Abstract
The question of interest in this paper is the estimation of the trend of a financial asset, and the impact of its misspecification on investment strategies. The setting we consider is that of a stochastic asset price model where the trend follows an unobservable Ornstein–Uhlenbeck process. Motivated by the use of Kalman filtering as a forecasting tool, we address the problem of parameter estimation, and measure the effect of parameter misspecification. Numerical examples illustrate the difficulty of trend forecasting in financial time series.
| Original language | English |
|---|---|
| Pages (from-to) | 369-382 |
| Number of pages | 14 |
| Journal | Quantitative Finance |
| Volume | 17 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2017 |
Keywords
- Investment strategies
- Kalman filter
- Ornstein–Uhlenbeck process
- Trend estimation
Cite this
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Forecasting trends with asset prices. / Bel Hadj Ayed, Ahmed; Loeper, Gregoire; Abergel, Frédéric.
In: Quantitative Finance, Vol. 17, No. 3, 2017, p. 369-382.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Forecasting trends with asset prices
AU - Bel Hadj Ayed, Ahmed
AU - Loeper, Gregoire
AU - Abergel, Frédéric
PY - 2017
Y1 - 2017
N2 - The question of interest in this paper is the estimation of the trend of a financial asset, and the impact of its misspecification on investment strategies. The setting we consider is that of a stochastic asset price model where the trend follows an unobservable Ornstein–Uhlenbeck process. Motivated by the use of Kalman filtering as a forecasting tool, we address the problem of parameter estimation, and measure the effect of parameter misspecification. Numerical examples illustrate the difficulty of trend forecasting in financial time series.
AB - The question of interest in this paper is the estimation of the trend of a financial asset, and the impact of its misspecification on investment strategies. The setting we consider is that of a stochastic asset price model where the trend follows an unobservable Ornstein–Uhlenbeck process. Motivated by the use of Kalman filtering as a forecasting tool, we address the problem of parameter estimation, and measure the effect of parameter misspecification. Numerical examples illustrate the difficulty of trend forecasting in financial time series.
KW - Investment strategies
KW - Kalman filter
KW - Ornstein–Uhlenbeck process
KW - Trend estimation
UR - http://www.scopus.com/inward/record.url?scp=84978976079&partnerID=8YFLogxK
UR - http://www.tandfonline.com.ezproxy.lib.monash.edu.au/doi/pdf/10.1080/14697688.2016.1206959?needAccess=true
U2 - 10.1080/14697688.2016.1206959
DO - 10.1080/14697688.2016.1206959
M3 - Article
VL - 17
SP - 369
EP - 382
JO - Quantitative Finance
JF - Quantitative Finance
SN - 1469-7688
IS - 3
ER -