline in the level of steady-state output, relative to the baseline. The impact of the new liquidity regulation is of a similar order of magnitude, at 0.08 percent. This paper does not estimate the benefits of the new regulation in terms of reduced frequency and severity of financial crisis, analyzed in Basel Committee on Banking Supervision (2010b). 2) The reform should dampen output volatility; the magnitude of the effect is heterogeneous across models; the median effect is modest. 3) The adoption of countercyclical capital buffers could have a more sizable dampening effect on output volatility. These conclusions are fully consistent with those of reports by the Long-term Economic Impact Group (Basel Committee on Banking Supervision 2010b) and the Macroeconomic Assessment Group (2010b).

The results are also consistent with the Bank of Canada report (discussed in the post BoC Studies Capital Ratio Cost/Benefits, which stated:

The results of this analysis are reported in Table 4, which summarizes the results for both Canada and the LEI study of the long-run impact of tighter capital standards. The range of the results on long-run output loss for the Canadian economy is similar to that observed in the LEI report. The average is similar to the median of the international results—i.e., about 0.1 per cent of GDP for a 1-percentage-point rise in bank capital requirements. Note that a high degree of uncertainty accompanies the calculation of long-run estimates, as evidenced by the wide range of model outcomes. Thus, the focus is on the median results of the Canadian models.

The Bank of Canada has released its Winter 2010/2011 Review with articles:

• Competition in the Canadian Mortgage Market
• Adverse Selection and Financial Crises
• Payment Networks: A Review of Recent Research
• Conference Summary: Financial Globalization and Financial Instability

The second article, Adverse Selection and Financial Crises by Koralai Kirabaeva has an interesting chart:

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The market for subprime mortgages was relatively small, comprising only about 25 per cent of the outstanding amount in the US$6 trillion mortgage-backed securities (MBS) market and about 30 per cent of total nonagency MBS issuance in the years before the crisis (Gorton 2008b). Direct losses from household defaults on subprime mortgages are estimated to be about US$500 billion, but the subprime crisis triggered losses in the U.S. stock market that reached US$8 trillion in October 2008 (Brunnermeier 2009).

In explaining the disproportionate effect of the subprime-mortgage crisis on the financial system, one can identify a number of amplification mechanisms that can significantly increase the initial impact of adverse selection: an increase in uncertainty about asset values, a flight to liquidity, and a misassessment of systemic risk. Increasing uncertainty about asset values contributes to the decline in demand for these assets, while a flight to liquidity and an underestimation of systemic risk cause a shortage of liquid assets in the market.

Direct loss estimates during the crisis ranged from $175-billion to $565-billion. It’s a pity Kirabaeva didn’t footnote his $500-billion figure. The Brunnermeier paper confines itself to “several hundred billion dollars”.

And I am still waiting for somebody, anybody, to estimate how much of these losses were borne by the senior (AAA) tranches of securitized subprime, that (although subjected to very major credit rating downgrades) may well have passed through the cataclysm with only minor losses.

The higher preference for liquid assets during a crisis can be viewed as precautionary liquidity hoarding because of a tightening in funding liquidity. A higher preference for liquidity may alleviate the problem of adverse selection, since assets are more likely to be sold because the seller needs to raise liquidity rather than because of an asset’s low quality. Nevertheless, a higher demand for liquid assets also implies a lower demand for illiquid assets. If the demand for illiquid assets is sufficiently low, then the asset’s price will be determined by the liquidity available in the market rather than by the expected return on the asset (Allen and Gale 2004). Hence, an increase in liquidity preference can lead to fire-sale pricing and possibly to a market freeze.

Government intervention during crises may create a moral hazard problem: if market participants anticipate such interventions, then their optimal holdings of risky assets are larger. Government bailouts (debt guarantees) can be inevitable during crises, and as a result, they lead to the inefficient allocation of capital towards risky investments. The pre-emptive policy response is an ex-ante requirement for larger holdings of safe assets (e.g., capital requirements), which offsets systemic externalities and reduces the probability of market breakdowns during crises (Kirabaeva 2010).

I am disappointed to see that there is no discussion of the possibility that a better policy response might be the provision of liquidity at a penalty rate.

Update: The mortgage article made the Financial Post, in a piece by John Greenwood titled Why do mortgage rates rise fast, fall slowly?

