Analyzing Market Volatility

This is how Central Bank liquidity affects markets: a step-by-step guide. Central Banks create bank reserves when they perform QE. Bank reserves are often referred to as ''Liquidity''. When Central Banks engage in liquidity creation (e.g. with QE), they do that in the hope that it activates the so-called Portfolio Rebalancing Effect. To understand this, let’s start from what QE does to the balance sheet of a commercial bank - take a look at the chart below. Following the GFC, regulators forced banks to own more HQLA (high quality liquid assets) to meet depositor outflows. Bank reserves and bonds qualify as ''HQLA'' as they are liquid enough to be converted in cash to meet potential outflows quickly. But banks are not indifferent between owning bank reserves and bonds, and especially if the amount of reserves grows dramatically as a result of QE. Bank reserves are a zero-duration and low-yielding instrument which can be suboptimal to own in big sizes especially if compared with bonds which offer higher returns and duration hedging properties. And this is when the Portfolio Rebalancing Effect kicks in. Once QE starts, Central Banks take away bonds and inject new reserves in the banking system. Loaded with suboptimal reserves, banks will try to switch back the composition of their portfolios towards more bonds. They will bid up safer bonds first, and bid up riskier bonds later when the hunt for returns intensifies. This will kick in a virtuous cycle of low volatility and a hunt for riskier assets: the Portfolio Rebalancing Effect in action. Summarizing: 1️⃣Central Banks expand their balance sheet and purchase government bonds, and often also corporate bonds and mortgage-backed securities. 2️⃣Commercial Banks are on the (forced) receiving end of QE, and hence their portfolio composition gets skewed towards more reserves, and less bonds; 3️⃣But reserves are sub-optimal to own compared to regulatory-friendly bonds for the reasons we discussed, and hence they look to rebalance their portfolios; 4️⃣They start buying the very same bonds QE is buying, hence suppressing volatility further and compressing credit spreads across the board. Other banks who have resisted the temptation have now held inert bank reserves for a while and missed on the ‘‘carry’’ party, and given the reduced volatility pile on and rebalance their portfolio too; 5️⃣Asset allocators and investors across the world are more and more encouraged to take additional risks in their portfolio, supporting the flow of credit and capital. Does the Portfolio Rebalancing Effect make sense to you? 👉 P.S. If you liked this post, you'll love my macro research. 🛑 You can receive it in your inbox FOR FREE! 👇 Sign up below: https://lnkd.in/dp_Ng89T

Bitcoin long-term holders are distributing tokens back into the market for the 3rd time this cycle. What's more interesting is what we can learn from the initial distribution phase in each cycle and how that impacted price action at the *end of the cycle.* In the '21 cycle, LT holders distributed coins late in the cycle (Nov. '20 - March '21) You can argue that they sold into the first (and "true" top of that cycle) In total, their holdings were reduced by 13.5% leading into the "true" top in April '21 (passing coins to short-term holders in the process) They then added to positions and finished the cycle in Nov. '21 with more holdings than they started. For this reason, the 2nd top in November was quite muted (barely surpassing the price peak in April '21). It was not driven by new $ entering the market (chart #2), but rather LT holders re-establishing positions (and some short-term holder positions turning into long-term holdings with the passing of time) ---- We're seeing a similar dynamic take shape in this cycle. You could argue that the first "true" top was in Q1 -- when long-term holders had reduced their holdings by 12.4% We're now seeing the same "re-accumulation" pattern as last cycle, with muted participation from short-term holders (new money entering) ---- What does it mean for the rest of the cycle? I'm sharing a full market update with readers of The DeFi Report on Wednesday If you'd like to have the latest research dropped into your inbox when it's published, you can sign up below Data: Glassnode *note that this data is onchain only (does not include exchanges & ETFs)

As illustrated by this Bloomberg chart, the price shock emanating from the Middle East War has shifted market expectations toward a "higher-for-longer" rate environment across nearly all systemically important central banks. (The outlier remains the Bank of Japan, which continues to inhabit its own paradigm—though less so recently. However, identifying the changed rate trajectory is merely the opening act of the analysis.) The current situation represents more than a simple price shock; it also involves a "second-round" adverse demand shock. Beyond these immediate economic effects, there is the lingering risk of spillovers into financial instability. All of this underscores the uncertain outlook: central banks will be navigating a series of judgments which, I suspect, will likely (or should) be adjudicated by a single, sobering question: "Which is the least unrecoverable mistake we can make?" The answer to this question is less complicated for single mandate central banks, such as the BoE and ECB, than it is for the dual-mandate Fed. #economy #markets #centralbanks

