Futures & Market Research Glossary

Implied volatility is the volatility input that makes an option-pricing model reproduce a specified market price for an option. The option premium is observed; implied volatility is inferred by solving the model backward. It is usually quoted on an annualized basis, with the exact convention determined by the market and pricing model.

A price-implied parameter, not an observed statistic

An option model relates premium to inputs such as the underlying or forward price, strike, time to expiration, interest rates, dividends where relevant, and volatility. If the other inputs are fixed, a higher option premium generally maps to a higher implied volatility. This inversion is why implied volatility can move even when the underlying price barely changes: the option market may be repricing uncertainty, event risk, hedging demand or other factors embedded in the premium.

Implied volatility therefore differs fundamentally from Realized Volatility. Realized volatility is calculated from price changes that actually occurred over a historical window; implied volatility is extracted from option prices for a remaining future horizon. It is forward-looking because it is derived from prices attached to that future horizon, but those prices can also incorporate volatility risk premia, supply and demand, and other pricing effects. Implied volatility is therefore not a promise that subsequent realized volatility will equal the quoted number. The comparison becomes meaningful only after matching the horizon and measurement convention.

Why an underlying does not have one implied volatility

A single underlying can have many simultaneous implied volatilities. Options with the same expiration but different strikes often map to different values, producing the cross-sectional pattern associated with Volatility Skew. Options with different expirations can imply different volatility levels across time, forming a Volatility Term Structure. Saying that “implied volatility is 25%” is therefore incomplete unless the relevant strike or moneyness, expiration and convention are understood.

The less obvious practical point is that implied volatility is also model- and data-dependent. Two systems can start from the same market and report different values if they use different pricing models, interest-rate or dividend assumptions, underlying or forward inputs, settlement versus live quote prices, or interpolation methods. That does not automatically mean one value is wrong; it means implied volatility is a derived coordinate for option prices rather than a directly observed market quantity. For research, comparisons and backtests, the model, price source, strike selection and expiration methodology should be kept consistent before differences in implied volatility are interpreted as differences in the market pricing of volatility.

Imports are goods brought into an economy from abroad and recorded under the applicable trade-statistics framework. In physical commodity analysis, imports are an inflow to available supply, but they are not a direct measure of domestic demand. In a simple commodity balance, total supply is commonly represented as beginning stocks plus production plus imports; that supply is then absorbed by domestic uses, exports, ending stocks and, where relevant, residual or balancing items.

This is why a rise in imports is economically ambiguous on its own. A country may import more because consumption is rising, but also because domestic production fell, inventories are being rebuilt, foreign supply became relatively cheaper, a processor changed feedstock, or cargo timing shifted between reporting periods. Imports are an observed inflow; the reason for that inflow has to be inferred from the rest of the balance and market context.

Measurement can change the signal

The word “imports” can also refer to series built on different statistical bases. International merchandise trade statistics generally focus on goods entering an economic territory. National customs systems may further distinguish general imports from imports for consumption; in U.S. statistics, for example, general imports include physical arrivals entering bonded warehouses or Foreign Trade Zones, while imports for consumption are goods that have cleared Customs into consumption channels. Balance-of-payments statistics use a different conceptual basis: general merchandise is recorded when economic ownership changes between a resident and a nonresident, so merchandise-trade data may require adjustments for coverage, timing, valuation or classification.

For research, the critical questions are therefore what commodity or grade is covered, which geography and unit apply, whether the series measures quantity or value, when the transaction is recorded, and which import concept the dataset uses. A rise in import value can reflect higher prices even when physical volume is unchanged; a rise in physical imports may simply replace domestic production or accumulate in inventories. Imports become more informative when read with production, exports, inventories, domestic use, logistics and relative prices rather than treated as a standalone bullish or bearish signal.

Inflation expectations are beliefs about the rate at which the general price level will change in the future. They matter because expectations can influence wage bargaining, price setting, saving and borrowing decisions, asset valuations and monetary-policy transmission. But expected inflation is not directly observable: published measures come from surveys, financial-market prices or models that infer expectations from other data. Two series labeled “inflation expectations” can therefore move differently without either being mechanically wrong.