The Bank of Canada has released a working paper by George J. Jiang and Ingrid Lo titled Private Information Flow and Price Discovery in the U.S. Treasury Market:

Existing studies show that U.S. Treasury bond price changes are mainly driven by public information shocks, as manifested in macroeconomic news announcements and events. The literature also shows that heterogeneous private information contributes significantly to price discovery for U.S. Treasury securities. In this paper, we use high frequency transaction data for 2-, 5-, and 10-year Treasury notes and employ a Markov switching model to identify intraday private information flow in the U.S. Treasury market. We show that the probability of private information flow (PPIF) identified in our model effectively captures permanent price effects in U.S. Treasury securities. In addition, our results show that public information shocks and heterogeneous private information are the main factors of bond price discovery on announcement days, whereas private information and liquidity shocks play more important roles in bond price variation on non-announcement days. Most interestingly, our results show that the role of heterogeneous private information is more prominent when public information shocks are either high or low. Furthermore, we show that heterogeneous private information flow is followed by low trading volume, low total market depth and hidden depth. The pattern is more pronounced on non-announcement days.

I have often made the point that the reverence shown by the media for traders is misplaced. They don’t make huge profits by keen analysis; they only need to be smart enough to sell at higher prices than they buy. The paper describes how this works:

One challenge of our study is that compared to public information flow in the Treasury market, which generally coincides with news announcements, private information flow is not directly observed. In this paper, we use the impact of order flow on bond prices to infer private information flow. For example, Brandt and Kajeck (2004) argue that order flow impact effectively captures heterogeneous information flow in the U.S. Treasury market. Empirically, Green (2004), Pasquariello and Vega (2007) use order flow impact to proxy for the level of information asymmetry on announcement versus nonannouncement days in the Treasury market. Loke and Onayev (2007) also find state-varying level of order flow impact in the S&P futures market. Using information from order flow impact, we specify a Markov switching model to identify private information flow. Using high frequency transaction data for the 2-, 5-, and 10-year Treasury notes, we obtain 5-minute estimates of the probability of private information flow (PPIF hereafter). In this aspect, our model can be viewed as an extension of the existing PIN model by Easley et al. (2002) and Li et al. (2009).

The data used in our study is obtained from the BrokerTec electronic limit order book platform on which secondary interdealer trading occurs. It contains not only tick-by-tick information on transaction and market quotes but also information of the entire limit order book for the on-the-run 2-year, 5-year, and 10-year notes. This allows us to examine the effect of heterogeneous private information in high frequency. The detailed information on the limit order book also allows us to examine how liquidity dynamics interact with private information. A novel aspect of our paper is that we examine how liquidity reacts to information uncertainty. Given that the timing and the context of information arrival is unknown on non-announcement days, we look at how trading activities and placement of limit orders differs from that on announcement days. Data on announcements comes from Bloomberg and includes date, time and values for expected and actual announcements. Since surveys of market participants provide ex ante expectations of major economic announcements, measures of announcement surprises or unexpected information shocks can be constructed.

Our results show that PPIF is higher for longer maturity bonds, and higher on announcement days than on non-announcement days. The finding is consistent with Brandt and Kavajecz (2004) that price discovery manifests in less liquid markets. In addition, on announcement days, PPIF coincides with public information shocks as measured by announcement times.This is consistent with Green(2004) finding that the role of private information is hihger [sic] at and after announcements.

If you’re a trader and do this, you’re smart. But if you create a computerized expert system to do this, you’re an evil High-Frequency Trader.

Jaksa Cvitanic, Andrei A. Kirilenko have published a paper titled High Frequency Traders and Asset Prices:

Do high frequency traders affect transaction prices? In this paper we derive distributions of transaction prices in limit order markets populated by low frequency traders (humans) before and after the entrance of a high frequency trader (machine). We find that the presence of a machine is likely to change the average transaction price, even in the absence of new information. We also find that in a market with a high frequency trader, the distribution of transaction prices has more mass around the center and thinner far tails. With a machine, mean intertrade duration decreases in proportion to the increase in the ratio of the human order arrival rates with and without the presence of the machine; trading volume goes up by the same rate. We show that the machine makes positive expected profits by “sniping” out human orders somewhat away from the front of the book. This explains the shape of the transaction price density. In fact, we show that in a special case, the faster humans submit and vary their orders, the more profits the machine makes.

They specify:

Machines are assumed to be strategic uninformed liquidity providers. They have only one advantage over the humans – the speed with which they can submit or cancel their orders. Because of this advantage, machines dominate the trading within each period by undercutting slow humans at the front of the book. This is only one of the strategies used by actual high-frequency traders in real markets, and the only one we focus on.[footnote] In the language of the industry, machines aim to “pick-off” or “snipe out” incoming human orders.

