{'fraction_non_alphanumeric': 0.019205298013245033, 'fraction_numerical': 0.0, 'mean_word_length': 5.068273092369478, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Out-of-Sample Performance of Jump-Diffusion Models for Equity Indices: What the Financial Crisis Was Good For

Out-of-sample performance of continuous time models for equity returns is crucial in practical applications such as computing risk measures like value at risk, determine optimal portfolios or pricing derivatives. For all these applications investors need to model the return distribution of an underlying at some point in time in the future given current information. In this paper we analyze the out-of-sample performance of exponentially affine and non-affine continuous time stochastic volatility models with jumps in returns and volatility. Our analysis evaluates the density forecasts implied by the models. In a first step, we find in general that the good in-sample fits reported in the related literature do not carry over to the out-of-sample performance. In particular the left tail of the distribution poses a considerable challenge to the proposed models. In a second step, we analyze the models by using a rolling window approach. We find that using estimation periods that include high market stress events improve forecasting power considerably. In a third step, we apply parameters estimated on the sub period including the financial crisis (period with highest market stress) to all other forecasting sub periods. This approach further increases overall forecasting power and results in an outperformance of affine compared to non-affine models and an outperformance of jump models.