What the number actually measures

Survey measures ask households, businesses or professional forecasters about future inflation over a specified horizon. The population, question wording and horizon construction all matter. A one-year forecast is not the same object as a longer-horizon measure, and even labels such as “three-year” or “five-year” do not necessarily mean average inflation over the entire period. Some surveys instead ask about inflation during a future one-year interval. Surveys may also collect probability distributions rather than only point forecasts, allowing researchers to distinguish a respondent's central expectation from uncertainty and to separate individual uncertainty from disagreement across respondents.

Market-based measures provide a different perspective. In the U.S. Treasury market, the difference between comparable nominal Treasury and Treasury Inflation-Protected Securities yields is commonly called breakeven inflation or inflation compensation. It represents the inflation rate associated with equalizing the returns implied by the two types of securities under the comparison. The crucial boundary is that inflation compensation is not a pure observation of expected inflation. It can also contain an inflation risk premium and effects associated with differences in liquidity between nominal Treasuries and TIPS. Models that estimate expected inflation from bond prices therefore attempt, explicitly or implicitly, to separate these components.

Why horizons and measures can disagree

Short-horizon expectations can react strongly to currently visible price changes or economic shocks while expectations farther into the future remain comparatively stable. That distinction is central to the idea of expectations being anchored: the important question is not merely whether near-term expected inflation moves, but whether beliefs about inflation at more distant horizons also shift persistently.

For research and trading, the practical mistake is to treat one series as the definitive inflation-expectations number. Household surveys measure household beliefs under their particular question design; professional surveys measure forecasters' assessments; breakevens measure market inflation compensation; and model estimates depend on their data and assumptions. A change in a breakeven rate, for example, may reflect changing expected inflation, changing compensation for inflation risk, changing relative liquidity, or some combination of them. Comparisons should therefore match the horizon, population, inflation concept and construction method. Inflation expectations are best understood as a latent economic concept observed only through different measurement and inference frameworks, each with its own information content and limitations.

An intercommodity spread is a futures spread in which long and short exposure is taken across two or more different, usually economically related commodity markets. Its purpose is to express or hedge a price relationship rather than the outright direction of a single market. This distinguishes it from a calendar spread, whose legs are different delivery months of the same futures contract.

The hedge ratio is part of the trade

The key variable is not merely which markets are paired, but how the legs are weighted. Contract sizes, quotation units, economic yields and price sensitivity can differ, so a one-for-one position may create a large unintended directional exposure. A usable spread therefore requires an explicit hedge ratio or common-unit conversion.

For a two-leg position that is long market A and short a weighted amount of market B, a reproducible representation is Spread_t = M_A × P_A,t − h × M_B × P_B,t. Here P is the quoted futures price, M is the factor needed to convert each quote into a comparable economic value, and h is the chosen hedge ratio. Depending on the markets, M can incorporate contract size, quotation-unit or currency conversion. Reversing the legs reverses the sign of the measured spread, so the sign convention must remain consistent when historical observations are compared.

Processing spreads make the importance of weighting especially clear: a soybean crush links soybeans with soybean meal and soybean oil, while a crack spread links crude oil with refined products. The ratio is intended to represent an economic relationship, not simply an equal number of contracts. CME Group’s facilitated soybean board crush, for example, uses 10 Soybean, 11 Soybean Meal and 9 Soybean Oil futures because a 1:1:1 construction does not properly reflect contract sizes and processing yields.

Relative-value risk is still market risk

A spread can widen or narrow even when both underlying prices rise, or when both fall; what matters is the movement of one leg relative to the weighted movement of the other legs. That makes intercommodity spreads useful for studying substitution, processing margins, cross-market dislocations and other relative-value relationships. It does not make them inherently market-neutral. The relationship can change because of inventories, seasonality, location, quality, currency, contract specifications, liquidity or a structural break in the underlying economics.

