Front office quantitative analyst In
sales and trading, quantitative analysts work to determine prices, manage risk, and identify profitable opportunities. Historically this was a distinct activity from
trading but the boundary between a
desk quantitative analyst and a quantitative trader is increasingly blurred, and it is now difficult to enter trading as a profession without at least some quantitative analysis education. Front office work favours a higher speed to quality ratio, with a greater emphasis on solutions to specific problems than detailed modeling. FOQs typically are significantly better paid than those in back office, risk, and model validation. Although highly skilled analysts, FOQs frequently lack software engineering experience or formal training, and bound by time constraints and business pressures, tactical solutions are often adopted. Increasingly, quants are attached to specific desks. Two cases are:
XVA specialists, responsible for managing
counterparty risk as well as (minimizing) the capital requirements under
Basel III; and
structurers, tasked with
the design and manufacture of client specific solutions.
Quantitative investment management Quantitative analysis is used extensively by
asset managers. Some, such as FQ,
AQR or
Barclays, rely almost exclusively on quantitative strategies while others, such as
PIMCO,
BlackRock or
Citadel use a mix of quantitative and
fundamental methods. One of the first quantitative investment funds to launch was based in
Santa Fe, New Mexico and began trading in 1991 under the name
Prediction Company. By the late-1990s, Prediction Company began using
statistical arbitrage to secure investment returns, along with three other funds at the time,
Renaissance Technologies and
D. E. Shaw & Co, both based in New York. Machine learning models are now capable of identifying complex patterns in financial market data. With the aid of artificial intelligence, investors are increasingly turning to deep learning techniques to forecast and analyze trends in stock and foreign exchange markets. See .
Library quantitative analysis Major firms invest large sums in an attempt to produce standard methods of evaluating prices and risk. These differ from front office tools in that
Excel is very rare, with most development being in
C++, though
Java,
C# and
Python are sometimes used in non-performance critical tasks. LQs spend more time modeling ensuring the analytics are both efficient and correct, though there is tension between LQs and FOQs on the validity of their results. LQs are required to understand techniques such as
Monte Carlo methods and
finite difference methods, as well as the nature of the products being modeled.
Algorithmic trading quantitative analyst Often the highest paid form of Quant, ATQs make use of methods taken from
signal processing,
game theory, gambling
Kelly criterion,
market microstructure,
econometrics, and
time series analysis.
Risk management This area has grown in importance in recent years, as the credit crisis exposed holes in the mechanisms used to ensure that positions were correctly
hedged; see
FRTB, . A core technique continues to be
value at risk - applying both
the parametric and
"Historical" approaches, as well as
Conditional value at risk and
Extreme value theory - while this is supplemented with various forms of
stress test,
expected shortfall methodologies,
economic capital analysis,
direct analysis of the positions at the
desk level, and,
as below, assessment of the models used by the bank's various divisions.
Model validation Model validation (MV) takes the models and methods developed by front office, library, and modeling quantitative analysts and determines their validity and correctness; see
model risk. The MV group might well be seen as a superset of the quantitative operations in a financial institution, since it must deal with new and advanced models and trading techniques from across the firm. Post crisis, regulators now typically talk directly to the quants in the middle office - such as the model validators - and since profits highly depend on the regulatory infrastructure, model validation has gained in weight and importance with respect to the quants in the front office. Before the crisis however, the pay structure in all firms was such that MV groups struggle to attract and retain adequate staff, often with talented quantitative analysts leaving at the first opportunity. This gravely impacted corporate ability to manage
model risk, or to ensure that the positions being held were correctly valued. An MV quantitative analyst would typically earn a fraction of quantitative analysts in other groups with similar length of experience. In the years following the crisis, as mentioned, this has changed.
Quantitative developer Quantitative developers, sometimes called quantitative software engineers, or quantitative engineers, are computer specialists that assist, implement and maintain the quantitative models. They tend to be highly specialised language technicians that bridge the gap between
software engineers and quantitative analysts. The term is also sometimes used outside the finance industry to refer to those working at the intersection of
software engineering and
quantitative research.
Hypothesis of non-ergodicity of financial markets The nonergodicity of financial markets and the time dependence of returns are central issues in modern approaches to quantitative trading. Financial markets are complex systems in which traditional assumptions, such as independence and normal distribution of returns, are frequently challenged by empirical evidence. Thus, under the non-ergodicity hypothesis, the future returns about an investment strategy, which operates on a non-stationary system, depend on the ability of the algorithm itself to predict the future evolutions to which the system is subject. As discussed by Ole Peters in 2011, ergodicity is a crucial element in understanding economic dynamics, especially in non-stationary contexts. Identifying and developing methodologies to estimate this ability represents one of the main challenges of modern quantitative trading. In this perspective, it becomes fundamental to shift the focus from the result of individual financial operations to the individual evolutions of the system. Operationally, this implies that clusters of trades oriented in the same direction offer little value in evaluating the strategy. On the contrary, sequences of trades with alternating buy and sell are much more significant. Since they indicate that the strategy is actually predicting a statistically significant number of evolutions of the system. ==Mathematical and statistical approaches==