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THE ROLE OF INFORMATION TECHNOLOGY

admin — Mon, 15 Aug 2011 15:49:26 +0000

Advances in information technology are behind the widespread adoption of modeling in finance. The most important advance has been the enormous increase in the amount of computing power, concurrent with a steep fall in prices. Government agencies have long been using computers for economic modeling, but private firms found it economically justifiable only as of the 1980s. Back then, economic modeling was considered one of the “Grand Challenges” of computational science.
In the late 1980s, firms such as Merrill Lynch began to acquire super- computers to perform derivative pricing computations. The overall cost of these supercomputing facilities, in the range of several million dollars, limited their diffusion to the largest firms. Today, computational facilities ten times more powerful cost only of a few thousand dollars.
To place today’s computing power in perspective, consider that a 1990 run-of-the-mill Cray supercomputer cost several million U.S. dollars and had a clock cycle of 4 nanoseconds (i.e., 4 billionths of a second or 250 million cycles per second, notated as 250 MHz). Today’s fast laptop computers are 10 times faster with a clock cycle of 2.5 GHz and, at a few thousand dollars, cost only a fraction of the price. Supercomputer performance has itself improved significantly, with top computing speed in the range of several teraflops7 compared to the several mega- flops of a Cray supercomputer in the 1990s. In the space of 15 years, sheer performance has increased 1,000 times while the price-performance ratio has decreased by a factor of 10,000. Storage capacity has followed similar dynamics.
The diffusion of low-cost high-performance computers has allowed the broad use of numerical methods. Computations that were once per- formed by supercomputers in air-conditioned rooms are now routinely performed on desk-top machines. This has changed the landscape of financial modeling. The importance of finding closed-form solutions and the consequent search for simple models has been dramatically reduced. Computationally-intensive methods such as Monte Carlo simulations and the numerical solution of differential equations are now widely used. As a consequence, it has become feasible to represent prices and returns with relatively complex models. Nonnormal probability distributions have become commonplace in many sectors of financial modeling. It is fair to say that the key limitation of financial econometrics is now the size of available data samples or training sets, not the computations; it is the data that limits the complexity of estimates.
Mathematical modeling has also undergone major changes. Techniques such as equivalent martingale methods are being used in derivative pricing and cointegration , the theory of fat-tailed processes, and state-space modeling (including ARCH/GARCH and stochastic volatility models) are being used in econometrics.
Powerful specialized mathematical languages and vast statistical software libraries have been developed. The ability to program sequences of statistical operations within a single programming language has been a big step forward. Software firms such as Mathematica and Math- works, and major suppliers of statistical tools such as SAS, have created simple computer languages for the programming of complex sequences of statistical operations. This ability is key to financial econometrics which entails the analysis of large portfolios.8
Presently only large or specialized firms write complex applications from scratch; this is typically done to solve specific problems, often in the derivatives area. The majority of financial modelers make use of high-level software programming tools and statistical libraries. It is difficult to overestimate the advantage brought by these software tools; they cut development time and costs by orders of magnitude.
In addition, there is a wide range of off-the-shelf financial applications that can be used directly by operators who have a general under- standing of the problem but no advanced statistical or mathematical training. For example, powerful complete applications from firms such as Barra and component applications from firms such as FEA make sophisticated analytical methods available to a large number of professionals.
Data have, however, remained a significant expense. The diffusion of electronic transactions has made available large amounts of data, including high-frequency data (HFD) which gives us information at the transaction level. As a result, in budgeting for financial modeling, data have become an important factor in deciding whether or not to under- take a new modeling effort.
A lot of data are now available free on the Internet. If the required granularity of data is not high, these data allow one to study the viability of models and to perform rough tuning. However, real-life applications, especially applications based on finely grained data, require data streams of a higher quality than those typically available free on the Internet.

FINANCIAL ENGINEERING IN HISTORICAL PERSPECTIVE

admin — Fri, 05 Aug 2011 15:45:57 +0000

In its modern sense, financial engineering is the design (or engineering) of contracts and portfolios of contracts that result in predetermined cash flows contingent to different events. Broadly speaking, financial engineering is used to manage investments and risk. The objective is the transfer of risk from one entity to another via appropriate contracts. Though the aggregate risk is a quantity that cannot be altered, risk can be transferred if there is a willing counterparty.
Financial engineering came to the forefront of finance in the 1980s, with the broad diffusion of derivative instruments. However the concept and practice of financial engineering are quite old. Evidence of the use of sophisticated cross-border instruments of credit and payment dating from the time of the First Crusade (1095–1099) has come down to us from the letters of Jewish merchants in Cairo. The notion of the diversification of risk (central to modern risk management) and the quantification of insurance risk (a requisite for pricing insurance policies) were already understood, at least in practical terms, in the 14th century. The rich epistolary of Francesco Datini, a 14th century merchant, banker and insurer from Prato (Tuscany, Italy), contains detailed instructions to his agents on how to diversify risk and insure cargo.5 It also gives us an idea of insurance costs: Datini charged 3.5% to insure a cargo of wool from Malaga to Pisa and 8% to insure a cargo of malmsey (sweet wine) from Genoa to Southampton, England. These, according to one of Datini’s agents, were low rates: He considered 12–15% a fair insurance premium for similar cargo.
What is specific to modern financial engineering is the quantitative management of uncertainty. Both the pricing of contracts and the optimization of investments require some basic capabilities of statistical modeling of financial contingencies. It is the size, diversity, and efficiency of modern competitive markets that makes the use of modeling imperative.