Footnote: Other known high frequency trading strategies include (i) the collection of rebates offered by exchanges for liquidity provision, (ii) cross-market arbitrage, and (iii) “spoofing”- triggering other traders to act.

The guts of the paper is:

We find that the presence of a machine is likely to change the average transaction price, even in the absence of new information. We also find that in the presence of a machine, the shape of the transaction price density remains the same in the middle, between the bid and the ask of the machine, the far tails of the density get thinner, while the parts of the tails closer to the bid and the ask of the machine get fatter. In the presence of the machine, mean intertrade duration decreases in proportion to the increase in the ratio of the human order arrival rates with and without the presence of the machine. Trading volume goes up by the same rate. In other words, if the humans submit orders ten times faster when the machine is present, intertrade duration falls and trading volume increases by a factor of ten.

Second, we compute the optimal bid and ask prices for the machine that optimizes expected profits subject to an inventory constraint. The inventory constraint prevents the machine from carrying a significant open position to the next intra-human-trade period. The optimal bid and the ask for the machine are close to being symmetric around the mean value of the human orders, with the distance from the middle value being determined by the inventory constraint – the less concerned the machine is about the size of the remaining inventory, the closer its bid and the ask prices are to each other. The expected profit of an optimizing machine is increasing in both the variance and the arrival frequency of human orders.

Our two findings are interrelated; one the one hand, an optimizing machine is able to make positive expected profits by “sniping” out human orders somewhat away from the front of the book. On the other hand, execution of the “sniping” order submission strategy results in a transaction price density with bulges near the front and thinner outer tails. In fact, in a special case, the faster humans submit and vary their orders, the more profits the machine makes.

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The conclusion of interest is:

We also find that a machine that optimizes expected profits subject to an inventory constraint submits orders that are essentially symmetric around the mean value of the human orders. The distance between the machine’s bid and ask prices increases with its concern about the size of the remaining inventory. The expected profit of an optimizing machine increases in both the variance and the arrival frequency of human orders.

Briefly mentioned on October 18 was High Frequency Trading and its Impact on Market Quality:

In this paper I examine the impact of high frequency trading (HFT) on the U.S. equities market. I analyze a unique dataset to study the strategies utilized by high frequency traders (HFTs), their profitability, and their relationship with characteristics of the overall market, including liquidity, price discovery, and volatility. The 26 HFT firms in the dataset participate in 68.5% of the dollar-volume traded. I find the following key results: (1) HFTs tend to follow a price reversal strategy driven by order imbalances, (2) HFTs earn gross trading profits of approximately $2.8 billion annually, (3) HFTs do not seem to systematically engage in a non-HFTr anticipatory trading strategy, (4) HFTs’ strategies are more correlated with each other than are non-HFTs’, (5) HFTs’ trading levels change only moderately as volatility increases, (6) HFTs add substantially to the price discovery process, (7) HFTs provide the best bid and offer quotes for a significant portion of the trading day and do so strategically so as to avoid informed traders, but provide only one-fourth as much book depth as non-HFTs, and (8) HFTs may dampen intraday volatility. These findings suggest that HFTs’ activities are not detrimental to non-HFTs and that HFT tends to improve market quality.

He provides a good discussion of pinging:

Pinging is defined by the SEC as, “an immediate-or-cancel order that can be used to search for and access all types of undisplayed liquidity, including dark pools and undisplayed order types at exchanges and ECNs. The trading center that receives an immediate-or-cancel order will execute the order immediately if it has available liquidity at or better than the limit price of the order and otherwise will immediately respond to the order with a cancelation” (SEC, January 14, 2010). The SEC goes on to clarify, “[T]here is an important distinction between using tools such as pinging orders as part of a normal search for liquidity with which to trade and using such tools to detect and trade in front of large trading interest as part of an ‘order anticipation’ trading strategy” (SEC, January 14, 2010).

Of interest is the section titled “10-Second High Frequency Trading Determinants”:

This section examines the factors that influence HFTs’ buy and sell decisions. I begin by testing a variety of potentially important variables in an ordered logistic regression analysis. The results show the importance of past returns. I carry out a logistic regression analysis distinguishing the dependent variables based on whether the HFTr is buying or selling and whether the HFTr is providing liquidity or taking liquidity. Finally, I include order imbalance in the logit analysis and find that the interaction between past order imbalance and past returns drives HFTr activity and is consistent with HFTs engaging in a short term price reversal strategy.