Execution and margin treatment are separate from the economic construction. Some exchanges list intercommodity spreads as multi-leg instruments, reducing the risk of filling one leg without the others. Clearing systems may also grant margin offsets between related products when portfolio risk is judged to be lower. Neither feature proves that the hedge ratio is correct or that the historical relationship will persist. For research, the leg definitions, contract months, units, conversion factors, ratio and spread-sign convention should therefore be recorded explicitly before comparing the spread through time.

An interest rate differential is the difference between comparable interest rates in two economies, currencies or markets. If it is defined as Rate A minus Rate B, a positive value means Rate A is higher; reversing the subtraction reverses the sign without changing either underlying rate. The comparison is most meaningful when the benchmarks have similar maturities and economic roles. A two-year government yield can, for example, be compared with a two-year yield in another currency, whereas subtracting a policy rate from a ten-year bond yield measures a mixture of different concepts.

The rate chosen changes the message

There is no single universal interest rate differential. A comparison of central-bank policy rates describes the gap between current policy settings. Shorter-maturity market rates can incorporate expectations for the future policy path, while longer-dated bond yields can additionally reflect term premia and, depending on the instruments used, differences in credit or liquidity conditions. Nominal-rate and real-yield differentials also answer different questions because inflation compensation affects their construction differently. Researchers therefore need to specify the benchmark, maturity and subtraction order rather than treating “the differential” as a self-defining number.

Interest rate differentials are especially important in foreign-exchange analysis because relative rates affect the returns associated with assets denominated in different currencies. Changes in expected monetary policy can therefore move rate differentials and exchange rates together. The relationship is not mechanical, however. Exchange rates also respond to risk appetite, growth and inflation expectations, capital flows, terms of trade and changes in expected returns on other assets. A widening differential is consequently an observation about relative rates, not by itself a forecast that one currency must appreciate.

Forward exchange rates add a funding dimension

A further distinction appears when currency exposure is hedged. Under covered interest parity, the interest rate differential between otherwise comparable same-maturity exposures is linked to the relationship between spot and forward exchange rates. In actual markets, a non-zero cross-currency basis can make the funding cost implied by FX swaps differ from the corresponding cash-market interest rates. Hedging demand, market liquidity and balance-sheet constraints can help sustain those deviations. As a result, an apparent yield advantage in one currency does not necessarily equal the return advantage available after the exchange-rate exposure is hedged.

For market research, an interest rate differential is therefore best treated as a constructed relative-price measure rather than an automatic trading signal. Its interpretation depends on which rates are compared, which rate is subtracted from which, their maturity and risk characteristics, and whether currency hedging is part of the exposure.

Intermarket analysis is the study of relationships among different financial markets to understand how prices, returns, yields, volatility or other market variables move together and respond to shared economic forces. It can compare asset classes such as equities, government bonds, currencies and commodities, or examine more specific links between markets whose economics are connected. The purpose is not simply to find correlations: it is to determine whether a relationship has a plausible mechanism, whether it is stable enough to be informative, and what a change in that relationship may reveal about the market environment.

Relationships are conditional, not fixed

Many intermarket relationships arise because markets react simultaneously to common drivers. Interest-rate expectations can affect bond yields, equity discount rates and exchange rates; inflation expectations can influence nominal yields, real yields, currencies and commodity pricing; changes in risk appetite or funding conditions can propagate across several markets at once. Other relationships can reflect more direct transmission between markets. Distinguishing these mechanisms matters because observed co-movement does not by itself identify which market is leading, which is responding, or whether either one is causing the other.

A central difficulty is that cross-market relationships can change materially through time. Correlations, sensitivities and apparent lead-lag patterns may strengthen, weaken or reverse as monetary regimes, inflation conditions, volatility, liquidity or investor positioning change. Research on U.S. asset markets has documented both time-varying conditional correlations and contemporaneous spillovers across markets, while high-frequency evidence shows that a single macroeconomic information shock can move equities, commodities and volatility measures almost simultaneously. A relationship that looks predictive at daily or weekly frequency may therefore be two markets reacting to the same information rather than one reliably forecasting the other.

