Finance

Research articles · Dec 2, 2024

Modelling Financial Derivatives Markets in a Firm Based Evolutionary Macro Model (MOSES)-On the market integration of computing, communications, and financial services

Gunnar Eliasson, Erol Taymaz

Industrial Economics/Dynamics, Kungliga Tekniska Högskolan (KTH), Stockholm, Sweden; Middle East Technical University (METU), Ankara, Turkey

Volume 17(2) Summer 2024

Cite as: G. Eliasson, E. Taymaz; 2024; Modelling Financial Derivatives Markets in a Firm Based Evolutionary Macro Model (MOSES)-On the market integration of computing, communications, and financial services; International Journal of Microsimulation; 17(2); 233–278. doi: 10.34196/ijm.00298

Abstract

The evolutionary firm based model MOSES of an Experimentally Organized Economy (EOE) is introduced and then outfitted with increasingly more sophisticated financial markets that take us from a stylized industrial banking system (standard in the model), to a (capital) market oriented monetary system, and then on to a securitized financing system where options and market derivatives play a role in allocating financial resources. We study the consequences for long term economic growth of introducing different financial market regimes.

The markets of the financial system and those of the real economy are integrated within the business plans of model firms. The immense micro to macro complexity thereby created reduces the transparency of the economic environment of all actors in the model, including Government as a policy maker, to a fraction of what is needed for informed decisions. The policy maker thereby loses its presumed information advantage of economy wide overview and becomes one bounded rational actor among all other ignorant actors. Because of the strong leverage on the entire economy of its actions the competence and information demands on Government, when conducting policy, however, becomes far more exacting than what is required of any other actor.

When Government engages in ambitious policy making its interventions in markets affect the behavior of all other actors. The entire market regime is therefore affected to the extent that the Central Government often cannot possibly be in control of what it is doing, especially when it comes to the long run consequences of its actions, and therefore often would do better by taking it easy (Eliasson and Taymaz, 1992). We find that financial markets matter for long term economic development and that seemingly insignificant respecifications of the financial market regime can cause considerable differences in long term GNP growth outcomes, amounting to as much as a difference of 1.5 percentage points per annum and a per capita GNP, that is up to 2.5 times larger on the 60 year horizon than in the worst scenario.

The capital market regime is found to outperform the industrial banking system. When introducing a crudely modeled securitized system (call options), the only one our computer system could handle, financial market transactions were, however, speeded up to the extent that resource allocations worsened. Mixing the capital market regime with the simple securitized system generally lowered long run economic growth. Adding options not only increased the speed of arbitrage, destabilizing fundamentals, but also made expectations unreliable and raised the exit rate, notably by making manufacturing firms lose money on their financial investment portfolios. The results on options cannot however be generalized beyond the very specific experimental designs implemented, but they indicate that speeded up market arbitrage, which is what financial derivatives are all about, has mixed economy wide consequences, that may be long term negative in an economy that is not otherwise diversified and robust. While the simulation analysis of the markets for derivates in the MOSES model is still too primitive to allow any empirical generalizations, the results so far should be taken seriously as indicative of possible surprise dynamics that makes it even more important to conduct more inquiries of this kind.

A final methodological note. The excessive demands on computer capacity of the options experiments have prevented us from testing the results for robustness (as for instance in Eliasson and Taymaz, 2000) by running a whole spectrum of experiments with small changes each time in the four different blocs where stochastic specifications are used. The non-linear specifications of the model often generate large differences in very long run outcomes when the stochastic seeds are changed marginally. Hence, the results to be reported should be regarded as explorative, suggesting new credible hypotheses, rather than empirically calibrated results, and also telling what kind of future analyses the new expanded model is capable of.

This article contains 1 figure and 6 tables.

Figures

Figure 1. A) A Salter Ranking of Rates of Return to Capital in Swedish Manufacturing 1983 and 1990. Note: The figure ranks nominal rates of return over the nominal market interest rate across Swedish manufacturing firms 1983 and 1990. The vertical columns show the position of the same firm each year. The width of the column measures the size of the capital of the firm in percent of that for all manufacturing. Its rate of return has increased between 1983 and 1990, but it has lost in ranking. Its relative size has increased between 1983 and 1990. Source: Eliasson (1991a) and Albrecht et al. (1992): Moses Database.