Also of interest is the section titled “Testing Whether High Frequency Traders Systematically Engage in Anticipatory Trading”:

In this section I test whether HFTs systematically anticipate and trade in front of non-HFTs (“anticipatory trading”) (SEC, January 14, 2010). It may be that HFTs predict and buy (sell) a stock just prior to when a non-HFTr buys (sells) the stock. If this is the case, HFTs are profiting at the expense of non-HFTs.[footnote]

Footnote: Anticipatory trading is not itself an illegal activity. It is illegal when a firm has a fiduciary obligation to its client and uses the client’s information to front run its orders. In my analysis, as HFTs are propriety trading firms, they do not have clients and so the anticipatory trading they may be conducting would likely not be illegal. Where HFT and anticipatory trading may be problematic is if market manipulation is occurring that is used to detect orders. It may be the case that “detecting” orders would fall in to the same category of behavior as that resulted in a $2.3 million fine to Trillium Brokerage Services for “layering”.

Trillium was fined for the following layering strategy: Suppose Trillium wanted to buy stock X at $20.10 but the current offer price was $20.13, Trillium would put in a hidden buy order at $20.10 and then place several limit orders to sell where the limit orders were sufficiently below the bid price to be executed. Market participants would see this new influx of sell orders, update their priors, and lower their bid and offer prices. Once the offer price went to $20.10, Trillium’s hidden order would execute and Trillium would then withdraw its sell limit orders. FINRA found this violated NASD Rules 2110, 2120, 3310, and IM-3310 (now FINRA 2010, FINRA 2020, FINRA 5210, and also part of FINRA 5210).

I don’t have any problems with what Trillium did – it’s just another example of random illegality. By me, that just shows that most traders are little girls with no conception whatsoever of fundamental value and FINRA panders to them. FINRA’s press release states … :

In concluding this settlement, Trillium and the individual respondents neither admitted nor denied the charges, but consented to the entry of FINRA’s findings.

… from which I conclude that FINRA doesn’t think it was a crime either and the whole thing was simply a case of regulatory extortion.

Of great interest is the author’s estimate of the effect of HFT on market impact:

Figure 4: Time Series of High Frequency Traders’ and Non High Frequency Traders’ Book Depth. This Figure analyzes the depth of the order book and how much depth different types of traders provide by analyzing the price impact of a 1000 share trade hitting the order book with and without different types of traders. There are three graphs. The first, Price Impact of a 1000 Share Trade, examines the total price impact a 1000 share trade would have with all available liquidity accessible. The second graph, Additional Price Impact without HFTs on the Book, depicts the additional price impact that would occur from removing HFTs’ limit orders The third graph, Additional Price Impact without non-HFTs on the Book, graphs the additional price impact from removing non-HFTs’ limit orders. The daily dollar price impact value is calculated giving equal weight to each stock. The order book data is available during 10 5-day windows. The X-axis identifies the first day in the 5-day window. That is, The observation 01-07-08 is followed by observations on January 8th, 9th, 10th, and 11th of 2008. The next observation is for April 7, 2008 and is followed by the next four consecutive trading days. To separate the 5-day windows I enter a zero-impact trade, creating the evenly spaced troughs.

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Also of interest was the effect of the short-selling ban on market participation by HFT:

Figure A-1: High Frequency Trading’s Fraction of the Market around the Short-Sale Ban. The figure shows how HFTs’ fraction of dollar-volume trading varied surrounding the September 19, 2008 SEC imposed short-sale ban on many financial stocks. In the HFT dataset, 13 stocks are in the ban. The two graphs plot HFTs’ fraction of dollar-volume traded for the banned stocks and for the unaffected stocks. The first graph reports the fraction of dollar-volume where HFTs supplied liquidity. The second reports the fraction where HFTs demanded liquidity. I normalize the banned stocks’ percent of the market in both graphs so that the affected and unaffected stocks have the same percent of the market on September 2, 2008. The two vertical lines represent the first and last day of the short-sale ban.

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In a working paper published by the Federal Reserve Bank of New York, authored by Tobias Adrian & Hao Wu and titled The Term Structure of Inflation Expectations:

We present estimates of the term structure of inflation expectations, derived from an affine model of real and nominal yield curves. The model features stochastic covariation of inflation with the real pricing kernel, enabling us to extract a time-varying inflation risk premium. We fit the model not only to yields, but also to the yields’ variance-covariance matrix, thus increasing identification power. We find that model-implied inflation expectations can differ substantially from break-even inflation rates when market volatility is high. Our model’s ability to be updated weekly makes it suitable for real-time monetary policy analysis.