This week’s market volatility has everyone focused on oil. But oil is not the real story. What matters is the chain reaction it can trigger across the system. Higher oil prices feed directly into inflation expectations. Inflation expectations push yields higher. Higher yields compress equity multiples. And all of this is happening while private credit is already under pressure and AI is accelerating structural disruption across the economy. In other words, several forces that normally unfold separately are now colliding at the same time: • Energy shocks • Financial leverage • AI-driven labor disruption • Rising global yields • The early stages of a transition toward digital financial networks Markets often look stable on the surface until the connections between these forces become visible. In this week’s video I walk through the framework I’m using to think about this moment, from the Strait of Hormuz and inflation expectations to private credit stress, AI adoption friction, and why Bitcoin and stablecoins will become increasingly important parts of the next financial architecture. The goal isn’t prediction. It’s understanding how the system is evolving. Video here:https://lnkd.in/eTRgZ2qh

Markets are once again asking what the Federal Reserve does next, especially amid geopolitical shocks and renewed energy volatility. A few takeaways from my recent conversation with Tom Keene and Paul Sweeney on Bloomberg Surveillance: 1. The Fed is in watch and wait mode: With growth holding up and unemployment low policymakers are letting the data evolve as the economy absorbs an energy shock. 2. Energy shocks still matter, even as a net exporter: Higher energy prices weigh on real incomes and growth. 3. Rate cuts remain the base case but timing is uncertain: Most FOMC participants still see scope for at least one cut this year and next, but patience prevails for now. 4. Models are tools, not crystal balls: Central banking is largely an ex-ante exercise judged against ex post outcomes , which argues for humility – and flexibility – when setting policy. 5. Fed independence matters. Restoring price stability while preserving institutional credibility remains central to its mandate and long term value to the economy. In an uncertain environment, the Fed’s challenge is less about predicting shocks, and more about responding carefully when they arrive. Listen to the podcast here. https://bloom.bg/47r4yrM

In S&P Global Ratings' latest edition of #CreditWeek, Nicolas Charnay explores how intensifying trade tensions and policy uncertainties are weighing on global credit conditions and will shift the landscape for financial institutions—with ripple effects that will be felt widely, but unevenly. ➡️ The earliest potential effects will be on financial institutions that have larger exposure to the directly targeted sectors and countries. Many global banks may need to boost credit-loss provisions in response to weaker economic expectations. All this points to a drag on profits for global banks, a trend that we already expected for this year. ➡️ Market volatility has raised counterparty risk, and a sustained period of volatility (particularly in bond and foreign exchange markets) could expose known or unknown vulnerabilities in the global financial system. ➡️ Higher market volatility, increased investor risk-aversion, lower economic growth, and accelerating global fragmentation are negative credit developments. Read and subscribe for forward-looking insights on emerging and established credit risks, answering the questions that matter to markets today.

A 60/40 portfolio does not deliver a constant risk exposure. That sounds obvious, but most investors still ignore it. From 1994 to 2016, the rolling one-year volatility of a monthly rebalanced 60% S&P 500 / 40% Barclays U.S. Aggregate portfolio ranged from less than 5% to as high as 20%.* That is a huge difference. In some environments, a 60/40 portfolio may be appropriate for a conservative investor. In turbulent markets, the same portfolio may look more appropriate for an aggressive investor with a thick skin and high risk tolerance. As I explain in Beyond Diversification, volatility and exposure to loss are not stable through time. A constant asset allocation does not mean constant risk. That is why risk management has to be dynamic. *Data sources are Ibbotson Associates, Standard & Poor’s, and Barclays. As of 12/31/2016.

