Currency Volatility Analysis

An airline made $75 million flying passengers. It lost $171 million to the exchange rate. In 2023, Kenya Airways reported an operating profit of roughly $75 million. Foreign exchange losses of $171 million on monetary items, loans, and leases turned it into a $162 million loss before tax. The airline made money operationally. Currency destroyed the bottom line. Everyone talks about fuel price volatility. Currency fluctuations don't get the same attention. They should. 55 to 60% of airline costs are USD-denominated. Only 50 to 55% of revenue is, and that is a global aggregate. US carriers earn heavily in USD, narrowing their gap. For most non-US carriers, the currency deficit is structural. A 1% move in the USD shifts global airline profits by roughly 1%. On a $41 billion industry profit, that is not a rounding error. The risk enters when fares are published in foreign currencies, when tickets are sold and rates have moved, through BSP, CASS, and PSP settlement delays, and again when funds are finally repatriated. It touches pricing, route economics, fleet financing, and the actual yield the airline captures. Every airline executive should understand how. Not just finance. This reference guide maps airline FX risk in a single view: the six sources of currency exposure, the revenue flow showing where risk enters, and the three structural positions that determine how sensitive an airline is to currency movements. A weaker dollar is widely reported as good news for airlines. Is it? And how will it impact the 2026 results? Like this post: 💾 Save for quick reference 🔄 Share with your network and spread the knowledge #Airlines #AirlineEconomics #AirlineFinance #AviationConsulting #Air52Insights

Volatility is far more predictable than returns. Returns are noisy and prone to sudden regime shifts. Volatility follows patterns. It clusters together when markets are turbulent. It reverts to long-term averages after extreme periods. It behaves differently across daily, weekly, and monthly timeframes. And it responds predictably to market shocks and announcements. Verdad tested multiple volatility forecasting models across major asset classes. The HAR (Heterogeneous Autoregressive) model, which combines short, medium, and long-term volatility measures was the clear winner. Different participants operate on different timeframes. High-frequency traders react to intraday movements. Hedge funds might trade weekly around events. Pension funds and sovereign wealth funds move monthly or quarterly. The model captures how volatility propagates across these different time horizons. In their analysis spanning 1996 to 2025, HAR consistently outperformed alternatives. It beat simple averages, trailing volatility measures, exponentially weighted averages, and even the more complex GARCH models. The outperformance was consistent across currencies, commodities, equities, and bonds. Verdad enhanced the basic HAR model by adding forward-looking information from options markets. This HAR-IV model incorporates the VIX to capture market expectations of future volatility. Adding implied volatility improved performance across all evaluation metrics. The enhancement proves particularly valuable during regime shifts or before major macro events. Options markets often price in risks that backward-looking measures haven't captured yet. Simple models often beat complex ones. The HAR model uses basic linear regression with lagged volatility measures. Despite its simplicity, it outperforms more sophisticated alternatives. Complexity does not always equals superiority in financial modeling. https://lnkd.in/eM_wktsH

📈 Why quants still rely on ARCH and GARCH family models Before deep learning and complex stochastic volatility models, quants learned one hard truth. Volatility is not constant. It clusters, reacts to shocks, and behaves asymmetrically. That insight gave rise to the ARCH and GARCH family of models, which are still widely used today because they capture how markets actually behave. ARCH models showed that today’s volatility depends on past squared returns. Large moves lead to large future risk. This was the first step toward modeling volatility as a dynamic process rather than a fixed number. GARCH extended this idea by allowing volatility to depend on both past shocks and past volatility. This simple structure captures persistence in volatility and remains a benchmark model across asset classes. GJR GARCH and TGARCH recognized an important market reality. Negative returns increase future volatility more than positive returns of the same magnitude. These models explicitly capture leverage effects and downside risk. EWMA takes a practical approach. Recent observations matter more than older ones. It is fast, intuitive, and widely used in risk systems where stability and speed are critical. These models matter because they directly power Option pricing adjustments Value at Risk and Expected Shortfall Stress testing and scenario analysis Volatility targeting and position sizing They may look simple on paper, but they encode decades of market behavior. Modern volatility models build on them. Risk systems still trust them. And every serious quant should understand them deeply. #QuantFinance #VolatilityModeling #ARCH #GARCH #RiskManagement #MarketRisk #Derivatives