They point out:

However, breakeven inflation rates of zero-coupon off-the-run curves (i.e., implied inflation) still are not pure measures of inflation expectations. This is because the absence of arbitrage implies that the difference between zero-coupon nominal and real yields can be decomposed into three components:

Breakeven Inflation = Expected Inflation + Inflation Risk Premium + Convexity

The literature commonly adjust for the convexity effect (see Elsasser and Sack, 2004). However, the adjustment of breakeven inflation for the inflation risk premium requires the estimation of a term structure model.

The paper by Brian P. Sack and Robert Elsasser is titled Treasury Inflation-Indexed Debt: A Review of the U.S. Experience. The convexity complication is explained as:

One complication with interpreting inflation compensation involves the adjustment for convexity. For a given level of the yield on a nominal security, uncertainty about future inflation increases the expected return on that security. This is a mechanical relationship that arises from the convexity of real returns in inflation—specifically, because higher inflation erodes the real return on the security at a slower rate than lower inflation boosts it (see the appendix). This point was originally made by Fischer (1975); a more recent description of the relationship between inflation compensation and future inflation can be found in McCulloch and Kochin (1998).

Although convexity tends to pull down inflation compensation relative to expected inflation, there may be an inflation risk premium that works in the opposite direction.

The methodology of Adrian & Wu is:

In this paper, we develop an affine term structure model that captures the dynamics of real and nominal yields curves, as well as the evolution of their variance-covariance matrix. This is important, as the inflation risk premium is proportional to the conditional covariance of the real pricing kernel and inflation. In order to increase the power for identifying the inflation risk premium, we match both the term structure of the yield curves, and the term structure of variances and covariances.

We find a relatively small and stable inflation risk premium. The order of magnitude of the inflation risk premium is comparable to other recent estimates in studies that use inflation protected bonds over similar sample periods, but it is smaller and less variable than estimates that use nominal bonds and inflation over longer time periods (see Buraschi and Jiltsov, 2005, and Ang, Bekaert, and Wei, 2006).

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The Federal Reserve Bank of San Francisco has published a working paper by Jens H. E. Christensen, Jose A. Lopez and Glenn D. Rudebusch titled Inflation Expectations and Risk Premiums in an Arbitrage-Free Model of Nominal and Real Bond Yields:

Differences between yields on comparable-maturity U.S. Treasury nominal and real debt, the so-called breakeven inflation (BEI) rates, are widely used indicators of inflation expectations. However, better measures of inflation expectations could be obtained by subtracting inflation risk premiums from the BEI rates. We provide such decompositions using an estimated affine arbitrage-free model of the term structure that captures the pricing of both nominal and real Treasury securities. Our empirical results suggest that long-term inflation expectations have been well anchored over the past few years, and inflation risk premiums, although volatile, have been close to zero on average.

This contradicts the results of Haubrich, et al, from the FRB-Cleveland, (paper reviewed on PrefBlog) who claimed:

The inflation risk premium on a ten-year bond varied between 38 and 60 basis points during our [1982-2008] sample period.

while the FRBNY paper by Adrian & Wu (discussed below) claimed a 0-40bp Inflation Risk Premium for 5-10 year time horizons.

The Christensen group first did a Principal Component Analysis of Treasury yields:

Researchers have typically found that three factors, often referred to as level, slope, and curvature, are sufficient to account for the time variation in the cross section of nominal Treasury yields (e.g., Litterman and Scheinkman, 1991). This characterization is supported by a principal component analysis of our weekly data set, which consists of Friday observations from January 6, 1995, to March 28, 2008, for eight maturities: three months, six months, one year, two years, three years, five years, seven years, and ten years. Indeed, as shown in Table 1, 99.9 percent of the total variation in this set of yields is accounted for by the first three principal components. Furthermore, the loadings across the eight maturities for the first component are quite uniform; thus, like a level factor, a shock to this component will change all yields by a similar amount. The second component has negative loadings for short maturities and positive loadings for long ones; thus, like a slope factor, a shock to this component will steepen or flatten the yield curve. Finally, the third component has U-shaped factor loadings as a function of maturity and is naturally interpreted as a curvature factor.

Only the first two principal components are required to explain real yields – best of all, the “slope” component is well-correlated with that of nominals:

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After developing the model, they conclude:

Finally, for our preferred specification, we subtract each model-implied expected inflation rate from the comparable-maturity model-implied BEI rate and obtain the associated inflation risk premium (IRP). At both the five- and ten-year horizons, these premiums are fairly small, as shown in Figure 5.(17) Indeed, during our sample, these inflation premiums have varied in a range around zero of about ±50 basis points.(18)

Footnote 17: This result provides some support for the argument that the gain to the U.S. Treasury from issuing TIPS bonds instead of nominal bonds may be quite limited, as argued in Sack and Elsasser (2004).

