In recent years, the insurance industry has gradually come into 2.0 era, the essence of online insurance relies on the new financial technology like big data and Blockchain, which can promote the rapid development of insurance Internet +, realizing the processes of Fintech automatic insurance, risk pricing and claim settlement. Therefore, Blockchain will play a […]
This short note draws some connections between Mandelbrot‗s empirical legacy, and the interdisciplinary work that followed in finance. Much of this work is now labeled econophysics, but some has always been more in the realm of economics than physics. In a few areas the overlap is even becoming quite complete as in market microstructure. I will also give some ideas about the various successes and failures in this area, and some directions for the future of agent- based modeling in particular.
LeBaron, B. Eur. Phys. J. Spec. Top. (2016) 225: 3243. doi:10.1140/epjst/e2016-60123-4
I will start a new series of posts here in Insight Corporation. It will feature a review of papers about Financial Engineering and Risk Management.
The first paper in this new series is about option pricing, a central Financial Engineering topic. This series will mostly feature posts in the leading publication in this field Risk.net. From now and then I will also publish some other relevant paper reviews from other source as well, and if the occasion is the right one.
The main points and abstract follows. Further download and reading of the paper is fully recommended:
- We present an error analysis in using Fourier methods for pricing European options when the underlying asset follows an exponential Levy process.
- The derived bound is minimised to achieve optimal parameters for the numerical method.
- We propose a scheme to use the error bound in choosing parameters in a systematic fashion to meet a pre-described error tolerance at minimal cost.
- Using numerical examples, we present results comparable to or superior to relevant points of comparison
We provide a bound for the error committed when using a Fourier method to price European options, when the underlying follows an exponential Lévy dynamic. The price of the option is described by a partial integro-differential equation (PIDE). Applying a Fourier transformation to the PIDE yields an ordinary differential equation (ODE) that can be solved analytically in terms of the characteristic exponent of the Lévy process. Then, a numerical inverse Fourier transform allows us to obtain the option price. We present a bound for the error and use this bound to set the parameters for the numerical method. We analyze the properties of the bound and demonstrate the minimization of the bound to select parameters for a numerical Fourier transformation method in order to solve the option price efficiently.
Featured Image: Black-Scholes Model Wiki at OptionTradingpedia.com
Mintegration with this interesting post on Evolutionary Optimization applied to Portfolio Management :
STRATEGY REPLICATION – EVOLUTIONARY OPTIMIZATION BASED ON FINANCIAL SENTIMENT DATA
Wow, I enjoyed replicating this neatly written paper by Ronald Hochreiter.
Ronald is an Assistant Professor at the Vienna University of Economics and Business (Institute for Statistics and Mathematics).
In his paper he applies evolutionary optimization techniques to compute optimal rule-based trading strategies based on financial sentiment data.
- Title: Computing trading strategies based on financial sentiment data using evolutionary optimization
- Author: Ronald Hochreiter
- Book: Mendel 2015, Recent Advances in Soft Computing,
The series “Advances in Intelligent Systems and Computing”, Volume 378 2015, Springer
- Sources: doi: http://dx.doi.org/10.1007/978-3-319-19824-8_15
The evolutionary technique is a general Genetic Algorithm (GA).
The GA is a mathematical optimization algorithm drawing inspiration from the processes of biological evolutionto breed solutions to problems. Each member of the population (genotype) encodes a solution (phenotype) to the problem. Evolution in the population of encodings is simulated by means of evolutionary processes; selection, crossover and
View original post 428 more words
With courtesy from Daily Fintech I kindly share the whole recent post on the Banking API Fintech Genome project:
A major theme that we track on Daily Fintech is the “programmable bank”, how innovation is enabled by APIs that abstract complex layers of utility services so that innovators can focus on “rebundling” (creating new UX based on integrating unbundled best of breed services). We are also big believers in the open source model of knowledge co-creation that was the concept behind Fintech Genome. This wiki on Fintech Genome is where those two threads come together. You can see the chronological story, starting with a simple question, followed by the community kicking into contribute more knowledge. Help us get the job complete. You can contribute via comments and then somebody edits the wiki. Or, Once you reach Level 1 on Fintech Genome, you can directly edit a wiki and that is obviously more efficient. (Read this to understand Levels on Fintech Genome. Don’t worry it is very easy to get to Level 1).
Why Banking APIs matter to the acceleration of Fintech innovation
Banks that want to transform into Fintech-like transaction banking platforms do so by exposing all the services they offer via APIs. This enables a lot of innovation because Banks or Fintechs operating higher up the stack do not need to all build/buy this utility layer.
Current Snapshot of the Banking API Wiki
This is a work in progress. Go to this thread on the Fintech Genome to see the current state.
Tools & Platforms:
F2 Framework https://www.openf2.org
Plaid – https://plaid.com/ – a tool that enables applications to connect with user’s bank accounts. Should get a big boost from PSD2.
3rd party providers
Coverage: UK, Poland, Czech Republic, Slovakia, Spain, Portugal, Mexico, Brazil, Russia.
Banks (listing by HO location, most operate in multiple countries).
France: BNP Paribas Open Bank Project11
Coverage (BNP Paribas Group Banks only): UK, US, Poland, France, France, Turkey, Italy
Spain: BBVA Compass
UK: Mondo https://getmondo.co.uk/docs/
Singapore: OCBC https://api.ocbc.com/store/
Wealth Management Focus
How you can contribute
On Daily Fintech, we publish snapshots of what we learn on the Fintech Genome. You can stay at this summary layer or dive deep into the subject by going into the Fintech Genome.
You can contribute in three ways depending on how much you want to get involved.
Comment here on Daily Fintech. No need to register (Daily Fintech is ungated). However if you want to have your contributions recognised within the global Fintech community, you may want to take a minute to register with your real ID on Fintech Genome and contribute there.
Comment on this thread on Fintech Genome. The chronological contributions are always visible here. So your knowledge in this area is recognised by Fintech leaders.
Directly edit the wiki that you see on this thread. Scroll down till you see the wiki and then click on the wiki icon. This is the most efficient way to aggregate knowledge that we know of (as Wikipedia has demonstrated).
Fintech thought-leaders who contributed to the Banking API knowledge include:
Two senior leaders of JPMorgan’s blockchain project, Juno, have left the firm to found their own blockchain startup. Will Martino, a developer, and Stuart Popejoy, previously the executive director within the bank’s new product development division, started Kadena.io in June, the two confirmed to Quartz. Popejoy had been the head of Juno, and Martino was…
From the page on the OpalesqueTV youtube channel. This is video from 2010, but I thought it still carries relevance and long-term logic:
Opalesque’s first CAMPUS series features Melvyn Teo, Associate Professor of Finance at Singapore Management University. Teo is also Director of the BNP Paribas Hedge Fund Centre at the Singapore Management University.
In this Opalesque CAMPUS, Teo shares his findings that hedge funds charging lower than average performance fees tend to have a higher liquidity risk, which will translate into problems for investors, when they try to pull money out of the fund. One example of such a problem is the “return impact” of redemptions, when the return of the fund tends to fall in the next month.
Teo also gives specific recommendations what investors can do to better understand the liquidity risk a fund may carry.