Accounting for Foreign Currency Transactions and Exchange Differences Recent movements in the kwacha illustrate just how material foreign exchange effects have become in both financial reporting and performance analysis. A comparison of exchange rates across key dates highlights the scale of these movements. As at 3 June 2025, the kwacha traded at an average of 26.8092 against the US dollar. By 3 January 2026, it had strengthened to 22.0694, representing an appreciation of approximately 17.68%. This momentum has continued into mid-year, with the rate improving further to 17.8439 by 3 June 2026, reflecting a further 19.17% appreciation over six months and an overall year-on-year strengthening of approximately 33.45%. How should these changes be reflected in financial statements? In line with IAS 21 – The Effects of Changes in Foreign Exchange Rates, the accounting treatment is as follows: ✅ Monetary items (such as cash and cash equivalents, borrowings, receivables, and payables) are retranslated at the closing rate at the reporting date. ✅ Non-monetary items (such as property, plant, and equipment) are generally measured at the historical spot rate when the transaction occurred. The resulting foreign exchange differences on monetary items are recognized in profit or loss, except in limited circumstances where they are capitalized or recognized in other comprehensive income (e.g., certain net investment hedges). Although foreign exchange differences are often described as non-cash, their impact on financial results is far from negligible. With exchange rate movements exceeding 30% over a twelve-month period, these remeasurements can materially distort earnings and, in some cases, overshadow underlying operational performance. This reinforces an important point for both preparers and users of financial statements: financial reporting in a volatile currency environment requires a clear understanding of economic exposure, careful analysis of performance drivers, and the ability to distinguish between operational outcomes and the effects of exchange rate movements. Are you evaluating company performance, or simply measuring the impact of currency movements? #finance #ifrs #exchangerates #future

Currency fluctuations cost us $500K in ARR (-2%) in November. Here's how: For a long time, customers could only pay in USD via credit card at lemlist. But, 9 months ago, we enabled payments in EUR & GBP to make things easier for international customers. To keep things simple, we used a 1:1 conversion rate: 1$ = 1€ = 1£. Fast forward to today: 1/3rd of our revenue — $9M — is billed in EUR. So when the EUR dropped 6% against the USD, our ARR dropped by 6% * 33% = -2%. The interesting part? Our business actually grew in November. But the reported ARR took a hit due to currency shifts — something totally outside our control. It’s a reminder that as you scale, metrics aren’t just about performance. They can be shaped by external forces like currency rates, inflation, and broader macroeconomic trends. Back in my M&A days, we always adjusted for constant FX rates to see a business’s real growth. It stripped out the noise and let us focus on what actually mattered. Hope you'll find this valuable 🙏

What Physics Has Taught Us About Market Volatility. The USD/JPY Case 2022–2023 All widely used volatility models, GARCH, EWMA, Heston, Local Vol, share the same epistemic foundation: they assume that present risk is merely a projection of the most recent shock and its associated historical volatility. This is an extreme reduction of system dynamics, implicitly presuming that the structure of risk is stationary, linear, and fully describable through regressions of the recent past. Such an approach completely ignores the processes of internal tension accumulation, the progressive degradation of system elasticity, the formation of structural curvature, and the emergence of critical nonlinearities that precede every regime transition. It is a reassuring representation because it is operationally simple but It is also a radically inadequate description of financial markets, which are complex systems characterized by agent heterogeneity, nonlinear feedback loops, self-organization phenomena, and sudden transitions between states of apparent equilibrium. All models of this paradigm are inevitably arbitrary: their outputs vary drastically with changes in parameters, time windows, or forecasting horizons. Physical properties, by contrast, emerge from measurable structural variables: accumulated tension, residual elasticity, curvature of the risk field, and the progressive rigidity of the system. Quantis Operational Context - USD/JPY 2022–2023 In that period, as mentioned in previous posts, we were monitoring USD/JPY closely. It was a market phase characterized by a growing imbalance between Bank of Japan interventions, carry-trade risk compression, and a volatility structure showing early signs of rigidity. While traditional econometric models (GARCH, EWMA, Realized Vol) continued to describe an apparently stable environment, the internal dynamics of the market were already shifting. On October 28, 2022, we registered a clear volatility regime change: a structural jump that traditional models only recognized ex post. This was not a parametrization error, but it was the logical consequence of the econometric paradigm itself, which measures the past rather than the physical state of the system. The Quantis Elasticity Surface helped us clarify the degradation of FX elasticity and the buildup of internal tension in the preceding weeks. It was not measuring shocks; it was measuring the system’s ability to absorb them. The rising curvature signaled the progressive deterioration of the structure long before price or realized volatility reflected it. We observed these structural properties emerging in real time. It was an important lesson, because it reinforced a fundamental truth: markets do not collapse because past volatility is high—they collapse because the structure loses elasticity until it breaks. Quantis builds physics-based mathematical models of complex financial systems.

