Douglas Roberts Dimick, J.D.

Foreign Expert Technology and Economics Certified,

Hubei Province,

PRC AmShell Ltd.

Horsetracks or market exchanges, I am reminded here of my early statement of theory research, developing my “quantitative relativity” for SMART, concerning benchmarks…

On March 30, 2006, I moved to China for two reasons. Primary was to complete coding of SMART (securities market automated relativity trading). Secondary was to research and identify locations for hedge fund formation — hence the current book project of the past two years, titled Foreign Capital Investment Banking for China.

During my first year here, I taught each of the three (primary, undergraduate, graduate) levels to learn about Chinese thinking regarding business and social order. In this past year, I have taught one two-month corporate and two three-week test training seminars, all with my Dao Ge (or Doug) English, which I developed based on my limited study of Shaolin.

I have taken some pictures during my two years in China.

Meanwhile, while coding SMART, I also met a black box contractor from London now living here in Wuhan, China. He had married a Chinese girl and then moved the family and his business here. Smart guy… I learned loads during one meeting with him.

His discussion about devising state machines for regulating energy systems operating among diversely located commercial buildings got me thinking about relative implied volatility arbitrage, specifically benchmarking.

In the design of my “quantitative relativity” methodology for SMART, I have focused on integrating varied markets (e.g., equities, indexes, options, futures, commodities, and Forex) for strategy design and engineering of stochastic indicators and functions.

To simplify this discussion, just think of corresponding inter- and intra-relationships between the studies of math and law. The issue: which “ought” — thus a philosophical query — to govern architecture and engineering during the course of constructing and operating program trading systems?

On New Years Eve 2001 at Palm Beach Polo and Country Club, a high-frequency trader suggested I look into formation of a hedge fund. One month later, I met another local trader (dual masters in mathematics and finance) who posited that the Theory of Relativity applied to electronic market exchange systems. We collaborated for six months and then parted, irrevocably conflicted concerning (a) the utility of “batching” and (b) application of indexing for parallel modeling of variable integration for indicator and function architecture.

COMMENT: Both of these issues touched upon a subsequent study, Relative Implied Volatility Arbitrage and Joint-Efficiency of Index Options Markets. Based on FTSE and DAX equity option index markets, “abnormal returns” (similar to those of mine with batching and indexing) appeared with “contemporaneous trading volume and lagged returns.”

Design of a simple no-arbitrage barrier to identify significant mispricings may determine statistical arbitrage trades. However, “benchmark issues for modeling escape may escape mathematical quantification.” For instance, as with my indexing research, the referenced study found that “two options markets are not jointly efficient with implications for multivariate risk estimation and volatility spillover.”

During R&D on SMART and as a “rules based” architect of program trading, I limited my mathematical analysis here to the conclusions of then contemporary research: see Ammann, Manuel and Herriger, Silvan, Relative Implied Volatility Arbitrage with Index Options (May 2001) ; University of St. Gallen, Department of Economics.

The “math” of benchmarking for option arbitrage (e.g., Black-Scholes) appeared to me (then and now) synonymous with my early modeling conclusions about batching and index-based parallel indicators. To wit: “delta is not a linear function of the underlying price given delta-curvilinear properties,”… thus traders having to re-hedge for a delta-neutral strategy (or gamma trading). Also see Javaheri, Alireza, Inside Volatility Arbitrage, The Secrets of Skewness (2005).

As a result, econometric models (for math purposes) “must simplify market assumptions that are not true in practice.” Example: “market prices are continuous and delta adjustments can be continuous and distributed in lognormal fashion”; actually, markets gap, particularly during periods of market stress, thereby skewing delta adjustments.

My conclusion — then and now? I recently read that a noted arb-academic was quoted to confess… “At present, I don’t know of any good benchmarks.”

Therefore, if mathematical quantification only reshapes (or reformulates) the issue (or parameters for delta [benchmark], theta [decay], and gamma [rent] computations), then deduction indicates that a resolution may be found in the physics (or laws) of those market anomalies which preclude or skew the modeling of market efficiencies (and inefficiencies).

With this analysis and distinguishing, I designed and am now coding SMART based on this formulation of “quantitative relativity,” whereby benchmarking is replaced by doctrinal states governing present-past output generation as opposed to past-present-future quantifications.

Only a JD among a panoply of PhD’s, I posit that my “quantitative relativity” methodology presents a rules-based paradigm that may provide an ecological (i.e., balancing) influence among the “quant” dominated program trading establishment.

As I grew up on a horse farm in Maine, and having played a little bit of polo over the years, I learned that, although there are two sides of a horse, emphasis is on not taking a position at the wrong end at the wrong time. We might consider this analogy given issues attendant with efforts to benchmark (or shall I say “setting the odds” for) world electronic market exchanges.


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