Journal of Risk and Financial Management

370 KiB

Open AccessArticle

An Empirical Analysis for the Prediction of a Financial Crisis in Turkey through the Use of Forecast Error Measures

by Seyma Caliskan Cavdar and Alev Dilek Aydin

J. Risk Financial Manag. 2015, 8(3), 337-354; https://doi.org/10.3390/jrfm8030337 - 24 Aug 2015

Cited by 4 | Viewed by 6720

In this study, we try to examine whether the forecast errors obtained by the ANN models affect the breakout of financial crises. Additionally, we try to investigate how much the asymmetric information and forecast errors are reflected on the output values. In our [...] Read more.

In this study, we try to examine whether the forecast errors obtained by the ANN models affect the breakout of financial crises. Additionally, we try to investigate how much the asymmetric information and forecast errors are reflected on the output values. In our study, we used the exchange rate of USD/TRY (USD), the Borsa Istanbul 100 Index (BIST), and gold price (GP) as our output variables of our Artificial Neural Network (ANN) models. We observe that the predicted ANN model has a strong explanation capability for the 2001 and 2008 crises. Our calculations of some symmetry measures such as mean absolute percentage error (MAPE), symmetric mean absolute percentage error (sMAPE), and Shannon entropy (SE), clearly demonstrate the degree of asymmetric information and the deterioration of the financial system prior to, during, and after the financial crisis. We found that the asymmetric information prior to crisis is larger as compared to other periods. This situation can be interpreted as early warning signals before the potential crises. This evidence seems to favor an asymmetric information view of financial crises. Full article

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1066 KiB

Open AccessArticle

Volatility Forecast in Crises and Expansions

by Sergii Pypko

J. Risk Financial Manag. 2015, 8(3), 311-336; https://doi.org/10.3390/jrfm8030311 - 05 Aug 2015

Cited by 1 | Viewed by 5582

Abstract

We build a discrete-time non-linear model for volatility forecasting purposes. This model belongs to the class of threshold-autoregressive models, where changes in regimes are governed by past returns. The ability to capture changes in volatility regimes and using more accurate volatility measures allow [...] Read more.

We build a discrete-time non-linear model for volatility forecasting purposes. This model belongs to the class of threshold-autoregressive models, where changes in regimes are governed by past returns. The ability to capture changes in volatility regimes and using more accurate volatility measures allow outperforming other benchmark models, such as linear heterogeneous autoregressive model and GARCH specifications. Finally, we show how to derive closed-form expression for multiple-step-ahead forecasting by exploiting information about the conditional distribution of returns. Full article

(This article belongs to the Special Issue Financial Risk Modeling and Forecasting)

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1546 KiB

Open AccessArticle

Inflation and Speculation in a Dynamic Macroeconomic Model

by Matheus R. Grasselli and Adrien Nguyen Huu

J. Risk Financial Manag. 2015, 8(3), 285-310; https://doi.org/10.3390/jrfm8030285 - 06 Jul 2015

Cited by 11 | Viewed by 6151

Abstract

We study a monetary version of the Keen model by merging two alternative extensions, namely the addition of a dynamic price level and the introduction of speculation. We recall and study old and new equilibria, together with their local stability analysis. This includes [...] Read more.

We study a monetary version of the Keen model by merging two alternative extensions, namely the addition of a dynamic price level and the introduction of speculation. We recall and study old and new equilibria, together with their local stability analysis. This includes a state of recession associated with a deflationary regime and characterized by falling employment but constant wage shares, with or without an accompanying debt crisis. Full article

(This article belongs to the Special Issue Selected Papers from the Fifth International Conference on Mathematics in Finance (MiF) 2014, Organized by North-West University, University of Cape Town and University of Johannesburg, South Africa)

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J. Risk Financial Manag., Volume 8, Issue 3 (September 2015) – 3 articles , Pages 285-354

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