Kronos is a new AI model from Tsinghua University built to understand financial candlestick data like a language. Trained on 12 billion records from 45 markets, it forecasts prices 93% better than leading models, cuts volatility errors by 9%, and creates more realistic synthetic market data. Traders can use it for forecasting, risk management, and testing strategies. Best part: the pre-trained model is open source on GitHub. 🔹 Performance Kronos lifts price-forecast RankIC by 93 percent versus the leading TSFM and 87 percent versus the best non-pre-trained model. Volatility MAE drops 9 percent. Synthetic K-line fidelity improves 22 percent in zero-shot tests. 🔹 Data scale Pre-trained on over 12 billion K-line records from 45 exchanges and seven frequencies. Learns cross-asset, cross-timescale representations from OHLCVA that transfer across forecasting, risk, and generative tasks without fine-tuning. 🔹 Tokenizer A specialized coarse-to-fine tokenizer with Binary Spherical Quantization discretizes market moves into hierarchical tokens. Sequential subtoken prediction beats concurrent prediction and continuous baselines in ablations with matched parameters. 🔹 Investment impact Backtests on China A-shares show the top AER and IR among baselines. Test-time sampling lets desks trade accuracy for compute. Averaging multiple rollouts raises IC and RankIC without retraining. How to use it: Alpha research. Start with zero-shot price or return forecasting on your universe. Rank by Kronos signals and run quick AER and IR checks. Volatility. Plug Kronos volatility forecasts into position sizing and options surfaces. Synthetic data. Use Kronos-generated K-lines for stress tests and data augmentation. Validate with TSTR before deployment. Inference scaling. Ensemble multiple sampled trajectories for higher stability around rebalance. Credits: Authors: Yu Shi, Zongliang Fu, Shuo Chen, Bohan Zhao, Wei Xu, Changshui Zhang, Jian Li. Institute for Interdisciplinary Information Sciences and Department of Automation, Tsinghua University.

Volatility often feels like risk. But the two are not the same thing. Imagine you invest in a diversified global equity portfolio. Over the next few years, markets swing sharply. At one point your portfolio falls 15%. A year later it rises 20%. The journey feels uncomfortable, but if you stayed invested, the long-term direction of markets has historically worked in your favour. That movement is volatility. It is the price you pay for equity returns. Now consider a different situation. Markets fall 15%, fear takes over, and you decide to sell everything to “protect” your capital. The loss becomes permanent. A few months later the market recovers, but you are no longer invested. That is real risk. Ironically, most investors try very hard to avoid volatility. And in doing so, they end up increasing the very risk they were trying to escape. Because volatility only tests your patience. But panic decisions damage your wealth. Over long periods, markets have rewarded discipline far more than clever timing. The real skill in investing is not predicting every rise and fall. It is having an allocation you understand, and the temperament to stay invested when the ride becomes uncomfortable.

A conversation with a retail investor last week reminded me how counterintuitive investing really is. He asked: “If I can handle the risk, why would I ever choose a lower-return asset?” Fair question. I mean, if one strategy offers a 12% expected return and another offers 6%, why pick the 6% one? Bigger return = better option, right? Not necessarily. What matters is not just the headline return, but also how efficiently that return is generated. Take two strategies: • Strategy A: 12% return, 18% volatility • Strategy B: 6% return, 6% volatility At first glance, A wins. But B only takes one-third of the risk. That means you could hold 3x as much of B and still take the same total risk as A. Now the comparison becomes: • A = 12% return at 18% risk • 3x B = 18% return at 18% risk Same risk. Higher expected return. Now we’re finally comparing apples to apples. That’s the core idea behind risk-adjusted returns, and why professional investors focus so much on the Sharpe ratio: return per unit of risk. Of course, I’m simplifying. Volatility isn’t the full picture of risk, leverage isn’t free, and historical Sharpes don’t hold perfectly going forward. Still, it has big implications: • 100% equities may not be the most efficient portfolio - even if your goal is high returns • Portfolio construction and position sizing matter at least as much as asset selection alone • Diversification is not just about reducing risk - it can improve returns too • Return and risk are linked, but they are not the same decision: choosing the most efficient portfolio first, then sizing it to the risk you actually want, may be better than simply selecting the asset with the highest expected return • Leverage is not automatically more risky than concentration - a modestly levered diversified portfolio can be less risky than an unlevered concentrated one And maybe a more actionable takeaway for retail investors: Spend less time asking which asset has the highest expected return, and more time asking how each asset changes the risk and efficiency of the overall portfolio. #assetallocation #portfoliomanagement #portfolioconstruction #retailinvestors