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This paper investigates the approaches of building WaveNet vocoders with limited training data for voice conversion (VC). Current VC systems using statistical acoustic models always suffer from the quality degradation of converted speech. One of the major causes is the use of hand-crafted vocoders for waveform generation. Recently, with the emergence of WaveNet for waveform modeling, speaker-dependent WaveNet vocoders have been proposed and they can reconstruct speech with better quality than conventional vocoders, such as STRAIGHT. Because training a WaveNet vocoder in the speaker-dependent way requires a relatively large training dataset, it remains a challenge to build a high-quality WaveNet vocoder for VC tasks when the training data of target speakers is limited. In this paper, we propose to build WaveNet vocoders by combining the initialization using a multi-speaker corpus and the adaptation using a small amount of target data and evaluate this proposed method on the Voice Conversion Challenge (VCC) 2018 dataset which contains approximately 5 minute recordings for each target speaker. Experimental results show that the WaveNet vocoders built using our proposed method outperform conventional STRAIGHT vocoder. Furthermore, our system achieves an average naturalness MOS of 4.13 in VCC 2018, which is the highest among all submitted systems.