South Korea Broad Liquidity (L)

The Korean monetary aggregates KRM1, KRM2, KRLF, and KRL are raw stock series recorded as reported without any transformation, the won volume of money and liquidity held by the domestic non-bank sectors. They form a nested liquidity hierarchy of increasing breadth, where narrow money is contained in broad money, broad money in the financial-institution liquidity aggregate, and that aggregate in the broadest liquidity measure, so each successive layer adds progressively less liquid instruments.

Each aggregate is a financial-accounts magnitude in the lineage of moneyflows accounting, where every deposit or instrument is entered as a liability of the issuing sector matched against the holder's claim (Copeland 1949). The components sit in the sectoral balance sheets built to measure financial-asset and liability stocks (Goldsmith, Lipsey, and Mendelson 1963), and the same quantities are read from the financial account of the integrated social-accounting system (Stone 1966).

Summing heterogeneous monetary instruments of differing liquidity into a single won total is an aggregation problem, resolved by combining components under index-number theory and user-cost weighting (Barnett 1980; Diewert 1976). The weighting of component growth rates into an aggregate follows the log-change index tradition (Divisia 1925; Fisher 1922).

Conceptually money is a claim whose yield forgone is the price equating present and future income (Fisher 1930). The broad totals are credit-and-liquidity aggregates in the same family as the economy-wide money stock studied in the long-run macrofinancial record (Schularick and Taylor 2012; Jordà, Schularick, and Taylor 2017).

Because each aggregate is a sectoral stock rather than a flow, it is interpreted through the stock-flow accounting identity (Obstfeld and Rogoff 1995), and it belongs to the family of national balance-sheet measures (Lane and Milesi-Ferretti 2001). Three of the four aggregates are seasonally adjusted at source and are recorded here without further adjustment.

KRED applies no transformation to KRM1, KRM2, KRLF, or KRL, neither deflating, re-scaling, smoothing, nor annualizing them, so each published number is the compiled stock exactly as measured, in billions of won. The seasonal adjustment carried in three of the four aggregates is performed at source and reproduced verbatim, not applied by KRED.

The measurement basis is the flow-of-funds framework, first founded as moneyflows accounting (Copeland 1949) and later implemented as a full empirical system (Copeland 1952), which records every monetary instrument simultaneously as a liability of the issuing sector and a claim of the holder under quadruple-entry bookkeeping. The aggregates are read from the financial account of the integrated social-accounting system (Stone 1966), and their balance-sheet tabulation follows the year-by-year national balance sheets (Goldsmith, Lipsey, and Mendelson 1963).

The practitioner conventions for sectoring institutional units and classifying instruments follow the handbook treatment (Dawson 1996). The narrow aggregate reports currency and transferable deposits, and each broader layer adds successively less liquid claims, so that the four totals nest by construction rather than by estimation.

Combining components that differ in liquidity and yield into one won figure invokes aggregation theory, under which quantities weighted by their user costs combine into an exact index (Barnett 1980; Diewert 1976). The index-number apparatus for such weighting is the classical one (Fisher 1922; Törnqvist 1936; Divisia 1925). Three aggregates are average-of-period balances and the broadest is an end-of-period balance, and in every case the series exists as an accounting-consistent stock rather than a modelled quantity (Fisher 1930).

The monetary aggregates are among the most-watched macro-financial quantities, because their growth traces the liquidity backdrop against which spending, inflation, and asset prices evolve. Money is a claim whose opportunity cost is the interest forgone by holding it, which links the aggregates to the intertemporal choices of the holders (Fisher 1930). The broad totals move with the financial cycle, and their rapid expansion has repeatedly preceded financial instability in the long-run record (Schularick and Taylor 2012; Jordà, Schularick, and Taylor 2017).

The liquidity hierarchy is informative precisely because its layers behave differently. A liquidity-weighted reading of the components, rather than a simple sum, better captures the monetary services the aggregates deliver (Barnett 1980; Diewert 1976). The moneyflows accounting situates each layer within the circular flow of payments (Copeland 1952), and the integrated accounts make the holders' balances a direct object of demand analysis (Stone 1966).

