In this section we return to the transparency of financial markets, which was introduced in Chapter 1. The degree of transparency is relevant because it influences traders’ strategies, hence the pricing process. However, given the great variety of aspects, assessing the effects of pre-trade transparency on market quality is complicated, so it is not surprising that the results offered by the literature differ significantly depending on the market structure considered and the type of information revealed.

If one models transparency as the increased visibility of the liquidity suppliers’ order flows, the effects of pre-trade transparency on market quality will show the benefits of the reduction in adverse selection costs for liquidity and uninformed traders’ welfare.

Clearly, when liquidity suppliers can screen informed and uninformed traders, they can also offer liquidity on better terms to the uninformed. If, however, pre-trade transparency is modelled as the visibility of traders’ identification codes, the effects on market quality can differ, as has been shown by Foucault, Moinas and Theissen (2007) and Rindi (2008). Finally, an often-debated question is the relationship between clients and intermediaries, given that the latter enjoy privileged information on the motivation for their clients’ trades and can exploit this by acting as a counterpart in the trades (dual capacity trading (Röell, 1990) or trading before the clients (front running)).

As the previous section has shown, some price effects of trading are temporary, and in an efficient market, in the long run, prices are expected to return to their fundamental values. However, the equilibrium price itself is not a fixed value, but moves with public news and information revealed by the trading process. In this section, we develop models that separate the equilibrium price dynamics from short-term price fluctuations around the equilibrium. Such models can be used, for example, to assess the speed of convergence to the equilibrium after a shock (e.g. a large transaction). The variance of the short-term deviations from the equilibrium price are a measure of market liquidity.

Finally, in a setting where one asset is traded on several different markets (fragmented trading), the models can be used to assess how informative the trading process is in each market (the contribution to price discovery); this last topic will be dealt with in section 9.4.

We discussed methods for estimating the information content of trades and the effect of inventory control on prices. The models that we discussed generally assume that only one of the effects (information or inventory control) is present. However, the empirical predictions of the asymmetric information and inventory control market microstructure models are very similar. Both theories predict that prices will move in the direction of the trade: a buyer-initiated order increases bid and ask prices, whereas a seller-initiated trade decreases bid and ask prices. This makes it difficult to distinguish the two theories empirically, unless good data on inventories are available. However, this is rarely the case.

Fortunately, in the absence of inventory data there is another way to separate information effects from inventory effects. Theoretically, information effects arise because trades reflect new information. This information will be incorporated in the price. If the market is efficient, the impact will be immediate and permanent. In contrast, inventory effects arise due to liquidity providers’ inventory imbalances. If the liquidity providers actively manage their inventory, these imbalances will be temporary. As a direct consequence, the price effect of trades will also be temporary and will reverse in the future. This gives a handle to distinguish information from inventory control effects:
information has permanent price effects whereas inventory effects are transitory.

In the foregoing articles we have seen that in the short run, the prices of financial instruments may deviate from their fundamental value on account of microstructure frictions such as bid–ask bounce, inventory control and order imbalances. Previous article introduced empirical models for estimating transaction costs and the price impact of a trade. These models were quite simple: they assumed that the price impact of a trade was immediate. In reality, this is not always so, and there may be lagged effects or slow adjustments. We therefore need a richer dynamic structure in order to model prices and trades on financial markets. In this chapter, we introduce dynamic timeseries models for prices and trades, and show how they can be used to describe the market’s convergence on the new equilibrium price after a shock.

This article extends the simple empirical models of Chapter 6 to a full dynamic setting. We show how time-series models for prices and trades can be used to study these questions. Throughout the chapter, we focus more on the structure and interpretation of the models than on the econometric and sampling issues that often arise in estimating dynamic time series using microstructure data. Section 9.1 introduces a dynamic model for prices and order flow, with lagged effects of order flow on prices and order-flow dynamics. Section 9.2 generalizes that model to the vector autoregressive model, which was introduced into microstructure by Hasbrouck (1988, 1991, 1993, 1995) and has since become the standard reference model in the literature. We then turn to a formal decomposition of prices into permanent and transitory components, where the permanent component is interpreted as the equilibrium value of the
asset, or the efficient price. Section 9.3 examines price discovery, i.e. the process of convergence on the efficient price, and the role of order flow in this process. Section 9.4 studies price discovery for securities that are traded in multiple market-places. The appendix gives some tools for dealing with dynamic econometric models and lag polynomials.

