These Lecture series are relating the use R language software, its interface and functions required to evaluate financial risk models. Furthermore, R software applications relating financial market data, measuring risk, modern portfolio theory, risk modeling relating returns generalized hyperbolic and lambda distributions, Value at Risk (VaR) modelling, extreme value methods and models, the class of ARCH models, GARCH risk models and portfolio optimization approaches.

1. FINANCIAL RISK MGT – FRM
Lecture by;
Dr. Syed
Muhammad Ali
Tirmizi
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TOPICS OF CHAPTER NO. 13
 In this lecture, we will cover the following topics:
13. Tactical Asset Allocation
13.1. Overview
13.2. Survey of Selected Time Series Models
13.2.1. Univariate Time Series Models
13.2.2. Multivariate Time Series Models
13.3. The Black-Litterman Approach
13.4. Copula Opinion and Entropy Pooling
13.4.1. Introduction
13.4.2. The COP model
13.4.3. The EP model

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TOPICS OF CHAPTER NO. 13
11.5. Synopsis for R Packages
11.5.1. The Package BLCOP
11.5.1. The Package dse
11.5.1. The Package fArma
11.5.1. The Package forecast
11.5.1. The Package MSBVAR
11.5.1. The Package PortfolioAnalytics
11.5.1. The Package urca and vars
11.6. Empirical Applications
11.6.1. Black-Litterman Portfolio Optimization
11.6.2. Copula Opinion Pooling
11.6.3. Entropy Pooling
11.6.4. Protection Strategies

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CHAPTER OVERVIEW
 The focus will be on the description of selected time
series methods for deriving forecasts of asset prices.
 There are quite a few different ways of deriving
TAA allocations, the Black–Litterman model
probably being the most widely known and almost
synonymous with TAA-driven portfolio allocations.
 However, TAA can also be combined with risk-
overlay and/or high-watermark strategies.

5. 5
CHAPTER OVERVIEW
 Given the quite huge variety of applications and
combinations of TAA, it is quite surprising that
the literature directly related to TAA is rather
sparse.
 Also, the number of articles explicitly covering
TAA is rather small.
 Although this chapter aim is to provide as
thorough an account of the topic.

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SURVEY OF SELECTED TIMESERIES MODELS
 Univariate time series models
 AR(p) time series process

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SURVEY OF SELECTED TIMESERIES MODELS

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SURVEY OF SELECTED TIMESERIES MODELS

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SURVEY OF SELECTED TIMESERIES MODELS

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MULTIVARIATE TIME SERIES MODELS
 This interdependence occurs across countries
and/or across assets.
 Structural multiple equation models allow the
modelling of explicit interdependencies as
observed in financial markets.
 In addition, exogenous variables (e.g.,
macroeconomic data) can be included in this
model type.

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MULTIVARIATE TIME SERIES MODELS
 Structural multiple equation models (SMEMs) can
be utilized for forecasting, scenario analysis, and
risk assessment as well as for multiplier analysis,
and they are therefore ideally suited for tactical
asset allocation.
 The origins of the SMEMcan be traced back to the
1940s and 1950s, when this model type was
proposed by the Cowles Foundation.

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MULTIVARIATE TIME SERIES MODELS

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MULTIVARIATE TIME SERIES MODELS
 Vector autoregressive models: VAR models explain
the endogenous variables solely in terms of their own
history, apart from deterministic regressors.
 In contrast, structural vector autoregressive (SVAR)
models allow the explicit modelling of
contemporaneous interdependence between the left-
hand-sidevariables. Hence, these types of models try
to bypass the shortcomings of VAR models.

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MULTIVARIATE TIME SERIES MODELS

15. END OF LECTURE NO. 14
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