Read against income and saving, the stocks measure the reallocation of purchasing power over time, formalized through the saving-investment identity (Obstfeld and Rogoff 1995). Gross sectoral positions carry information beyond what net flows reveal (Obstfeld 2012), and the deepening of monetary and financial claims relative to activity is itself a structural indicator (Goldsmith, Lipsey, and Mendelson 1963).

Because the broadest layers include instruments issued outside the banking system, the aggregates widen the lens beyond deposits alone, and elevated system-wide liquidity conditions the path of the subsequent cycle (Lane and Milesi-Ferretti 2018).

For market participants the monetary aggregates are a macro-liquidity overlay for tactical positioning, capturing the money and near-money balances that ultimately fund asset demand across the circular flow of payments (Copeland 1952). Broad-money growth linked to crisis frequency raises the probability of disorderly adjustment and informs credit spreads across bank and non-bank exposures (Schularick and Taylor 2012). The crisis-risk pricing implied by the long-run sample lets desks calibrate tail scenarios for liquidity-sensitive portfolios (Jordà, Schularick, and Taylor 2017).

The layer that a balance occupies matters for pricing, since a liquidity-weighted view of the components separates transaction balances from yield-bearing near-money more sharply than a simple sum (Barnett 1980; Diewert 1976). The interest forgone on money balances anchors the cash-flow sensitivity of holders to policy-rate moves (Fisher 1930).

Liquidity stocks must be read with attention to valuation and measurement, cautions raised by the position-measurement and return-measurement literature (Gourinchas and Rey 2007a; Curcuru, Dvorak, and Warnock 2008), and this caveat generalizes to all gross positions (Obstfeld 2012).

The sectoral balance sheets and the gross-position estimation frame the exposures that lenders, money-market investors, and macroprudential overseers monitor when sizing liquidity and funding risk (Goldsmith, Lipsey, and Mendelson 1963; Lane and Milesi-Ferretti 2007).

KRL is a seasonally-adjusted-at-source broad-liquidity aggregate, and its published log-level is the statistical object, expected to be integrated of order one. The sample is 271 monthly observations spanning 2003-10 to 2026-04, and the unit-root triplet is fitted on the log-level with a constant and trend.

The augmented Dickey-Fuller regression of Dickey and Fuller (1979), in the lag-augmented form of Said and Dickey (1984), fails to reject the unit root at p = 0.988, the nonparametric test of Phillips and Perron (1988) concurs at p = 0.971, and the stationarity-null test rejects trend stationarity at p < 0.01 (Kwiatkowski et al. 1992), so all three procedures agree on a clean I(1) classification. The GLS-detrended escalation of Elliott, Rothenberg, and Stock (1996) is reserved for ambiguous outcomes under the house protocol and is not invoked on this clean reading.

Because the log-level is I(1), the portmanteau and the break search are run on its first difference, since a level portmanteau reads the near-unit autocorrelation of a stochastic trend and a level mean-break search spuriously segments it (Perron 1989; Hamilton 2018; Bai and Perron 1998). The portmanteau statistic of Ljung and Box (1978), refining the form of Box and Pierce (1970), rejects the white-noise null on the differenced series, with Q = 27.63 at lag 12 (p = 0.006) and Q = 38.79 at lag 24 (p = 0.029), and the multiple-break procedure computed by the dynamic-programming algorithm of Bai and Perron (2003) finds no break in the mean of the differenced series.

The seasonal battery is run with the interpretation that any absence of seasonality reflects the source adjustment rather than an inherent property. The seasonal-unit-root test of Hylleberg et al. (1990), with the monthly mechanics of Beaulieu and Miron (1993), rejects unit roots at the seasonal frequencies at p = 0.000, and the seasonal dummies are not jointly significant at p = 0.065, so no deterministic seasonality survives at source. The seasonal portmanteau rejects at p = 0.000 on the differenced series, which reflects residual serial dependence at the seasonal lags rather than a seasonal unit root or a deterministic seasonal pattern the source adjustment failed to remove. This stationary-seasonality reading is consistent with Canova and Hansen (1995) and with the seasonal-adjustment literature (Ghysels and Osborn 2001).