Construction can change the conclusion

Intermarket analysis is also sensitive to how the comparison is built. Price levels and returns answer different questions; assets quoted in different currencies can introduce exchange-rate effects; raw comparisons can be dominated by differences in volatility; and estimated correlation, beta or lead-lag behavior depends on the sampling frequency and rolling window. Commodity futures add another complication because contract rolls and continuous-series construction can create movements that are not equivalent to changes in the underlying economic relationship.

For practitioners, the strongest use of intermarket analysis is therefore conditional rather than mechanical. A bond-equity, dollar-commodity or yield-currency relationship is evidence to investigate, not a permanent trading rule. The most informative event may sometimes be the breakdown of a previously persistent relationship: divergence can indicate a change in the dominant macro driver, market-specific information, liquidity conditions or a broader regime shift. Intermarket analysis becomes most useful when the economic mechanism, statistical construction and current regime support the same interpretation—and most misleading when historical co-movement is treated as an immutable law.

Inventories are quantities of a physical commodity held within the supply chain for future use, processing, sale or delivery rather than immediate consumption. In storable commodity markets, they buffer flows that rarely match perfectly in time: when inflows exceed outflows, inventories can build; when outflows exceed inflows, they can draw down. In commodity-market usage, stocks is often used as a synonym for inventories, especially in official reports, but a published stock figure inherits the source’s own coverage and classification rules.

The reported number has a boundary

Published inventory data are therefore not necessarily a count of every unit of the commodity that physically exists. Petroleum statistics may measure primary stocks while excluding retail or end-use holdings; grain statistics may distinguish on-farm from off-farm stocks; exchange warehouse reports may divide metal by warrant or reporting status. Two series both described as stocks or inventories can consequently represent materially different pools of supply, so comparisons require consistent geography, product definition, storage coverage and observation date.

Physical existence is also different from economic availability. Material can be far from the relevant consumption or delivery point, constrained by quality specifications or logistics, or required for the operation of a storage system. Underground natural-gas storage illustrates the distinction particularly clearly: base or cushion gas is maintained to support reservoir pressure and deliverability, while working gas is the portion available for withdrawal. The headline quantity in storage can therefore be larger than the volume that is readily available to the market.

A build or draw is an observation, not a price signal

An inventory build means the measured stock increased over the observation interval; a draw means it decreased. That fact alone does not identify which flow caused the change or what prices should do next. Production, consumption, imports, exports, processing schedules, transport disruptions, seasonality, measurement adjustments and revisions can all affect the observed balance. Low inventories may accompany physical tightness and stronger demand for prompt supply, while high inventories may coincide with greater storage pressure, but the price implication depends on expectations, location, accessibility and prevailing market structure.

For research, the useful question is not simply “How much inventory is there?” but “What is being counted, where is it, how available is it, and against what seasonal or operational benchmark?” That discipline prevents a source-defined stock statistic from being mistaken for the market’s entire usable supply.

An inventory build is an increase in the quantity of a physical commodity held in a defined inventory pool between two observations. In physical balance terms, stocks generally build when inflows into that pool exceed outflows. A reported build, however, describes a change in a particular inventory measure; it does not necessarily mean that all available supply in the wider market increased by the same amount.

Location and accessibility change the meaning

The same headline build can arise from very different conditions. Inventories may accumulate because production increased, consumption weakened, exports slowed, seasonal storage activity intensified or a logistical constraint prevented material from moving onward. Where the stocks accumulate can matter as much as the aggregate change. For example, U.S. crude inventories can rise while stocks at Cushing, Oklahoma, fall. Because Cushing is the designated delivery point for NYMEX crude-oil futures, a national build does not by itself establish that deliverable supply at that location has become more abundant. Grade, location, operational accessibility and storage constraints can all affect the economic significance of a unit of inventory. More inventory and less physical tightness are therefore related concepts, not synonyms.

A reported build is not always a pure physical inflow

Inventory statistics can also contain measurement effects that matter for interpretation. EIA's weekly underground natural-gas data, for example, measure working gas, and changes in reported working-gas stocks reflect injections, withdrawals and reclassifications between base gas and working gas. A reported increase can therefore include a classification effect rather than representing only net physical injection. The weekly estimates are also derived from a survey sample rather than every storage operator. Similar scope and measurement questions arise across commodity datasets, so researchers should identify exactly what stock category, geography and reporting convention a series represents before comparing builds.

