Data processing: financial – business practice – management – or co – Automated electrical financial or business practice or... – Finance
Reexamination Certificate
1993-07-01
2002-09-24
Sough, Hyung-Sub (Department: 2161)
Data processing: financial, business practice, management, or co
Automated electrical financial or business practice or...
Finance
C705S001100, C705S014270, C705S035000, C705S037000
Reexamination Certificate
active
06456982
ABSTRACT:
I. FIELD OF THE INVENTION
The present invention is directed to electrical computers and data processing systems, with applications involving finance. More particularly, the present invention includes an apparatus, along with methods for making and using it, to receive input data (which can represent market data), to process the input data to calculate projected data, and to generate output including the projected data, wherein the processing was first tested by calculating projected test data from input test data and then using the projected test data to derive a portion of the input test data. The projected data can include financial simulations of future market behavior, such as prices, to aid in making financial decisions including transactions, hedging, etc.
II. BACKGROUND OF THE INVENTION
A. Overview
Many mathematical and statistical techniques have been used to estimate the likelihood of future events. Sophisticated techniques include “Random Walk” models which assume that future behavior characteristics will continue as they have in the past. Thus, projections and distributions of projections can be made and, by examining historic data, a statistical level of confidence for the projections can be computed. Such models, which are usually implemented by computer, have been used as the basis for making financial decisions.
To intelligently engage in market transactions—buying or selling a financial product, and even maintaining an investment position—players considering possible future market behavior make projections from present market phenomenon. One aspect of such projections involves market simulations, wherein the future market prices of such financial products as futures, swaps, options, and any other derivative products are randomly generated in great numbers and over chosen future time periods.
A “forward price” is a risk-adjusted future spot price; a “future spot price” is the spot price to be observed at some future time; and the “spot price” is the price for which some asset can be exchanged for money. In case of commodity markets, the ‘asset’ is some commodity; in case of equity markets, the asset is some stock; in case of interest rate markets, the ‘asset’ is some type of a loan or deposit. A financial “derivative” is a financial product having future cash flows, the values of which are derived as functions of future spot prices.
Financial products that commonly use simulation include: for Interest Rate Markets—mortgage-rate contingent derivative products (e.g., derivative products for which future cash flows are derived as functions of future mortgages rates as ‘spot prices’); path-dependent options, swaps, and swaptions (these are derivative products having future cash flows derived as functions of future London Inter Bank Offering Rates as ‘spot prices’; and, counter-party risk exposure calculations (here, the ‘spot prices’ can be any of the foregoing, but are combined with the additional default information of the counterparty); for Commodities—path-dependent options (which are derivative products having cash flows derived as functions of more than one future spot price, and where the future spot price is the price of the commodity at the corresponding future date), swaps and swaptions (these are derivative products having cash flows derived as functions of future spot prices, the spot prices being the commodity prices); and, counter-party-risk exposure calculations (which are functions of commodity spot prices and counterparty risks, but applied to commodity-related products); and for Equities—hedging scenarios (these are cash flows which result from using a particular market hedging strategy, with the cash flows derived as functions specific to the hedging strategy and of future equity prices as the ‘spot prices’), and counter-party-risk exposures.
Simulations can also involve using present information about liquid financial products to predict forward prices and to generate price distributions of various liquid and illiquid financial products. As the number of random numbers increases, the average of simulated changes in prices “converges” toward the drift term, which is defined below.
Prices are typically considered as following “Brownian motion.” According to Brownian motion, a percent change in price depends on a deterministic drift term (i.e., an expected change in price) and a random term (which gives variability to price changes around the expected change in price). The future value of the random term cannot be predicted per se. However, the magnitude of price changes can be measured statistically as the price volatility over some previous time period. Thus, in simulating future prices, many random terms can be generated to build a future price distribution. The greater the number of random numbers generated, the closer the average of the simulated prices represents the drift term and the closer the simulated distribution represents the price volatility about the drift term.
At any point in time, the markets will provide quotes on the spot price and on a series of forward spot prices—the quotes corresponding to a number of different future time periods. A market quote for a forward spot price corresponding to some future time period represents the market's expectation of what the spot price will be at that future time period—adjusted for risk.
The market quotes for the spot price and the forward prices combine to create what is called the “forward price curve.” Very seldom is the forward price curve the same from day to day, and it is the movement of the forward price curve which the simulations attempt to realistically represent.
Simulating methodologies typically use statistical parameters—such as price volatility and other characteristics of pride probability distributions to predict the distributions of various financial derivative product prices. Simulations of the distributions of derivative product future cash-flows can be used to solve a, variety of problems including pricing, hedging analysis, and profit-loss analysis, as set forth below.
1. Pricing
Simulations can be used in pricing financial products, even those which are difficult to price because they are illiquid (i.e., rarely traded) and thus do not have a readily available market price. Such financial products are difficult to price easily or correctly with readily available, simpler pricing techniques.
2. Hedging
Simulations can be a powerful financial tool for hedging analysis or portfolio management. The simulation of the market behavior allows for an analysis of particular hedging scenarios which a firm might consider for managing its exposure to market risks. In comparing different hedging scenarios, one would analyze the standard deviations of the simulated distributions: the smaller the standard deviation, the better is the hedging scenario.
3. Profit/Loss Analysis
Simulations can also be used to simulate the profit and loss distributions of portfolios of derivative products. The generated distribution of the portfolio performance may be used very generally to manage a firm's exposure to the market risks. By incorporating the market risks with the counter-party default risks, simulations can be used to manage the firm's exposure to the counter-party risks. Particular measures of this counter-party risk exposure can be used by the firm to make decisions on when to limit the firm's dealing with some counter-party. For these purposes, one would analyze the “tails” of the distribution curves which would represent unlikely but extreme events.
B. Methods of the Prior Art
Simulation methods are widely used in fields such as physics and finance. Through a method commonly referred to as “Monte Carlo,” a large number of random numbers are generated in simulating random behaviors. All Monte Carlo methods have in common an assumption that random behaviors can be represented by using a Random Walk model. To select a particular Random Walk model, performance of the model is tested by calculating confidence levels from historic data.
Specifically, in finance, Monte Ca
Hewitt II Calvin L.
Sough Hyung-Sub
LandOfFree
Computer system for generating projected data and an... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Computer system for generating projected data and an..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Computer system for generating projected data and an... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2875329