The Indian Rupee at 90+: Crisis or Structural Reality? In 2025, the Indian Rupee crossed the psychologically important ₹90 per USD mark. Predictably, headlines screamed “Rupee Weakness” and “Currency Pressure.” But is this really a crisis — or a structural macro adjustment playing out as expected? After closely analysing data, capital flows, and policy responses, one conclusion is clear: The current rupee depreciation is structural, gradual, and largely managed — not a panic-driven collapse. What Is Driving the Rupee’s Depreciation? Structural Trade Imbalance India remains heavily dependent on oil, gold, and capital goods imports, creating persistent demand for dollars. With exports unable to fully offset imports, a current account deficit naturally puts pressure on the currency. Inflation Differential India’s inflation has consistently exceeded that of the US. Over time, currencies adjust to preserve real purchasing power parity — making gradual nominal depreciation both logical and necessary. Global Dollar Strength High US interest rates and a strong Dollar Index (DXY) have attracted global capital back to dollar assets, weakening most emerging-market currencies — not just the rupee. FPI Outflows: The Cyclical Accelerator In 2025 alone, foreign portfolio outflows exceeded ₹1.5 lakh crore, amplifying depreciation pressure. Capital flows move faster than trade flows — and their impact on FX markets is immediate. RBI’s Silent Strategy: Managed Depreciation One of the most under-appreciated aspects of 2025 has been the RBI’s strategic restraint. Rather than aggressively defending a specific exchange-rate level, the RBI has: Allowed orderly, predictable depreciation Intervened only to smooth volatility Preserved FX reserves (still ~$600+ billion) Maintained monetary policy independence This shift signals maturity — not weakness. Why This Matters for Corporates & Investors Importers face rising input costs → FX hedging is no longer optional Exporters benefit from margin expansion but must manage volatility Investors see INR depreciation dilute unhedged returns Portfolio Managers must separate asset performance from currency effects In short: FX risk is now a balance-sheet risk, not a treasury footnote. Outlook: What Lies Ahead? Base Case (Most Likely): INR depreciates gradually at 2–3% annually USD/INR stabilises in the 91–93 range Inflation impact remains manageable No disorderly devaluation Tail Risks Exist, but they are linked to global shocks — not domestic fragility. The real question for finance professionals is not “Why is the rupee falling?” but “How effectively are we managing currency risk?” Those who understand this shift will hedge better, price smarter, and allocate capital more intelligently. This article is part of an ongoing series on currency markets, capital flows, and financial risk management.

𝗞𝘄𝗮𝗰𝗵𝗮 𝘃𝗼𝗹𝗮𝘁𝗶𝗹𝗶𝘁𝘆 — 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗶𝗺𝗽𝗮𝗰𝘁 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗻𝘁𝘀? In June 2025 alone, the Zambian Kwacha appreciated by over 10% against the US Dollar. While some celebrated, others , especially those with USD receivables, quietly watched their margins shrink. If you owe USD, this appreciation probably feels like a gift. If you’re owed USD, it’s a daily hit to your income statement. But here’s the key question: 𝗔𝗿𝗲 𝘆𝗼𝘂 𝗺𝗮𝗻𝗮𝗴𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗿𝗶𝘀𝗸, 𝗼𝗿 𝗷𝘂𝘀𝘁 𝗵𝗼𝗽𝗶𝗻𝗴 𝗶𝘁 𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘀𝗲𝘀? Despite decades of FX exposure, most Zambian companies still don’t use derivatives like forwards, swaps, or options to hedge currency risk. Why? • They’re seen as too complex • The accounting (under IFRS 9) is intimidating • There’s little internal capacity to value or manage them But ignoring derivatives doesn’t remove the risk, it just keeps it off the radar until it’s too late. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗹𝗲𝘀𝘀𝗼𝗻𝘀 𝗳𝗼𝗿 𝗭𝗮𝗺𝗯𝗶𝗮𝗻 𝗳𝗶𝗻𝗮𝗻𝗰𝗲 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 & 𝘁𝗿𝗲𝗮𝘀𝘂𝗿𝗲𝗿𝘀: 1. Hedging doesn’t eliminate risk , it provides clarity; You pay a premium to protect your downside. It’s not about winning on every trade , it’s about avoiding surprises. 2. IFRS 9 requires fair value accounting: Every derivative must be revalued at each reporting date. Without hedge accounting, these gains/losses go to profit or loss and can be material. 3. Valuations must be robust: Many companies rely on simple rate spreads, but proper valuation considers forward curves, discounting, and counterparty risk. Get a specialist if needed. 4. Derivatives are easy to miss in reporting: Because they're 'off-balance sheet' until maturity, they’re often ignored, until your auditor finds them. 5. Fair value ≠ FX translation: You’re not just restating a USD balance. You’re valuing the contract itself, a forward deal is a separate financial instrument. 6. Banks will always price in their margins: You won’t “win” unless you understand how the instrument is priced and what you’re actually paying for. As they say, FX volatility isn’t going away and the real risk is not in the exchange rate , it’s in being unprepared. 

