Economics of Markets Neoclassical Theory, Experiments, and Theory of Classical Price Discovery Sabiou M. Inoua Vernon L. Smith Economics of Markets · Sabiou M. Inoua Vernon L. Smith Economics of Markets Neoclassical Theory, Experiments, and Theory of Classical Price Discovery Sabiou M. Inoua Vernon L. Smith Economic Science Institute Economic Science Institute Chapman University Chapman University Orange, CA, USA Orange, CA, USA ISBN 978-3-031-08427-0 ISBN 978-3-031-08428-7 (eBook) https://doi.org/10.1007/978-3-031-08428-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 This work is subject to copyright. 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In the process of writing this book, we published several papers as articles that we wish to acknowledge, and cite permissions granted for using substantially the same or similar material herein. v Contents 1 Prologue 1 2 Introduction 9 3 Rediscovering Classical Economics in the Laboratory 21 4 Price Formation: Overview of the Theory 39 5 Price Formation: Partial Equilibrium 93 6 Price Formation: General Equilibrium 131 7 Financial Instability: Re-tradable Assets and Speculation 157 Index 183 vii List of Figures Fig. 3.1 Short-Side Rationing and Classical Price Adjustment 29 Fig. 4.1 Distribution of costs and values in Table 4.1 41 Fig. 4.2 Supply and Demand functions for an Isolated Exchange 42 Fig. 4.3 No market-clearing price exists for this supply and demand configuration. The relevant equilibrium concept here is v1, which minimizes overall market imbalance: we shall refer to this more general concept of competitive equilibrium, as the center of value 56 Fig. 4.4 Böhm-Bawerk’s horse market simulated: two-sided (buyer-seller) competition (10 buyers, 8 sellers). (a) Supply and demand functions. (b) Price dynamics specified as pt+1 = pt exp[lambda[D( pt ) − S( pt )] (with lambda = 0.07 arbitrary, but set low enough to have small oscillations, which are inevitable given this approximate specification of competitive dynamics). (c) Price-value distance and information revealed in price. (d) Dynamics of the price-value distance 66 Fig. 4.5 Simulation of a well-behaved non-clearing market studied experimentally (V. L. Smith, 1965). (a) “Swastika” supply and demand configuration. (b) Price dynamics (same as in Fig. 4.4, with lambda = 0.01). (c) Price-value distance, V (p), and information, I (p), revealed in price. (d) Dynamics of the price-value distance 67 Fig. 4.6 Under Short-side rationing, trade volume is maximum when supply equals demand 68 ix x LISTOFFIGURES Fig. 4.7 The basic profitability or affordability indicator function (Left: a(x) = I (c ≤ x). Right: a(x) = I (v ≤ x)) 76 Fig. 5.1 Center of value is graphically the set of prices that maximize the area below excess demand Z . It generalizes the concept of market-clearing price: formally C = [r+,r−], where the endpoints are, the critical points at which Z changes sign 101 Fig. 5.2 Maximum attainable surplus is the hatched area 103 Fig. 5.3 A large market’s competitive dynamics [values and costs from exponential distributions: mean (v) = 5, mean (c) = 3, fraction of demand units = 0.7, H(Z) = 10Z]. (a) Average supply, demand, and number of transactions under short-side rationing. (b) Price trajectories are shown for various initial conditions. (c) Average potential versus actual surplus (the latter calculated under the no-covariance assumption (1.35)). (d) Dynamics of potential versus actual surplus 108 Fig. 5.4 Isolated buyer–seller haggling simulated: (a) Supply and Demand (b) Price dynamics: p0 > vmax means we are modeling the standing ask price. (c) Potential surplus function V (left scale) and information function I (in bits, right scale). (d) Dynamics of V 111 Fig. 5.5 Simulation of a non-clearing market studied in the lab (V. L. Smith & Williams, 1990, Fig. 10, Condition A). (a) “Swastika” supply and demand configuration. (b) Price dynamics. (c) Potential surplus function, V, and value and cost information amount, I, revealed by price (in bits). (d) Dynamics of V 113 Fig. 5.6 English auction simulated. (a) Cumulative distribution of values (5 to 40 by increment of 5) and cost (or seller reserve price, 10). (b) Price dynamics: minimum bid increment βmin= 5. (c) Potential surplus function, V, and Information, I, revealed by price (in bits). (d) Dynamics of the potential surplus, V 116 Fig. 5.7 Multiple-unit English auction simulated (Four Units). (a) Values and cost (seller reserve price). (b) Price dynamics: minimum bid increment βmin=5. (c) Potential surplus function, V, and Information, I, revealed by price (in bits). (d) Dynamics of the potential surplus, V 118 LISTOFFIGURES xi Fig. 7.1 Ford motor company stock: (a) Price; (b) Return (in percent); (c) Cumulative distribution of volatility in log–log scale, and a linear fit of the tail, with a slope close to 3; (d) Autocorrelation function of return, which is almost zero at all lags, while that of volatility is nonzero over a long range of lags (a phenomenon known as volatility clustering) 164 Fig. 7.2 S&P 500 index: (a) Price; (b) Return (in percent); (c) Cumulative distribution of absolute return; (d) Autocorrelation function of return and absolute return 165 Fig. 7.3 US–UK exchange rate: (a) Exchange rate; (b) Return (in percent); (c) Cumulative distribution of absolute return; (d) Autocorrelation function of return and absolute return 166 Fig. 7.4 Bitcoin: (a) Price; (b) Return (in percent); (c) Cumulative distribution of absolute return; (d) Autocorrelation function of return and absolute return 167 Fig. 7.5 A lab asset price volatility (Data source Kirchler and Huber [2009, Market 5]) 168 Fig. 7.6 Big volatility clusters triggered by major events (Crises) 169 Fig. 7.7 A purely speculative asset market model: the simplest case of momentum speculators or RCAR(1) model, with prob(news at time t) = 1, in this simulation; feedback term nt drawn from an exponential distribution with mean 0.55; impact of news is zero-mean Gaussian with standard deviation of 1 175 Fig. 7.8 General model simulated. (a) Price; (b) Return (in percent); (c) Cumulative distribution of volatility in log–log scale, and a linear fit of the tail, with a slope close to 3; (d) Autocorrelation function of return and absolute return 178 List of Tables Table 3.1 Number of contracts above, equal to, or below equilibrium in first (second) experiment 37 Table 4.1 Example of values and costs 41 Table 4.2 Values and costs in Böhm-Bawerk’s horse market 65 xiii