Footnote 18: Again, in theory, the sign of the inflation risk premium depends on the covariance between the real stochastic discount factor and inflation, but there are real-world considerations as well. For example, a liquidity premium for holding TIPS instead of nominal Treasury bonds would show up as a negative inflation risk premium.

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Finally, the authors conclude:

This paper estimates an arbitrage-free model with four latent factors that can capture the dynamics of both the nominal and real Treasury yield curves well and can decompose BEI rates into inflation expectations and inflation risk premiums. The model-implied measures of inflation expectations are correlated closely with survey measures, while the estimated inflation risk premiums fluctuate in fairly close range around zero. The empirical results suggest that long-term inflation expectations have been well-anchored in the period from year-end 2002 through the first quarter of 2008.

In a working paper published by the Bank for International Settlements, authored by Peter Hördahl and Oreste Tristani, titled Inflation risk premia in the US and the euro area:

We use a joint model of macroeconomic and term structure dynamics to estimate inflation risk premia in the United States and the euro area. To sharpen our estimation, we include in the information set macro data and survey data on inflation and interest rate expectations at various future horizons, as well as term structure data from both nominal and index-linked bonds. Our results show that, in both currency areas, inflation risk premia are relatively small, positive, and increasing in maturity. The cyclical dynamics of long-term inflation risk premia are mostly associated with changes in output gaps, while their high-frequency fluctuations seem to be aligned with variations in inflation. However, the cyclicality of inflation premia differs between the US and the euro area. Long term inflation premia are countercyclical in the euro area, while they are procyclical in the US.

This model is much more macro-oriented than most:

In this paper we focus on modelling and estimating the first of these two components – i.e. the inflation risk premium – in order to obtain a “cleaner” measure of investors; inflation expectations embedded in bond prices. In doing so, we try to reduce the risk that liquidity factors might distort our estimates by carefully choosing when to introduce yields on index-linked bonds in the estimations. We also include survey information on expectations, which should aid us in pinning down the dynamics of key variables in the model. Moreover, in order to understand the macroeconomic determinants of inflation risk premia we employ a joint model of macroeconomic and term structure dynamics, such that prices of real and nominal bonds are determined by the macroeconomic framework and investors’ risk characteristics. More specifically, building on Ang and Piazzesi (2003), we adopt the framework developed in Hördahl, Tristani and Vestin (2006), in which bonds are priced based on the dynamics of the short rate obtained from the solution of a linear forward-looking macro model and using an essentially a¢ ne stochastic discount factor (see Duffie and Kan, 1996; Dai and Singleton, 2000; Duffee, 2002).

They find a rather peculiar difference in the responses of EUR denominated bonds vs Treasuries to output-gap shocks:

There is however one striking di¤erence in the conditional dynamics of risk premia in the two currency areas. While we find that inflation premia always respond positively to upward inflation shocks, the response to output gap shocks di¤er between the US and the euro area. A positive output shock results in a higher inflation premium in the US, while it lowers it in the euro area. The positive relationship for the US could reflect perceptions of a higher risk of inflation surprises on the upside as the output gap widens, while the euro area result is consistent with investors becoming more willing to take on risks – including inflation risks – during booms, while they may require larger premia during recessions.

The model’s detailed results are:

Looking at the results in more detail, Figure 9 shows that a one standard deviation upward shock to the output gap (about 0.4%) in the US pushes the 10-year break-even rate up by around 15 basis points on impact. About two thirds of this e¤ect is due to a rising inflation premium, while one third corresponds to an increase in expected inflation as a result of the expansionary shock. At the 2-year horizon, the effect on the break-even rate is even larger, at around 26 basis points on impact, but now the bulk of this response is due to rising inflation expectations (16 basis points), whereas the inflation premium accounts for the remaining 10 basis points. Hence, a shock to the output gap seems to result in a parallel shift in the inflation premium, while inflation expectations react much more strongly for short horizons than long, reflecting the short- to medium-term persistence of output gap shocks. In the euro area, a positive shock to the output gap also raises expected inflation – and more so at the 2-year horizon than the 10-year horizon – but the inflation premium response is uniformly negative (Fig. 11). Moreover, as the expected inflation response declines with the horizon, the break-even response goes from being positive at the 2-year horizon to being slightly negative at 10 years.

