Economics of Markets: Neoclassical Theory, Experiments, and Theory of Classical Price Discovery

197 Pages·2022·5.363 MB·English

by  Sabiou M. Inoua, Vernon L. Smith

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
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Acknowledgments
For thoughtful and constructive reviews of our papers or chapters, we
thank anonymous reviewers of our chapters and papers. We also benefited
from many discussions with faculty and seminar participants at ESI and
we thank them all. Chapman University, and ESI in particular, is hosting
one of the authors as visiting research associate and we are grateful to the
many people who helped, one way or another, in this process; we also
thank the Koch foundation for its financial support.
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
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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