Tables

Table 1. The four mechanisms of schumpeterian creative destruction and economic growth
1. Entrepreneurial competition (entry) enforces
2. Reorganization of incumbent agents and/or
3. Rationalization, or
4. Death (exit)

Source: Eliasson (1993) and Eliasson (1996a).

Table 2. A break down of economic systems effects
1. Rationalization of information processing given the manufacturing and distribution structure (“doing the same thing but faster”)
2. Reorganizing information and communications flows, given manufacturing and distribution structures (e.g. through ”speeding up inventory turnover”)
3. Optimizing manufacturing and distribution flows over a given structure
4. Reorganizing physical flows to exploit information and communications technology (dynamic systems effects)
5. Simultaneous reorganization of physical and information and communications flows (Integrated production)

Source: (Eliasson, 1996c).

Table 3. Outcomes of 60-year experiments
ExperimentGDPNew firmsExitsNumber of firms on horizon
E0000(7165)7164(132)132(93)93(264)264
E0010(7922)6816(126)112(98)91(253)246
E0100(8579)7779(117)123(105)99(237)249
E0110(10001)7978(111)125(99)105(237)245
E1000(5923)9098(126)118(137)197(214)146
E1010(6113)4017(116)105(133)145(208)185
E1100(5927)5097(?)128(?)231(?)122
E1101(?)7980(156)132(202)206(179)151
E1110(5193)3624(130)129(100)231(255)123
E1111(?)5803(?)114(?)127(?)212
E1200(4446)4746(137)170(230)199(132)196
E1210(5955)4498(129)124(143)200(211)149
E1001(9728)8721(124)122(141)110(208)237
E1011(6394)5704(120)119(102)88(243)256
E1201(6736)7932(122)123(130)120(217)228
E1211(5159)5422(125)117(124)133(226)209
E2000(?)4969(?)110(?)242(?)93
E2010(?)4487(?)150(?)288(?)87
E2011(?)5545(?)121(?)160(?)186
E2001(?)7009(?)142(?)169(?)198
E2100(?)4191(?)133(?)253(?)105
E2200(4136)4143(154)158(277)280(102)103
E2110(?)4532(?)154(?)266(?)113
E2210(3601)4953(192)188(303)264(124)149
E2101(?)6092(?)123(?)155(?)193
E2201(5636)6689(138)145(191)187(172)183
E2111(?)5691(?)114(?)191(?)148
E2211(6220)5518(137)157(223)214(139)168

Initial number of firms, always = 225.

Note: Numbers within brackets are the results from same experiments conducted in Taymaz (1999). The two sets of experiments are identical except for the initial state year which has been moved up a couple of years in this paper, to allow statistical errors in the initial state (because of taxonomic differences between Planning Survey data and National Accounts data in the new C& C industry (Eliasson and Johansson, 1999 to sort themselves out in the markets of the MOSES model economy. For how to interpret these statistical error problems, see Albrecht (1992), and the text below. Apparently, they affect the relative outcomes of the experiments.

Table 4. Stochastic R&D Choices
ExperimentGNPEnterExitNumber of firms on the horizon
E(0000)184214393275
E(1201)4955121217129
E(2201)3297135169191

Initial stock of firms, always = 225.

Table 5. Insider Experiments
ExpNo insiderInsider rob. 1%Insider prob. 10%
GNPEntryExitGNPEntryExitGNPEntryExit
E1201368858317844294121814287423180
E1200282848522627545872453499581241
E1211360942417031614821914899483194
E2201278949623835465002853086528255
E1101470439814047346111173973373136
E10014374414994430480122459746691
ExperimentGNPNew firmsExits
E0000716513293
E0010792212698
E01008579117105
E01101000111199
E10005923126137
E10106113116133
E11005927156202
E11105193130100
E12004446137230
E12105955129143
E10019728124141
E10116394120102
E12016736122130
E12115159125124
E22004136154277
E22103601192303
E22015636138191
E22116220137223