The authors conclude, in part:

Our results show that the inflation risk premium is relatively small, positive, and increasing with the maturity, in the United States as well as in the euro area. Our estimated inflation premia vary over time as a result of changes to the state variables in the model. Specifically, in both economies the output gap and inflation are the main drivers of inflation premia. The broad movements in long-term inflation risk premia largely match those of the output gap, while more high-frequency premia fluctuations seem to be aligned with changes in the level of inflation.

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The Federal Reserve Board of St. Louis has published an article by Richard G. Anderson, Charles S. Gascon, and Yang Liu titled Doubling Your Monetary Base and Surviving: Some International Experience:

The authors examine the experience of selected central banks that have used large-scale balancesheet expansion, frequently referred to as “quantitative easing,” as a monetary policy instrument. The case studies focus on central banks responding to the recent financial crisis and Nordic central banks during the banking crises of the 1990s; others are provided for comparison purposes. The authors conclude that large-scale balance-sheet increases are a viable monetary policy tool provided the public believes the increase will be appropriately reversed.

The authors review current and past examples of central bank balance sheet expansion and conclude:

During the past two decades, large increases — and decreases — in central bank balance sheets have become a viable monetary policy tool. Historically, doubling or tripling a country’s monetary base was a recipe for certain higher inflation. Often such increases occurred only as part of a failed fiscal policy or, perhaps, as part of a policy to defend the exchange rate. Both economic models and central bank experience during the past two decades suggest that such changes are useful policy tools if the public understands the increase is temporary and if the central bank has some credibility with respect to desiring a low, stable rate of inflation. We find little increased inflation impact from such expansions.

For monetary policy, our study suggests several findings:

• (i) A large increase in a nation’s balance sheet over a short time can be stimulative.
• (ii) The reasons for the action should be communicated. Inflation expectations do not move if households and firms understand the reason(s) for policy actions so long as the central bank can credibly commit to unwinding the expansion when appropriate.
• (iii) The type of assets purchased matters less than the balance-sheet expansion.
• (iv) When the crisis has passed, the balance sheet should be unwound promptly.

Econbrowser’s James Hamilton has presented a review of QE2 and concludes:

I agree with John that the primary effects of QE2 come from restructuring the maturity of government debt, and that any effects one claims for such a move are necessarily modest. But unlike John, I believe those modest effects are potentially helpful.

Just to reiterate, my position is that when you combine the Fed’s actions with the Treasury’s, the net effect has been a lengthening rather than shortening of the maturity structure:

given the modest size, pace, and focus of QE2, and given the size and pace at which the Treasury has been issuing long-term debt, the announced QE2 would have been associated with a move in the maturity structure of the opposite direction from that analyzed in our original research. The effects of the combined actions by the Treasury and the Fed would be to increase rather than decrease long-term interest rates.

He has also noted the effects on commodity prices:

I feel that there is a pretty strong case for interpreting the recent surge in commodity prices as a monetary phenomenon. Now that we know there’s a response when the Fed pushes the QE pedal, the question is how far to go.

My view has been that the Fed needs to prevent a repeat of Japan’s deflationary experience of the 1990s, but that it also needs to watch commodity prices as an early indicator that it’s gone far enough in that objective. In terms of concrete advice, I would worry about the potential for the policy to do more harm than good if it results in the price of oil moving above $90 a barrel.

And we’re uncomfortably close to that point already.

Oil is now over USD 90/bbl.

Another effect I haven’t seen discussed much is a reversal of crowding-out:

Company bond sales in the U.S. reached a record this week and relative yields on investment- grade debt shrank to the narrowest since May as money managers boosted bets economic growth is gaining momentum.

Issuance soared to $48.5 billion, eclipsing the $46.9 billion raised in the week ended May 8, 2009, as General Electric Co.’s finance unit sold $6 billion of notes in the largest offering in 11 months, according to data compiled by Bloomberg. Investment-grade bond spreads narrowed to 162 basis points, or 1.62 percentage points, more than Treasuries, Bank of America Merrill Lynch index data show.

Appetite for corporate debt is growing after annual sales topped $1 trillion for the second consecutive year as the securities return more than Treasuries.
…
Foreign borrowers dominated U.S. sales this week, with companies from Sydney-based Macquarie Group Ltd. to the U.K.’s Barclays Plc accounting for 57 percent of the total, Bloomberg data show.
…
“The expectation coming into this year was that Yankee issuance would be heavy,” said Jim Probert, managing director and head of investment grade capital markets at Bank of America Merrill Lynch. “There’s enough maturing debt coming out of European financials in particular that they needed to be in the marketplace, and right now, U.S. dollars is a good alternative, in addition to euros.”