At the heart of the global monetary and financial system sits the foreign exchange market (FX Market)- a highly liquid market which processes $9.8 trillion worth of transactions every single day. The foreign exchange market facilitates trade and the movement of money globally - it has grown 5x since the 1990s. Central to this market are a handful of currencies anchored by the US dollar (USD). The USD has become increasingly dominant since the end of Bretton Woods as it supplanted gold. Currently, over 60% of the world’s economies anchor their currencies to the USD in one form or another and 89% of all global transactions are conducted in USD. Yet, the FX Market seems increasingly susceptible to policy uncertainty and macroeconomic shifts. Last week, the International Monetary Fund (IMF) warned that shifts in policy that elevate volatility and uncertainty are likely to adversely impact the FX Market. Disruptions in the FX Market could then bleed into other assets such as equities and bonds with widening currency bid-asks, higher FX volatility, more illiquidity and increased funding and hedging costs. These shifts can have negative repercussions on global yields and risk premia as countries and corporates have to manage their currency exposures.   Historically, increased uncertainty was good for the USD. Since 2002 at least, any heightened volatility or increases in perceived or real risks has meant USD appreciation. In 2025, that long established pattern broke. In April, for example, demand for the USD in the spot market was less than it was during previous cycles of higher VIX. The USD, instead, depreciated by 6.5% against the Euro and on Oct 10, the DXY was lower on news of further trade escalations. In contrast, when tariffs were imposed in 2018 and 2019, the USD rallied by 10% in ‘18 and 5% in ‘19. A USD trending lower may make US companies less attractive to non-US investors as their returns in home currencies are lower (think of a non-US country with a depreciating currency and its impact on returns in USD). Less investment by foreign investors effectively means less demand for USD potentially creating a self-enforcing negative cycle for the USD and a virtuous cycle for other currencies. In addition, if the USD is no longer an automatic “buy” at times of stress, it will become less attractive and further lowering demand. Lower USD relative to other currencies means higher inflation as import prices increase, elevated yields for corporates and the US government as well as lower demand for US assets by non-US investors (as they worry about the exchange differential). None of these are good for US assets or US Markets. Instead, inflation protection strategies, hard assets (that don’t melt down on a whim) and non-US assets may thus become increasingly more attractive for global investors looking to mitigate against the consequences of this paradigm shift. PS: Not AI content. Not investment advice.

The recent market shocks have left a tremendous effect on investors’s mindmap. The volatility and the jump in the asset prices movements are extremely high. On a behavioural finance level, there is surely panic in the market leaving less headroom to ponder about the situations for normal retail investors. Thus, the implementation of mathematical models becomes a necessity not only to predict pricing value but considering volatility, jumps and high shocks. Although, the reason is different but at the end considering the dip in Japan stock market was lower than the Covid-19 pandemic. Using stochastic process mathematical models like Heston model could be used to predict both the asset price and its volatility, allowing for a mean-reverting volatility process while Hull White model for incorporating jumps in the asset prices. This way we get the volatility, jump and asset price. Also, if we consider multivariate volatility (time varying correlations with standardized returns) with correlation b/w the multiple assets, a great recommendation to opt for the extended GARCH model with dynamic conditional coorelation (DCC). Once you could predict the dynamic correlation with varying time portfolio optimization becomes more efficient with time-varying covariance matrix. No wonder, why maths with finance using tech makes such predictions better and high accuracy rates. #quantitativefinance #quant #finance #riskmanagement #japan