An inventory build is therefore an observation, not a directional trading signal or a complete diagnosis of market balance. Its significance becomes clearer when it is evaluated alongside seasonal norms, storage capacity, location, contemporaneous supply and demand flows and the persistence of the change. A build caused by weak demand can carry different information from one caused by seasonal storage or disrupted exports, even when the reported increase is identical.

An inventory cycle is the sequence through which physical stocks accumulate, peak, decline, and trough as inflows and outflows differ over time. It is broader than an Inventory Build or Inventory Draw, which describe a change over one reporting interval: the cycle is the multi-period path formed by successive builds and draws. A Storage Cycle is narrower, focusing on the operational pattern of placing material into storage and withdrawing it.

The path matters more than the latest change

Inventory analysis is inherently relative. Many commodity stocks follow recurring seasonal patterns because production and consumption do not occur at the same rate throughout the year. Weather, harvest timing, maintenance, trade flows, logistical disruptions and the economics of carrying stocks can alter that expected path. A build can therefore be consistent with tightening relative to expectations if inventories are rising more slowly than normal, while a draw can occur from historically ample stocks. The sign of the latest change does not, by itself, identify the state of the physical market.

Headline stocks can hide physical and measurement differences

The same aggregate inventory total can mask different market flexibility when stocks differ by location, storage type or deliverability. Natural gas provides a clear example: working gas is the portion available to the market above base gas, and withdrawal deliverability generally declines as an underground reservoir is depleted, while injection capability generally declines as it becomes fuller. Stock quantity and usable flow capacity are therefore different dimensions of inventory conditions.

Reported inventory changes may also contain more than physical inflows and outflows. In U.S. weekly natural gas storage data, for example, changes in working-gas stocks can reflect injections, withdrawals and reclassifications between base gas and working gas. For research, the current stock level and its rate of change should therefore be compared with seasonally matched history, relevant physical constraints and the reporting methodology before inferring tightness or surplus. Seasonality describes a recurring calendar tendency; the inventory cycle is the realized stock path, which may follow that tendency, deviate from it or change as market structure evolves.

An inventory draw is a decrease in the quantity of a commodity held in a defined stock pool between two observation dates. It is the opposite of an Inventory Build. The stock level ended below where it began, meaning withdrawals and other reductions exceeded additions within that measurement universe. The observation does not by itself reveal which balance component caused the decline, whether the market is physically tight, or whether price should rise.

One draw can have several causes

Inventories are the residual of inflows and outflows. In a simplified physical balance, stocks fall when consumption, processing, exports, transfers out or other withdrawals exceed additions from production, imports, transfers in and other supply, after any market-specific adjustments. A draw can therefore reflect stronger consumption, weaker production, lower imports, higher exports, an operational disruption or several forces at once. The same absolute draw can carry very different implications depending on starting stocks, normal seasonality, market size, deliverability and expected future supply.

The stock universe can matter more than the headline

Every inventory series measures a particular universe. Coverage may differ by geography, grade, facility type, ownership class or whether stocks in pipelines and in transit are included. Official series can also separate commercial, strategic, on-farm or off-farm inventories. An aggregate draw can therefore coexist with a build at a delivery hub or in another region. For futures analysis, that distinction can matter more than the national total when nearby supply, deliverable stocks or local basis is the binding constraint.

Interpreting a one-period draw also requires attention to data construction. Survey coverage, estimation, revisions and the timing of related flow reports can affect how confidently a researcher attributes the change to production, imports, exports or demand. EIA, for example, documents that petroleum imports and associated inventories can be reported in different weeks because different respondents use different accounting systems; the timing mismatch affects weekly product supplied, not the basic meaning of a draw as lower measured stocks. The stronger interpretation compares the draw with expectations, seasonality and recent history, then asks where stocks moved and whether other balance data confirm the explanation. A draw is an observation. “Bullish” is a separate conclusion.