Notebooks for financial economics. Keywords: IPython notebook pandas statistics GDP inflation CPI PCE Fed FRED Ferbus unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency Eurozone euro yen FX USD EUR JPY XAU gold Brent WTI oil Holt-Winters time-series forecasting econometrics
The pre-Xmas decline of over 25% in Bitcoin price in less than 24 hours induced a backwardation in futures pricing relative to spot. Easy to imagine a trading strategy somewhat like a put, given the borrowing rate against such assets.
But is it hard to imagine a call option one-year out at $50,000 strike? Some institution paid close to a million dollars in premiums last Wednesday for notional 275 Bitcoins (source: LedgerX CEO Paul Chou). The usual Black-* models should avoided to price this option, for the stochastic process is extremely non-Gaussian (Levy).
Fed Funds forecast one-year out is 1.82% using forefunds('18h', '19h') so this implies two 25 bp rate hikes expected. The first presser by the new Fed chairman will be most interesting to see.
[Please see https://git.io/fedfunds for forecasting the Fed Funds rate using futures contracts on LIBOR.]
gemrat()
computes the mean geometric annualized rate at +3.97% (volatility of 20.17% with kurtosis at nearly 15), data starting at 2008-01-01. The street talks of possible "melt-up" -- how would a blow-off phase and its expected duration be characterized statistically?
foreinfl()
to forecast Unified Inflation. The best documentation for this function is https://git.io/infl which shows how it was derived by interacting with data and plots. This single function distills the forecasting process derived in the notebook. It's further discussed in new Appendix 2.
>>> foreinfl() # Inflation forecast
_1 UTC 2018-04-09 16:59:04
[2.1506, '2018-02-01', 1.5708, 2.7909, 2.0900]
Unified Inflation forecast in one function: Up to February 2018 data points, the geometric mean rate is 1.57%, the optimized Holt-Winter method expects 2.79% over the next year, whereas the 10-y BEI break-even rate using Treasuries is 2.09%. Thus, averaging them out: 2.15% is our near-term forecast. Notebook https://git.io/infl gives an in-depth analysis of inflation.
@econdb hi Oriol, yes, getting economic data outside the US is frustrating due to time lags and incompatible data formats. So your service has a great niche! https://www.econdb.com/source (The site does not mention costs, so please kindly clarify.) Nice to see the databases of many central banks included for monetary data -- though BOJ is missing, definitely a priority item.
We'll trial your Python API https://github.com/inquirim/inquisitor over at https://github.com/MathSci/fecon236 when we redo the notebooks on the Europe region. Thank you very much!
FOMC presser will be held Sept 26. The current Fed Funds range is 1.75 to 2.00%.
Our Fed Funds forecast one-year out is 2.42% using fe.forefunds('18z', '19z') so this implies two rate hikes of 25 bp each during that period [see https://git.io/fedfunds to forecast the Fed Funds rate using futures contracts on LIBOR]. Updated fecon236 function
They are currently in excess of EU limits, and thus the yield spread between the 10-year government bonds of Italy and Germany, BTP-Bunds, has been increasing. One way to monitor the situation is through the shorts on BTP futures: 5BTS chart, last quoted at 55.25.
5BTS is a fully collateralised, UCITS eligible Exchange-Traded Product. The ETP provides five times the inverse daily performance of the Long Term BTP Rolling Future Index, which tracks front-month Long-Term Euro-BTP futures, plus the interest revenue earned on the collateralised amount. Long-Term Euro-BTP futures are traded on EUREX and deliver Italian government bonds with 8.5-11 years to maturity. For example, if the index rises by 1% over a day, then the ETP will fall by 5%, and if the index falls by 1% over a day, then the ETP will rise by 5%, excluding fees and interest revenue.
Machine Learning in Investing, interview with Jeremiah Lowin: http://investorfieldguide.com/machinelearning
For related topics and details, follow sub-Quoras: https://optimal.quora.com for the financial aspects, and https://compute.quora.com for the ML computational aspects.