Fallen Angels and crossover credits are often targeted by alternative investor groups like hedge funds and risk arbitrageurs who speculate on the mispricing between the various financing instruments of a company.

Characteristics of Fallen Angels:

• High leverage in respect to operating cash flows
• Weak industry trends lead to low and unpredictable operating cash flows
• A further deterioration of the operating performance is not sustainable with the financial profile
• Loss of market share
• Not enough liquidity to support the ongoing business
• Decreasing asset quality
• Management is unable to identify profitable business units
• Weak and complex debt structure
• Unfavorable regulatory environment and lack of support by the government (mainly for European companies)

A correlation between total returns of high yield and treasury bonds shows that interest rate risk can certainly not be neglected by high-yield investors. The mid-1990s serve as a good example. High yield and treasury returns had a quite high correlation in an environment of low default rates and tight credit spreads. In 2003 an increased correlation could be observed again when spreads were approaching historical lows and default rates were falling.

High-yield sensitivity to interest rates is a function of credit risk. This means that the high-yield upper tier (BB/BB) segment’s correlation to 10-year treasuries is higher than for lower tier credit (B and below). Duration management in high-yield portfolios will have a positive performance contribution. Particularly crossover credits and BB’s total returns will be also determined by the movements of interest rates.

During times of low default rates, historically tight spreads and low interest rates it is worthwhile to analyze the duration contribution of various sectors to the high-yield index. In a scenario of rising interest rates, sectors with tight spreads and a high average duration should be watched closely due to a high underperformance potential.

Whenever the equity risk premium falls below current spread levels, there is a quasi-arbitrage opportunity between corporate bonds and equities. After a long period with a positive equity credit premium, the picture changed in 1999, signaling the height of the equity bubble. The interpretation of this was that expected returns on corporate bonds versus equities were extremely attractive. While corporate bonds actually outperformed equities by far between 2000 and 2002, those years were characterized by a massive widening of credit spreads. Due to the bursting of the tech bubble and the credit spread tightening since fall 2002, the gap has closed.

One approach to identify relative value between the above-mentioned asset classes is to compare risk premia. For corporate bonds this is equivalent to the spread over government bonds. The comparison versus equities requires the estimation of the equity risk premium, that is the difference between the expected rate of return on the stock market and a risk-free interest rate, usually long-term government bond yields. While there are differences in the sector structure of the equity and corporate bond markets, for example with respect to technology exposure, the equity credit premium may nevertheless provide valuable insights into the relative valuation of both markets. This is because risk factors such as economic growth, risk aversion and implied equity volatility influence both markets in a similar manner.

The rapid growth of the European corporate bond market since 1997 has promoted the acceptance of corporate bonds as a separate asset class. Therefore, identifying relative value not only between equities and government bonds, but also relative to corporate bonds, has become a central task of asset allocators. But, of course, this analysis is also relevant from the perspective of a pure fixed income investor. Not only does it help to assess the outlook for credit spreads in general, but also to decide on the beta or, in other words, the aggressiveness of a pure corporate bond portfolio relative to its benchmark. Although it has been common use to compare equities and government bonds, it is far less common to compare equities and corporate bonds.

Remember that corporate bonds can be replicated by the combination of a riskless bond and a short put on the assets of the company. Since lower rated bonds generally are closer to at-the-money than higher rated bonds, it can be expected that the increase of equity-market volatility leads to a widening of the spread differential between issues of different rating classes. This is due to the fact that the sensitivity of the bonds to changes in volatility is different. Options that trade close to at-the-money levels react more strongly given a change in volatility compared with options, which trade far out-of-the-money. The above-described relationships can be witnessed particularly well during crash scenarios in equity markets. In 1990/91, the rise in equity volatility, which was initiated by numerous profit warnings by companies, was a leading indicator of credit spreads.

The subsequent rise in implied equity-market volatility led to a steepening of the yield differential between high and lower rated credits. Baa and Aa rating classes are chosen to illustrate this relationship because for these rating classes the bond universe offers sufficient breadth and liquidity.