Meanwhile, Janet L. Yellen, the Fed’s Vice-Chair, has delivered a speech titled The Federal Reserve’s Asset Purchase Program:

As inflation has trended downward, measures of underlying inflation have fallen somewhat below the levels of about 2 percent or a bit less that most Committee participants judge to be consistent, over the longer run, with the FOMC’s dual mandate. In particular, a modest positive rate of inflation over time allows for a slightly higher average level of nominal interest rates, thereby creating more scope for the FOMC to respond to adverse shocks. A modest positive inflation rate also reduces the risk that such shocks could result in deflation, which can be associated with poor macroeconomic performance.
…
Figure 3 depicts the results of such a simulation exercise, as reported in a recent research paper by four Federal Reserve System economists. For illustrative purposes, the simulation imposes the assumption that the purchases of $600 billion in longer-term Treasury securities are completed within about a year, that the elevated level of securities holdings is then maintained for about two years, and that the asset position is then unwound linearly over the following five years.

This trajectory of securities holdings causes the 10-year Treasury yield to decline initially about 1/4 percentage point and then gradually return toward baseline over subsequent years. That path of longer-term Treasury yields leads to a significant pickup in real gross domestic product (GDP) growth relative to baseline and generates an increase in nonfarm payroll employment that amounts to roughly 700,000 jobs.
…
Inflation and bank reserves. A second reason that some observers worry that the Fed’s asset purchase programs could raise inflation is that these programs have increased the quantity of bank reserves far above pre-crisis levels. I strongly agree with one aspect of this argument–the notion that an accommodative monetary policy left in place too long can cause inflation to rise to undesirable levels. This notion would be true regardless of the level of bank reserves and pertains as well in situations in which monetary policy is unconstrained by the zero bound on interest rates. Indeed, it is one reason why the Committee stated that it will review its asset purchase program regularly in light of incoming information and adjust the program as needed to meet its objectives. We recognize that the FOMC must withdraw monetary stimulus once the recovery has taken hold and the economy is improving at a healthy pace. Importantly, the Committee remains unwaveringly committed to price stability and does not seek inflation above the level of 2 percent or a bit less than that, which most FOMC participants see as consistent with the Federal Reserve’s mandate.

The research paper referenced in conjunction with Figure 3 is Have We Underestimated the Likelihood and Severity of Zero Lower Bound Events? by Hess Chung, Jean-Philippe Laforte, David Reifschneider and John C. Williams:

Before the recent recession, the consensus among researchers was that the zero lower bound (ZLB) probably would not pose a significant problem for monetary policy as long as a central bank aimed for an inflation rate of about 2 percent; some have even argued that an appreciably lower target inflation rate would pose no problems. This paper reexamines this consensus in the wake of the financial crisis, which has seen policy rates at their effective lower bound for more than two years in the United States and Japan and near zero in many other countries. We conduct our analysis using a set of structural and time series statistical models. We find that the decline in economic activity and interest rates in the United States has generally been well outside forecast confidence bands of many empirical macroeconomic models. In contrast, the decline in inflation has been less surprising. We identify a number of factors that help to account for the degree to which models were surprised by recent events. First, uncertainty about model parameters and latent variables, which were typically ignored in past research, significantly increases the probability of hitting the ZLB. Second, models that are based primarily on the Great Moderation period severely understate the incidence and severity of ZLB events. Third, the propagation mechanisms and shocks embedded in standard DSGE models appear to be insufficient to generate sustained periods of policy being stuck at the ZLB, such as we now observe. We conclude that past estimates of the incidence and effects of the ZLB were too low and suggest a need for a general reexamination of the empirical adequacy of standard models. In addition to this statistical analysis, we show that the ZLB probably had a first-order impact on macroeconomic outcomes in the United States. Finally, we analyze the use of asset purchases as an alternative monetary policy tool when short-term interest rates are constrained by the ZLB, and find that the Federal Reserve’s asset purchases have been effective at mitigating the economic costs of the ZLB. In particular, model simulations indicate that the past and projected expansion of the Federal Reserve’s securities holdings since late 2008 will lower the unemployment rate, relative to what it would have been absent the purchases, by 1½ percentage points by 2012. In addition, we find that the asset purchases have probably prevented the U.S. economy from falling into deflation.