Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 1682-1700 |
| Journal | Journal of Applied Statistics |
| Volume | 40 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2013 |
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A dynamic analysis of stock markets using a hidden Markov model. / de Angelis, L.; Paas, L.J.
In: Journal of Applied Statistics, Vol. 40, No. 8, 2013, p. 1682-1700.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - A dynamic analysis of stock markets using a hidden Markov model
AU - de Angelis, L.
AU - Paas, L.J.
PY - 2013
Y1 - 2013
N2 - This paper proposes a framework to detect financial crises, pinpoint the end of a crisis in stock markets and support investment decision-making processes. This proposal is based on a hidden Markov model (HMM) and allows for a specific focus on conditional mean returns. By analysing weekly changes in the US stock market indexes over a period of 20 years, this study obtains an accurate detection of stable and turmoil periods and a probabilistic measure of switching between different stock market conditions. The results contribute to the discussion of the capabilities of Markov-switching models of analysing stock market behaviour. In particular, we find evidence that HMM outperforms threshold GARCH model with Student-t innovations both in-sample and out-of-sample, giving financial operators some appealing investment strategies. © 2013 Copyright Taylor and Francis Group, LLC.
AB - This paper proposes a framework to detect financial crises, pinpoint the end of a crisis in stock markets and support investment decision-making processes. This proposal is based on a hidden Markov model (HMM) and allows for a specific focus on conditional mean returns. By analysing weekly changes in the US stock market indexes over a period of 20 years, this study obtains an accurate detection of stable and turmoil periods and a probabilistic measure of switching between different stock market conditions. The results contribute to the discussion of the capabilities of Markov-switching models of analysing stock market behaviour. In particular, we find evidence that HMM outperforms threshold GARCH model with Student-t innovations both in-sample and out-of-sample, giving financial operators some appealing investment strategies. © 2013 Copyright Taylor and Francis Group, LLC.
U2 - 10.1080/02664763.2013.793302
DO - 10.1080/02664763.2013.793302
M3 - Article
VL - 40
SP - 1682
EP - 1700
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
SN - 0266-4763
IS - 8
ER -