Abstract
We consider the portfolio optimization problem for the criterion of maximization of expected terminal log-utility. The underlying market model is a regime-switching diffusion model where the regime is determined by an unobservable factor process forming a finite state Markov process. The main novelty is due to the fact that prices are observed and the portfolio is rebalanced only at random times corresponding to a Cox process where the intensity is driven by the unobserved Markovian factor process as well. This leads to a more realistic modeling for many practical situations, like in markets with liquidity restrictions; on the other hand it considerably complicates the problem to the point that traditional methodologies cannot be directly applied. The approach presented here is specific to the log-utility. For power utilities a different approach is presented in the companion paper (Fujimoto et al. in Appl Math Optim 67(1):33–72, 2013).
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Notes
We are grateful for an anonymous suggestion of this useful norm.
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Appendix
Appendix
Proof of Lemma 4.1
Proof of statement (i). It follows from the two lemmas shown below.
Lemma 5.1
We have the following representation,
Proof
It suffices to prove that for any \(\mathcal{G }_t-\)adapted process \(Z_t\)
First notice that any \(\mathcal{G }_t-\)adapted process \(Z_t\) has the representation (see Bremaud 1981)
with the process \(Z_k(t)\) being \(\mathcal{G }_k \otimes \mathcal{B }(\mathbb{R }_+)\)-measurable. Furthermore, under our assumptions, for all \(t>0, \lim _{n\rightarrow \infty }1_{ \{ \tau _n < t\} } =0\) and thus
Note, finally, that \(E\left[ 1_{\{\tau _k< t\le \tau _{k+1} \}} |\mathcal{G }_k \right] =1_{]\tau _k, \infty )} (t) E[1_{\{ t\le \tau _{k+1} \}}|\mathcal{G }_k ]] \). We then have
and thus we obtain (5.2) since
which follows from (2.16). \(\square \)
Lemma 5.2
We have the following equation
with \(\hat{C}(t,\pi ,h)\) defined by (4.6) in Definition 4.1.
Proof
For simplicity, in the following formula we shall use the notation
Using (5.1) we have similarly as above
Since \(\left( \theta _t, \tilde{X}_t\right) \) is a time homogeneous Markov process,
We now have, recalling the definition of \(r_{ji}(t,z)\) in (3.8),
We finally have
\(\square \)
Proof of statement (ii) of Lemma 4.1.
We start by proving that \(\hat{C}(t,\pi , h)\) is Lipschitz continuous with respect to \(t\).
Thus
where \(\Vert f \Vert := \sup _{e\in E, h\in \bar{H}_m}\Vert f(e, h) \Vert \). Next, let us prove that \(C(t,\pi , h)\) is Lipschitz continuous with respect to \(\pi \) [in the metric introduced in (3.21)].
where we have used (3.20).
Next, let us prove that \(C(t,\pi , h)\) is continuous with respect to \(h\) [always in the metric introduced in (3.21)]. The function \(f(e_i,h)\) is bounded and continuous with respect to \(h\) for all \(i\). Furthermore, \(\gamma (x,h)\) is continuous with respect to \(h\) for all \(x\in \mathbb{R }^m\). Applying the dominated convergence theorem, for \(\ {h_n}\subset \bar{H}_m\), s.t.\(\displaystyle \lim _{n\rightarrow \infty } h_n =h\in \bar{H}_m\)
\(\hat{C}(t,\pi , h)\) is thus continuous with respect to each of the variables \(t,\pi ,h\). However, continuity in \(t,\pi \) is independent of the other variable. Hence, \(\hat{C}(t,\pi , h)\) is a continuous function on \([0,T]\times \mathcal{S }_N \times \bar{H}_m\).
Proof of Lemma 4.2
Fix \(n\ge 0\). Recall the definition of \( h_{n}^i\) given in Sect. 2.3. Since \(S_t\) is continuous and \(V_t\) satisfies the self-financing condition, we obtain
Using (2.16), (2.18), for \( all \ k\ge 1,h \in \mathcal{A }^{n},t\in [\tau _{n+k},T]\), one furthermore has
Therefore, using lemma 4.1(i) for \(\ h\in \mathcal{A }^n\)
Proof of Lemma 4.6
By the definition of \(\mathcal{A }^n\), for \(n\ge 0, \mathcal{A }^n\subset \mathcal{A }^{n+1} \subset \mathcal{A }\), hence,
By the definition of \(W^{n}(t,\pi ) \) and \(W(t,\pi ) \)
Using Lemma 4.5, for \(\ n,m\ge 0\)
Letting \(m\rightarrow \infty \)
Proof of Lemma 4.7
For\( \ h \in \mathcal{A }, W(t,\pi ,h)\) defined by (4.8) satisfies
because of the representation of \(W^n(t,\pi )\) in Corollary 4.2 (Eq. 4.13) and Lemma 4.5. Thus, by letting \(n \rightarrow \infty \), we obtain
for all \( \ h \in \mathcal{A }\).
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Fujimoto, K., Nagai, H. & Runggaldier, W.J. Expected Log-Utility Maximization Under Incomplete Information and with Cox-Process Observations. Asia-Pac Financ Markets 21, 35–66 (2014). https://doi.org/10.1007/s10690-013-9176-1
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DOI: https://doi.org/10.1007/s10690-013-9176-1