How to estimate credit spread curves

February 19, 2022

Credit spread curves are essential for analyzing lower-grade bond markets and for the construction of trading strategies that are based on carry and relative value. However, simple spread proxies can be misleading because they assume that default may occur more than once in the given time interval and that losses are in proportion to market value just before default, rather than par value. A more accurate method is to estimate the present value of survival-contingent payments – coupons and principals – as the product of a risk-free discount factor and survival probability. To this, one must add a discounted expected recovery of the par value in case of default. This model allows parametrically defining a grid of curves that depends on rating and maturity. The estimated ‘fair’ spread for a particular rating and tenor would be a sort of weighted average of bonds of nearby rating and tenor.

(more…)

Building a real-time market distress index

March 20, 2021

A new Fed paper explains how to construct a real-time distress index, using the case of the corporate bond market. The index is based on metrics that describe the functioning of primary and secondary markets and, unlike other distress measures, does not rely on prices and volatility alone. Thus, it includes issuance volumes and issuer characteristics on the primary side and trading volumes and liquidity on the secondary market side. Making use of a broad range of data on market functioning reduces the risk of mistaking a decline in asset values for actual market distress. Distress in a market that is critical for funding the economy and the financial system has predictive power for future economic dynamics and can be a valuable trading signal in its own right. It can be used for more advanced trend following and for detecting price distortions.

(more…)

Detecting market price distortions with neural networks

February 22, 2020

Detecting price deviations from fundamental value is challenging because the fundamental value itself is uncertain. A shortcut for doing so is to look at return time series alone and to detect “strict local martingales”, i.e. episodes when the risk-neutral return temporarily follows a random walk while medium-term return expectations decline with the forward horizon length. There is a test based on the instantaneous volatility to identify such strict local martingales. The difficulty is to model the functional form of volatility, which may vary over time. A new approach is to use a recurrent neural network for this purpose, specifically a long short-term memory network. Based on simulated data the neural network approach achieves much higher detection rates for strict local martingales than methods based on conventional volatility estimates.

(more…)

Risk management shocks and price distortions

January 18, 2020

Risk management relies on statistical metrics that converge on common standards. These metrics can change drastically alongside market conditions. A risk management shock is a large unanticipated market-wide change in statistical risk estimates. These shocks give rise to coerced or even distressed flows, typically subsequent to an initial large move in market prices. Risk management shocks and related flows can team up with other dynamics in the financial system to form feedback loops. Such reinforcing dynamics include dynamic hedging, market price-driven credit downgrades, popular fear of crisis, investment fund redemptions, and forced deleveraging. Feedback loops can trigger large and persistent price distortions and offer special trading opportunities.

(more…)

How market liquidity causes price distortions

December 28, 2019

Liquidity is a critical force behind market price distortions (and related trading opportunities). First, the cost of trading in and out of a contract gives rise to a liquidity premium. Second, the risk that transaction costs will rise when market conditions necessitate trading commands a separate liquidity risk premium. Third, actual changes in liquidity can precipitate large price changes without any fundamental value consideration. Finally, low liquidity is conducive to ‘run equilibria`, where bids or offers of some institutional investors turn into pricing signals for others, giving rise to self-reinforcing dynamics with feedback loops and margin calls. Examples for liquidity-driven price distortions in the past include breakdowns of covered interest parity across currencies, bond market ‘tantrums’, and ‘fire sales’ in emerging local-currency markets.

(more…)

The price effects of order flow

December 20, 2019

Order flow means buyer- or seller-initiated transactions at electronic exchanges. Order flow consumes liquidity provided by market makers and drives a wedge between transacted market price and equilibrium price, even if the flow is based on information advantage. Flow distorts market prices for two reasons. First, the need for imminent transaction carries a convenience charge. Second, the prevalence of informed flow justifies a charge for market risk on the part of the market maker. Standard models suggest that the price impact is increasing in the square root of the order flow, i.e. increases with the order size, but not linearly so. New theoretical work suggests that the price impact function may be “S-shaped”, i.e. increases more than proportionately in the smaller size range and less than proportionately for large sizes. The price effects of order flow are relevant for the design of algorithmic trading strategies, both as signal and execution parameter.

(more…)

Rebalancing and market price distortions

December 14, 2019

Price distortions are an important source of short-term trading profits, particularly in turbulent markets. Here price distortions mean apparent price-value gaps that arise from large inefficient flows. An inefficient flow is a transaction that is not motivated by rational risk-return optimization. One source of such inefficient flows is ‘rebalancing’, large-scale institutional transactions that align allocation with fixed targets. Rebalancing flows are detectable or even predictable if one understands their rules. Their motives include benchmarking of portfolios, benchmark changes, regulatory changes, ETF designs, equity parity, capital protection, and – to some extent – high-frequency trading algorithms.

(more…)

Equity return anomalies and their causes

December 7, 2019

The vast range of academically researched equity return anomalies can be condensed into five categories: [1] return momentum, [2] outperformance of high valuation, [3] underperformance of high investment growth, [4] outperformance of high profitability, and [5] outperformance of stocks subject to trading frictions. A new empirical analysis suggests that these return anomalies are related to market inefficiencies, such as investor protection, limits-to-arbitrage, and investor irrationality. In particular, the analysis provides evidence that the valuation return anomaly is largely driven by mispricing.

(more…)

A theory of hedge fund runs

June 8, 2019

Hedge funds’ capital structure is vulnerable to market shocks because most of them offer high liquidity to loss-sensitive investors. Moreover, hedge fund managers form expectations about each other based on market prices and investor flows. When industry-wide position liquidations become a distinct risk they will want to exit early, in order to mitigate losses. Under these conditions, market runs arise from fear of runs, not necessarily because of fundamental risk shocks. This is a major source of “endogenous market risk” to popular investment strategies and subsequent price distortions in financial markets, leading to both setbacks and opportunities in arbitrage and relative value trading.

(more…)

Market noise

June 1, 2019

The term “market noise” refers to transactions that are erratic and unrelated to fundamental value. Theory suggests that without market noise profitable trading would be impossible. Yet, while irrational and erratic trading may occur, most of what we call “noise” reflects rationality disguised by complexity. Illustrating that point, a new paper shows that the effect of rebalancing cascades on the net demand for individual assets is not predictable, even if we know everything about the underlying rules and if they are fully rational. Predictions become infeasible because of alternating buy and sell orders, feedback loops and threshold-based execution rules. This cautions against dismissing seemingly non-fundamental market flows as irrational and betting against them.

(more…)

Tags

Tags
asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation central counterparty clearing China collateral commodities credit returns default ECB economic data EM equity returns euro area crisis Fed financial crisis FX FX interventions FX returns government debt government deficits herding inflation information insurances Japan leverage liquidity machine learning market turmoil price factors regulation risk management shadow banking statistical methods term structure trend following variance risk premium VIX volatility ZLB
ABOUT US

Macrosynergy is a London based macroeconomic research and technology company whose founders have developed and employed macro quantamental investment strategies in liquid, tradable asset classes, across many markets and for a variety of different factors to generate competitive, uncorrelated investment returns for institutional investors for two decades. Our quantitative-fundamental (quantamental) computing system tracks a broad range of real-time macroeconomic trends in developed and emerging countries, transforming them into macro systematic quantamental investment strategies. In June 2020 Macrosynergy and J.P. Morgan started a collaboration to scale the quantamental system and to popularize tradable economics across financial markets.

FOLLOW US
Linkedin
LEGAL
Research
Quantamental Academy
About Us