CEMC

admin — Tue, 05 Jul 2011 17:02:51 +0000

Most of the currency crises of the 1990s happened against soft currency pegs. In the wake of the Asian currency crisis, I made a stab at creating a model which focused on how exchange rates typically performed in the run to and after the break down of a pegged exchange rate regime. For good or ill, the Classic Emerging Market Currency Crisis (CEMC) model was the result. To be sure, the title is a mouthful, but for the most part it tells the story of most emerging market currency crises during the 1990s and thus may serve as a useful barometer should any such crises be experienced going forward. This can be broken down into five phases during which the currency crisis takes place:
1. Capital inflows and real currency appreciation
2. Fundamental deterioration and inevitable currency collapse
3. A positive current account swing and a liquidity-based rally
4. The economy hits bottom; a period of consolidation
5. The fundamental rally
A key aspect of these crises was the relationship between the real exchange rate and the external balance. In floating exchange rate regimes, economic imbalances are usually smoothed out over time. In pegged exchange rate regimes, they can build up to unsustainable levels, thus forcing the collapse of the exchange rate peg, if not checked by changes in macroeconomic policy.

EXCHANGE RATE REGIMES

admin — Sat, 02 Jul 2011 17:00:59 +0000

The signal grid and the risk appetite indicator should be the two main tools of the currency strategist. There are however other aspects of the currency markets that still have to be considered. For instance, the type of exchange rate regime is an important consideration as it can have a significantly different impact on the economy depending on what type of regime is being used. The latest fashion within the official community in Washington DC is to advocate the so-called “bi-polar” world of exchange rates, supporting the idea that in a world of free capital markets only the hardest currency peg or a completely free-floating currency are appropriate, and that anything else is unsustainable. It seems likely that this will ultimately give way to a new trend, whereby there are significantly less currencies, all of which are freely floating. As far as currency market practitioners are concerned, key questions that a corporate executive or an investor must ask if they are exposed to a currency peg regime are:
Does the currency peg itself contribute to macroeconomic stability?
What is the degree of participation in global capital flows of the country concerned?
Is the currency peg at the right value?
Most soft or semi-pegged exchange rate regimes have gone, voluntarily or otherwise. If you have currency exposure to a pegged exchange rate regime and you are concerned about currency risk, the rule to remember is that you should hedge when the market has no interest in hedging and thus when risk premiums are low. By the time the market is keen to hedge currency risk, liquidity and price conditions will have deteriorated and it will be too late to obtain anything but the most expensive of currency protection.
The beauty of freely floating exchange rates is that they act as a self-adjusting mechanism, transmitting changes in fundamental dynamics across the economy. In that sense, a freely floating exchange rate regime cannot be defeated, unlike a pegged exchange rate regime. That said, they can still be highly volatile at times.

RISK APPETITE INDICATORS

admin — Fri, 01 Jul 2011 17:00:09 +0000

When there is no clear, unequivocal signal from the signal grid, that is when not all four signals are pointing in the same direction, currency traders and investors can still boost their total return by using a risk appetite indicator to gauge overall market sentiment in terms of “risky” or “safe” assets, both in terms of putting on new positions and in terms of measuring their existing positions. Risk sentiment can be divided up into three levels:
Risk-seeking/stable
Risk-neutral
Risk-aversion/unstable
When the indicator is in risk-seeking or risk-neutral mode, be long a basket of higher carry currencies, either in the developed or emerging markets. Conversely, when it is in risk-aversion mode, obviously having moved there from risk-neutral, cut and reverse the position, going short the carry basket of currencies. Risk appetite has become an increasingly important concept not just because of the need to create more accurate models for forecasting short-term currency moves, but also because the last few years have shown a marked pick-up in cross-asset market volatility. There are several risk appetite indicators created by the private sector for this purpose. Not just currency traders or speculators can use this. A risk appetite indicator can be a crucial tool for corporate Treasurers and institutional investors, not least in providing them with an informed context within which their exposure exists.

THE SIGNAL GRID

admin — Tue, 30 Jun 2009 16:59:24 +0000

The four analytical disciplines of currency economics, flow analysis, technical analysis and long-term valuation which come together to make a currency strategy decision can be expressed in the form of a signal grid. To be sure, this is a very simple model. However, what is important here is having the discipline to create it. Only when all four analytical indicators are reading buy or sell together should one put out an official currency strategy recommendation. Granted, this is still no guarantee of success. It should however have a number of positive effects on one’s trading or analytical performance:
It should eliminate the bias created by relying only on one analytical type
By nature, four buy signals make up a more powerful buy signal than just one
The bottom line — it should improve one’s performance and total returns

LONG-TERM VALUATION

admin — Mon, 29 Jun 2009 16:59:11 +0000

The dividing line between currency economics and long-term valuation analysis is somewhat blurred. There is a difference however and it concerns the time span involved in one’s analysis. The aim of currency economics is to look at the parts of the economy that affect and are affected by the exchange rate, such as the balance of payments and infkation differentials, in order to give an idea about that exchange rate’s current valuation and direction. Long-term valuation models, such as those that focus on REER or FEER, are trying to give a multi-month or more likely a multi-year view of exchange rate valuation. In line with this, the main exchange rate models that focus on long-term valuation are the following:

Purchasing Power Parity
The Monetary Approach
The Interest Rate Approach
The Balance of Payments Approach
The Portfolio Balance Approach

Most of these models focus on the relative price of an asset or good which should over time cause an exchange rate adjustment to restore “equilibrium”.