Liquidity and Tail Risks

Liquidity in Fixed Income Markets

Unlike equity markets, fixed-income markets are generally less liquid despite being more prominent. This reduced liquidity can be attributed to several factors discussed in this section. Among these factors, the absence of uniformity in bond issues and the over-the-counter (OTC) nature of the markets play significant roles.

Factors Contributing to Credit Market Illiquidity

Digging deeper into the credit markets within fixed-income trading, we uncover additional complexities that contribute to dealing with illiquidity. In comparison to developed market government bonds and high-yield bonds, credit markets encounter liquidity challenges stemming from the following factors:

• The aftermath of the 2008-2009 financial crisis has led to reduced trading volume.
• During market crises, credit markets experience heightened volatility that affects them disproportionately compared to investment-grade or government debt markets. When investor sentiment declines, many investors seek safety in less credit-risky securities, causing an uptick in prices for investment-grade bonds. This flight to safety ensures that higher creditworthy issues remain popular even in market catastrophes.
• Dealers have become less willing to hold substantial amounts of credit-risky fixed-income securities for sale. This unwillingness increases bid-ask spreads and reduces overall market liquidity.

Tools to Manage Liquidity Issues

To tackle the growing complexities of credit market management, fixed-income portfolio managers utilize the following strategies:

• Boost cash holdings: As per the investment policy statement (IPS), managers may maintain a portion of the portfolio as cash. This enhances the ability to seize opportunities and acquire new bonds without being hindered by cash shortages.
• Allocate to more liquid bonds: Continuing the same approach, managers with allocation flexibility can overweight more liquid bonds in the market, ensuring increased maneuverability.
• Utilize derivatives: For managers facing difficulty sourcing appropriate credit market transactions, derivatives offer an attractive solution. Instead of holding the underlying credit-risky security, managers can achieve the desired exposure through derivative products like credit default swaps, bond forwards and futures, and various swaps.
• Increase ETF allocation: ETFs act as interim solutions, allowing managers to gain exposure to a specific bond market segment through ETFs that mirror desired allocations. While temporary, ETFs offer time to locate a more suitable, liquid transaction for the required bond in the portfolio.

Tail Risk

Tail risk refers to the possibility of extreme events or outcomes occurring more frequently than traditional statistical models predict. These extreme events, often called “fat tails,” represent situations where the Probability of significant losses in a portfolio is higher than a normal distribution would suggest. In other words, the distribution's tails are fatter, indicating a greater likelihood of rare but impactful events. This concept challenges the assumption of a normal distribution commonly used in financial modeling and risk assessment. During market stress or crisis, correlations between different securities can increase, reducing the effectiveness of diversification strategies. This can lead to significant losses in investment portfolios, underscoring the importance of understanding and managing tail risk.

Value at Risk Measures for Fixed-income Analysis

Value at Risk (VAR) is widely used for assessing tail risk in fixed-income portfolios. It estimates the minimum expected loss over a specified time horizon and confidence level. For instance, a daily VAR of 7% amounting to $2,000,000 indicates an anticipation of losing $2,000,000 on 7% of trading days annually.

Although VAR is a helpful starting point in tail risk management, it has limitations. Specifically, it underestimates losses during extreme events, central to tail risk. To address this, other “VAR variant” measures are employed:

Conditional VAR calculates the average of significant losses in the tail beyond the VAR threshold. This aims to quantify losses when they occur and rectify some of the deficiencies of traditional VAR.

Incremental VAR gauges the uncertainty introduced to a portfolio when positions are bought or sold, analyzing each position individually. For example, a portfolio manager could compute the traditional VAR and then re-evaluate incremental VAR after removing the lowest credit quality holding.

Relative VAR involves dividing the portfolio's VAR by an appropriate benchmark's VAR, similar to tracking error. While a favorable VAR figure might seem appealing, a significant deviation from the benchmark could indicate issues within the portfolio.

Methods to Manage Tail Risk

Tail risk's elusive nature complicates its management. Addressing tail risk ranges from obtaining extra insurance for a portfolio to anticipating unforeseen risks. Several strategies are available:

• Diversification: A strong starting point involves diversifying to mitigate losses during market catastrophes. For example, a manager concerned about inflation risk might purchase inflation-indexed bonds such as Treasury Inflation-Protected Securities (TIPS) to counter market downturns. TIPS' value increases with inflation, acting as a hedge. However, diversification has limits due to heightened correlations during market sell-offs.
• Hedging Products: Various fixed-income instruments offer portfolio protection. Bonds with embedded options like put options shield bondholders against rising interest rates. Credit default swaps (CDS) offer known payoffs upon specific adverse events, resembling insurance.
• Monte Carlo Simulation/Backtesting: Backtesting uses historical data to comprehend current market trends. Combining Monte Carlo Simulation (MCS) with backtesting can provide insight into a portfolio's risk metrics. Analysts can explore what the backtest captured beyond standard deviation, return, and correlations if simulation results differ from mean-variance analysis.
• Historical Simulation: This technique ranks portfolios based on random outcomes generated from historical data. Although suited for option positions, it assumes historical data repetition, which might not always hold.

Question

Security correlations and market volatility most likely have the following relationship:

1. Inverse.
2. Positive.
3. Mostly stable.

Solution

The correct answer is A.

The most likely relationship between security correlations and market volatility is inverse. When market volatility rises, security correlations decrease as investors seek diversification and non-correlated assets to manage risk during turbulent times. Conversely, security correlations may increase when market volatility subsides as asset movements synchronize.

B is incorrect. It implies that security correlations and market volatility move in the same direction, which is not the typical relationship observed in financial markets.

C is incorrect. Security correlations and market volatility often exhibit changes, especially during market stress or changing economic conditions.

Reading 22: Fixed Income Active Management: Credit Strategies

Los 22 (e) Discuss liquidity risk in credit markets and how liquidity risk can be managed in a credit portfolio

Los 22 (f) Describe how to assess and manage tail risk in credit portfolios